1 determinants of capital structure of firms in the manufacturing

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Determinants of Capital Structure of Firms in the Manufacturing Sector of Firms

in Indonesia

Dissertation

To obtain the degree of

Doctor of Business Administration

at the Maastricht School of Management,

under authority of the Dean Director Prof. dr. Peter P. de Gijsel

to be defended in public on May, 2012

by

Siti Rahmi Utami

born in Jakarta (Indonesia)

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Published by:

Maastricht School of Management

P.O. Box 1203

6201 BE Maastricht

The Netherlands

Siti Rahmi Utami, Determinants of Capital Structure of Firms in the Manufacturing Sector of

Firms in Indonesia. DBA Dissertation, Maastricht School of Management, Maastricht 2012. –

With references. – With summary in English.

Key words: Capital Structure/Pecking Order Theory/Trade-off Theory/Firm Life Cycle/Signalling

Theory/Asymmetric Information/Agency Cost Theory

ISBN:

Cover: Stoerebinken, The Netherlands

Printing: Gildeprint, The Netherlands

© 2012 by Siti Rahmi Utami, Maastricht School of Management. All rights reserved. No part of

this publication may be reproduced, stored in a retrieval system or transmitted in any form or by

any means, electronic, mechanical, photocopying, recording or otherwise, without prior written

permission of the publisher.

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This dissertation is approved of by the Doctoral Supervisor:

Prof. Eno L. Inanga

Maastricht School of Management, The Netherlands

Composition of the Evaluation Committee:

Prof. Dr. Ir. E. J. de Bruijn

Twente University, The Netherlands

Prof. Dr. Geert Braam RA

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ACKNOWLEDGEMENTS

It is with a lot of gratitude and appreciation that I acknowledge the help of my supervisor,

Professor Eno L. Inanga, who has helped me to complete this Draft DBA thesis. The Draft DBA

thesis would not have reached this stage in the present form without his help. He has given me

support throughout the entire process. I am hugely indebted to him for all the hours he spent

reading my texts, writing suggestions and comments for me, and helping me to shape my thinking

in many ways. I greatly appreciate his expertise in the field of my research.

Likewise, I would also like to express my gratefulness to Professor Dadan, from Trisakti

University, Indonesia, for his encouragement and guidance. I also owe many thanks to the

administrative support I enjoyed from the Doctoral Office at MSM, as well as the administration

office at TIBS, Indonesia, are worthy of a mention with special thanks.

I must express my profound thanks to my parents (especially my father, Professor Gani

SH), without their support, I would not have achieved this stage. Last, but not least, I would also

like to thank to my friends, I have learned many things from them.

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TABLE OF CONTENTS

ACKNOWLEDGEMENTS ................................................................................................................ 4

EXECUTIVE SUMMARY ................................................................................................................. 9

1. INTRODUCTION ....................................................................................................................... 11

1.1 Background of the Research ............................................................................................... 11

1.1.1. The Importance of Capital Structure Theory........................................................................ 11

1.1.2. Research Motivation ............................................................................................................. 15

1.2 Problem Identification .............................................................................................................. 17

1.3 Research Questions .................................................................................................................. 19

1.3.1 Major Research Questions .................................................................................................... 19

1.3.2 Minor Research Questions .................................................................................................... 19

1.4 Research Objectives ................................................................................................................. 20

1.5 Scope and Limitation of the Study ............................................................................................ 20

1.6 Expected Contribution .............................................................................................................. 22

1.7 Organisation of the Study ......................................................................................................... 23

2. AN OVERVIEW OF THE CAPITAL STRUCTURE OF INDONESIAN MANUFACTURING

FIRMS ............................................................................................................................................ 24

2.1. Indonesian Capital Market ...................................................................................................... 24

2.1.1 History of Indonesia Stock Exchange .................................................................................... 24

2.1.2 Stock Price Index in the Indonesian Capital Market ............................................................. 24

2.1.3. Description of the LQ45 Index ............................................................................................. 26

2.2. Characteristics of the Research Sample .................................................................................. 26

2.3 Leverage Analysis ..................................................................................................................... 34

3. LITERATURE REVIEW .............................................................................................................. 36

3.1 Theories of Capital Structure ................................................................................................... 36

3.1.1 Modigliani-Miller Theory ...................................................................................................... 36

3.1.2. The Capital Structure Theory ............................................................................................... 37

3.2. The Conclusions What Variables We Use for Our Research, and Why These, Theories

Predictions of the Relationship between Variables, and Some Previous Research Findings ......... 40

3.2.1 Selected Variables regarding Capital Structure for Research Question 1a, 1b, 1c, 1d, and 1e 40

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3.2.2 Selected Variables for Research Question 2 .......................................................................... 46

3.2.3 Selected Variables for Research Question 3a, 3b, and 3c ..................................................... 48

3.2.4 Selected Variables for Research Question 4 .......................................................................... 51

4. CONCEPTUAL FRAMEWORK ................................................................................................. 54

4.1 Conceptual Framework for Research Question 1a, 1b, 1c, 1d, and 1e .................................... 54

4.1.1 Previous Research regarding Capital Structure Determinants ............................................. 54

4.1.2 Conceptual Framework for Research Question 2 ................................................................. 65



5. RESEARCH METHODOLOGY.................................................................................................. 76

5.1 Research Design ....................................................................................................................... 76

5.2Research Strategy ...................................................................................................................... 77

5.2.1. Quantitative Strategy ............................................................................................................ 77

5.2.2. Mixed Method Strategy ......................................................................................................... 78

5.3 Data Collection ........................................................................................................................ 78

5.4. Sampling Design and Procedure ............................................................................................. 79

5.5. Variables Measurement ........................................................................................................... 80

5.5.1. Variable of Hypothesis 1 ...................................................................................................... 80

5.5.2 Measuring Variables of Hypotheses 2, 3, and 4 .................................................................... 82

5.6. Hypotheses Testing ................................................................................................................... 83

5.6.1. Hypothesis 1 .......................................................................................................................... 84

5.6.2. Hypothesis 2 ......................................................................................................................... 84

5.6.3. Hypothesis 3 ......................................................................................................................... 86

5.6.4. Hypothesis 4 ......................................................................................................................... 86

5.7. Regression Analysis ................................................................................................................. 91

A. The Un-standardised Beta Coefficients ..................................................................................... 91

B. The Standardised Beta Coefficients ........................................................................................... 91

C. Analysis of Variance (ANOVA).................................................................................................. 91

D. The Coefficient of Determination (R2) ....................................................................................... 91

E. Descriptive Statistics .................................................................................................................. 92

F. Regression Assumptions of Hypothesis 1-4 ................................................................................ 92

5.8. The Credibility of Research Findings ...................................................................................... 94

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5.8.1 Reliability .............................................................................................................................. 94

5.8.2 Validity .................................................................................................................................. 94

5.8.3 Generalisability ..................................................................................................................... 94

5.9. The Limitations of Research Design ........................................................................................ 94

6. PRESENTATION OF DATA AND ANALYSIS OF RESULTS .................................................... 96

6.1 Research Question 1, Hypotheses, Hypotheses Testing, and Result Analysis .......................... 96

6.1.1. Research Question 1 ............................................................................................................. 96

6.1.2. Hypothesis One (H1) ............................................................................................................ 96

6.1.3. Testing the Hypothesis 1 ....................................................................................................... 97

6.1.4. Analysis of Results ................................................................................................................ 97

6.2. Research Question 2, Hypothesis 2, Hypothesis Testing, and Result Analysis ...................... 111

6.2.1. Research Question 2 ........................................................................................................... 111

6.2.2. Hypothesis 2 ....................................................................................................................... 111

6.2.3. Testing the Hypothesis 2 ..................................................................................................... 111

6.2.4. Analysis of Quantitative Results of Hypothesis 2 ............................................................... 112

6.2.5 Qualitative Analysis of Hypothesis 2 ................................................................................... 117

6.3. Research Question 3, Hypothesis, Hypothesis Testing, and Result Analysis ........................ 129

6.3.1. Research Question Three .................................................................................................... 129

6.3.2. Hypothesis 3 ....................................................................................................................... 129

6.3.3. Testing the Hypothesis 3 ..................................................................................................... 130

6.3.4. Analysis of Results .............................................................................................................. 130

6.4. Research Question 4, Hypothesis, Hypothesis Testing, and Result Analysis ......................... 137

6.4.1. Research Question 4 ........................................................................................................... 137

6.4.2. Hypothesis 4 ....................................................................................................................... 137

6.4.3. Testing Hypothesis 4 ........................................................................................................... 137

6.4.4 Sample Description ............................................................................................................. 138

6.4.5. Analysis of Results .............................................................................................................. 139

6.4.6. Capital Structure over Firm’s Life Cycle ........................................................................... 148

6.4.7 Frequency ............................................................................................................................ 151

6.5. Statistical Power Analysis of Hypotheses 1, 2, 3, and 4 ........................................................ 157

6.6. Regression Assumptions of Hypotheses 1, 2, 3, and 4 ........................................................... 163

1. Multicollinearity ....................................................................................................................... 163

2. Autocorrelation ........................................................................................................................ 165

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3. Heteroscedasticity .................................................................................................................... 166

4. Normally Distributed ................................................................................................................ 166

6.7. Results of Panel Data Regression Analysis and the Comparison to Regression Analysis .... 166

7. CONCLUSION ......................................................................................................................... 170

7.1. Conclusion ............................................................................................................................. 170

7.2 Conclusion regarding Result and Its Consistency with Condition of Indonesian Capital Market

171

7.3. To What Extent is the Study Scientifically Relevance ............................................................ 174

7.4. Recommendations and Suggestions for Further Research .................................................... 175

7.5. Suggestions for Managers ..................................................................................................... 176

7.6. Managerial Implication ......................................................................................................... 176

BIBLIOGRAPHY .......................................................................................................................... 177

APPENDIX ................................................................................................................................... 186

APPENDIX A ............................................................................................................................... 186

APPENDIX B ............................................................................................................................... 206

APPENDIX C ............................................................................................................................... 213

APPENDIX D ............................................................................................................................... 220

APPENDIX E ............................................................................................................................... 241

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EXECUTIVE SUMMARY

The objectives of this research are: to investigate the determinants of capital structure of

the firms in the manufacturing sector in Indonesian capital market; to analyse how firms in the

manufacturing sector raise capital for investments, internally or externally (with debt, equity, or

debt to repurchase equity); to examine if debt policy does matter; what will happen to the firm‟s

stock price if firms issue new debt, issue new equity, or issue debt to repurchase equity; and to

examine within the context of a firm‟s life cycle whether we can expect that growth-small firms

follow the pecking order more closely than mature-large firms. Therefore, we examine 4 major

hypotheses. By using regression analysis we test all hypotheses, while for hypothesis 2 we use

qualitative analysis, too, and for hypotheses 2 and 4 we also apply an augmented model.

Overall, our results showed that under the linear regression model, firms exhibit as

follows. For hypothesis 1, profitability has a negative significant regression coefficient on short-

term leverage; long-term leverage; total leverage, and on market leverage. Tangibility has a

negative significant regression coefficient on short-term leverage, while tangibility has a positive

significant regression coefficient on long-term and market leverage. Tangibility also has a positive

but not significant regression coefficient on total leverage.

Size, has a positive, yet not significant regression coefficient on short-term leverage and

total leverage, while size has a negative, yet not significant regression coefficient on long-term

leverage, and size has a negative significant regression coefficient on market leverage. Risk has a

positive significant regression coefficienton short-term leverage and total leverage while risk has a

negative significant regression coefficient on long-term leverage. Risk also has a positive but not

significant regression coefficient on market leverage. Growth has a positive significant regression

coefficient on short-term, long-term, and total leverage; however, growth has a negative

significant regression coefficient on market leverage.

For hypothesis 2, we can conclude that the financing deficit has positive significant

effects on the net debt issue and on net equity issue. This result suggests that high deficit firms

would tend to issue more net debt and net equity to finance their financing deficit. The financing

deficit has negative, yet not significant effects on newly retained earning. This result suggests that

high deficit firms would not tend to use newly retained earning to finance the financing deficit.

The financing deficit has negative, but not significant effects on repurchase equity. This result

suggests that high deficit firms would not tend to repurchase equity to finance the financing

deficit. From the descriptive table, we see that the amount of net debt issue is more than net equity

issue and it is consistent with regression results.

For the augmented model, our result shows a positive coefficient on the financial deficit

and also on the squared deficit term. However, for the squared deficit term, the coefficient was not

significant. Therefore, we conclude that our firm sample firm prefers external to internal financing

and debt to equity if external financing is used.

For hypothesis 3, the results indicate that net debt has no positive significant impact on

the stock price of from January to December and on the yearly stock price. Net equity has no

negative significant impact on the stock price from January to December and on the yearly stock

price. This result suggests that firms that issue more net equity would tend to have decreasing

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stock price, while issuing more net debt, the firm would tend to have increasing stock price. The

result also suggests that firms repurchasing equity would tend to have increasing monthly and

yearly stock price.

For hypothesis 4, the growth firms, we conclude that the financing deficit has positive

significant effects on the net debt issue and on the net equity issue, and financing deficit has

negative significant effects on newly retained earning. For mature firms, we conclude that the

financing deficit has positive significant effects on the net debt issue and the net equity issue,

while a financing deficit has negative insignificant effects on newly retained earning. From these

results, we conclude that our mature and growth firm prefers external to internal financing and

debt to equity if external financing is used. Overall, we find that the pecking order theory describes

the financing patterns of growth firms better than mature firms.

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1. INTRODUCTION

1.1 Background of the Research

1.1.1. The Importance of Capital Structure Theory

At the time a firm faces a financial deficit that affects its financial condition, the manager

of the firm should be able to make a managerial decision as well as a financial decision in order to

maintain the viability of the firm. One way that can be chosen is to undertake a capital

restructuring, especially debt restructuring. The decision taken on debt restructuring, of course,

requires expertise and analystic capabilities so managers can make the right decisions of financial

restructuring for the company. An ideal composition of capital structure which consists of debt and

equity, will minimise the cost of capital and maximise the firm‟s value. Therefore, it is important

for the firm‟s manager to understand the theory of capital structure.

The sources of funds include retained earnings, debt, and equity. Retained earning is the

cheapest fund for the funding source as it does not have explicit costs in the same way as funds

obtained from outside sources. When the company uses debt to finance investments which has an

impact on costs rising in its capital structure, the company will have a financial risk, because the

company must consider their priority in the structure of debt, debt maturity, decision of mixed debt

to certain parties or to the investor, and other types of debt contracts (Peirson, Brown, Easton and

Howard, 2002; Barclay et al., 2003).

If a firm uses stocks as its capital structure, either common stocks or preferred stocks,

then the shareholders of those stocks are the owner of the company. While debt has due date, the

stocks do not have one. Thus the repayment of stocks is not necessarily required since stocks are

liquidated if the company went bankrupt. Issuing the stocks may reduce the authority of the old

owners in the company. To maintain the dominance of the existing owner of the company, the

issuance of stocks is managed not to cross the line of power. The cost of the issuance of stocks is

dividend which will be distributed to shareholders. Furthermore, debt can be treated as tax-

deductible expenses, but common stock dividend payments and preferred stocks are not tax-

deductible.

Firm‟s capital structure decision can be viewed from the following theories: Modigliani-

Miller theory, pecking order theory, and trade-off theory. The theory of business finance in a

modern sense starts with the Modigliani and Miller (1958) capital structure irrelevance

proposition. Before them, there was no generally established theory of capital structure. The

debate about how and why firms choose their capital structure began in 1958 (Myers, 2001), when

Modigliani and Miller (1958) published their famous arbitrage argument showing that „the market

value of any firm is independent of its capital structure‟. Modigliani and Miller start their theory

by assuming that the firm has a particular set of expected cash flows. When the firm chooses a

certain proportion of debt and equity to finance its assets, what it has to do is to divide up the cash

flows among investors. Investors and firms are assumed to have equal access to financial markets,

which allows for homemade leverage. As a result, the leverage of the firm has no effect on the

market value of the firm. Modigliani and Miller‟s theory influenced the early development of other

capital structure theory.

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The introduction of taxation effects implies that firms should, theoretically, try to

increase their debt levels as much as possible (Miller, 1988). However, other theorists (for

example Stiglitz, 1974; 1988) added limitations to the optimal level of firm debt by arguing that

bankruptcy costs enhance as the firm‟s level of debt increases, and this places a higher limit on

the amount of debt that should be present in a firm‟s capital structure. This evolved into the static

trade-off theory, which proposes that firms attempt to achieve an optimal capital structure that

maximises the value of the firm by balancing the tax benefits, with the bankruptcy costs,

associated with increasing levels of debt (Myers, 1984).

Some researchers have identified problem areas in the capability of the static trade-off

theory to explain actual firm behaviour. For example Myers (2001) argued that the static trade-off

theory implies that highly profitable firms should have high debt ratios in order to shield their

large profits from taxation, whereas in reality, highly profitable firms tend to have less debt than

less profitable firms. Warner (1977) suggested that bankruptcy costs are much lower than the tax

advantages of debt, implying much higher debt levels than predicted by the theory.

There is, however, also some empirical evidence and theoretical support for the idea that

firms, at least in part, raise their capital structure to take advantage of the interest tax shield (net of

the interest tax burden to investors), while ensuring that they avoid acquiring excessively high

financial distress costs. For example, Kayhan and Titman (2004) found that, over the long term,

firms do tend to move towards target debt ratios consistent with the theory. Static trade-off theory

therefore offers one possible explanation of how firms choose their capital structure.

Myers observed how firms actually structure their balance sheets, and found that firms

tend to follow a „pecking order‟ in financing their projects: first they use internal equity, then debt,

and only then do they use external equity (Myers, 1984). In contrast to Ross (1977), who argued

that firms use more debt to overcome information asymmetries and signal better prospects, Myers

(2001) used information asymmetries to argue that managers are unlikely to issue equity, because

they fear it will signal that the stock price is overvalued. In addition to the evidence presented by

Myers, several other studies have given support to the pecking order theory. For instance , Allen

(1993), like Fama and French (1988), found that leverage is inversely related to profitability,

which supports the pecking order theory view that debt is only issued when there is insufficient

retained income to finance investment.

Therefore, capital structure decision is influenced by a pecking order preference, which

has advantages and disadvantages based on the pecking order theory, and trading off cost and

benefit of using debt based on trade-off theory, in order to maximise return and minimise cost of

capital. Besides capital structure, the decision is influenced by the pecking order preference and

the trading off cost and benefit of using debt, capital structure decision is influenced by the firm‟s

life cycle where the firm exist and may consider the firm‟s characteristics.

Capital structure life stage theory is conspicuously underdeveloped. Although mentioned

in text-books (Damodaran, 2001) and obliquely in some research (for example Morgan and Abetti,

2004), and even referred to in the development of some of the other major theories (for example

Myers, 2001), the idea that the capital structure of a firm may be related to its life stage, appears to

have received very little direct theoretical or empirical examination. Some of the organisational

life stage theory research has suggested that changing life stages may require changes in the way

the firm is financed. Thus the firm‟s financing characteristics change from one life stage to the

next stage.

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The pecking order theory describes the financing patterns of growth firms better than of

mature firms as mature firms are more closely followed by analysts and are better known to

investors, and hence should suffer less from problems of information asymmetry. Our result is

consistent with the theory, and also consistent with the previous research findings of Shyam-

Sunder and Myers (1999). They propose a direct test of the pecking order and find strong support

for the theory among a sample of large firms.

Older and more mature firms are more closely followed by analysts and are better known

to investors, and hence, should suffer less from problems of information asymmetry. For example,

a good reputation (such as a long credit history) mitigates the adverse selection problem between

borrowers and lenders. Thus, mature firms are able to obtain better loan rates compared to their

younger firm counterparts (Diamond, 1989).

The theory‟s prediction that firms with the greatest information asymmetry problems

(specifically young growth firms) are exactly those which should be raising financing choices

according to the pecking order theory. In general, the significant difference between mature and

young firms is not that mature firms are larger, but because they are more mature which implies

that mature firms are older, more stable, higher profitable with few growth opportunities and good

credit histories. Growth firms are thus more suited to use internal funds first, and then debt before

equity for their financing needs.

As mentioned above, capital structure decision is also affected by a firm‟s characteristics.

These characteristics are potentially contentious (Titman and Wessels 1988). Each theory of

capital structure gives the different implication on how the firm‟s characteristics influence the

firm‟s capital structure choices. In order to identify which of the firm‟s characteristics that have

significant effect on capital structure based on theories in the context of Indonesian firms, so this

research concentrates on a group of variables identified in the previous literature. The selected

explanatory variables are firm size, risk, profitability, tangibility and growth opportunities.

For profitability, the pecking order theory, based on works by Myers and Majluf (1984)

suggests that firms prefer internal funds rather than external funds. If external finance is required,

the first choice is to issue debt, hybrid, then eventually equity as a last resort (Brealey and Myers,

1991). This behaviour may be due to the costs of issuing new equity, as a result of asymmetric

information or transaction costs. All things being equal, the more profitable the firms are, the more

internal financing they will have, and therefore we should expect a negative relationship between

leverage and profitability. However, from the trade-off theory point of view more profitable firms

are exposed to lower risks of bankruptcy and have greater incentive to employ debt to exploit

interest tax shields.

For tangibility, according to the pecking order theory and the trade-off theory, a firm with

a large amount of fixed asset can borrow at a relatively lower rate of interest by providing the

security of these assets to the creditors. Having the incentive of getting debt at a lower interest

rate, a firm with a higher percentage of fixed asset is expected to borrow more as compared to a

firm whose cost of borrowing is higher because of having less fixed assets. Thus, we expect a

positive relationship between tangibility of assets and leverage. From a pecking order theory

perspective, firms with few tangible assets are more sensitive to informational asymmetries. These

firms will thus issue debt rather than equity when they need external financing (Harris and Raviv,

1991), leading to an expected negative relation between the importance of intangible assets and

leverage.

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For size,, according to trade-off theory, first, large firms don‟t consider the direct

bankruptcy costs as an active variable in deciding the level of leverage as these costs are fixed by

constitution and constitute a smaller proportion of the total firm‟s value. And also, larger firms

being more diversified have lesser chances of bankruptcy (Titman and Wessels 1988). Following

this, one may expect a positive relationship between size and leverage of a firm. According to

pecking order theory, Rajan and Zingales (1995) argue that there is less asymmetrical information

about the larger firms. This reduces the chances of undervaluation of the new equity issue and,

thus, encourages the large firms to use equity financing. This means that there is a negative

relationship between size and leverage of a firm.

For risk, according to these theories, the pecking order theory and trade-off theory, we

can expect that firms with higher income variability have lower leverage (Bradley et al. , 1984;

Kester, 1986; Titman and Wessels, 1988), since higher variability in earnings indicates that the

probability of bankruptcy increases. Firms that have a high operating risk can lower the volatility

of the net profit by reducing the level of debt. A negative relation between the operating risk and

the leverage is also expected from a pecking order theory perspective: firms with a high volatility

of results try to accumulate cash during good years, to avoid under-investment issues in the future.

For growth, by applying pecking order arguments, growing firms place a greater demand

on the internally generated funds of the firm. Consequentially, firms with a relatively high growth

will tend to issue securities less subject to information asymmetries, i.e. short-term debt. This

should lead to firms with relatively higher growth having more leverage. Following trade-off

theory, for companies with growth opportunities, the use of debt is limited as in the case of

bankruptcy, the value of growth opportunities will be close to zero, growth opportunities are

particular cases of intangible assets (Myers, 1984; Williamson, 1988 and Harris and Raviv, 1990).

Firms with less growth prospects should use debt because it has a disciplinary role (Jensen, 1986;

Stulz, 1990). Firms with growth opportunities may invest suboptimally, and therefore creditors

will be more reluctant to lend for long horizons. This problem can be solved by short-term

financing (Titman and Wessels, 1988) or by convertible bonds (Jensen and Meckling, 1976; Smith

and Warner, 1979).

Furthermore, while the literature is rich in studies that examine the importance of firm-

specific factors in determining a firm‟s financing choice, empirical evidence on the effect of

capital structure choice on stock market reaction is limited. When a firm issues, repurchases or

exchanges one security for another, it changes its capital structure. What are the valuation effects

of these changes? There are several theories which explain the relationship between capital

structure and stock price.

Based on signalling through capital structure, as the increased level of leverage is

accompanied by a higher risk of bankruptcy, the increased level of debt indicates the confidence of

the management in the future prospects of the firm. Hence, it carries greater conviction than a

mere announcement of undervaluation of the firm by the management. On the other hand, an issue

of equity is a signal that the firm is overvalued. The market concludes that the management has

decided to offer equity because it is valued higher than its intrinsic worth by the market. The

markets normally react favourably to moderate increases in leverage and negatively to a fresh

issue of equity.

Under the trade-off theory, firms will only take actions if they expect profits. An

implication of the theory is that the market reaction to both equity and debt securities will be

positive. The market response to a leverage change consists of two pieces of information: the

revelation of the information that the firm‟s conditions have changed, necessitating financing, and

the impact of the financing on security valuations. The information contained in security issuance

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decisions could be either bad news or good news. It might be bad news if the company is issuing

securities, because the company actually needs more resources than anticipated to carry out

operations. It would be good news if the company is issuing securities to take advantage of a

promising new opportunity that was not previously anticipated. A company may also issues

securities to anticipate a change in future needs. This indicates that the trade-off theory by itself

places no apparent limitations on the effect of market valuation of issuing decisions.

Jung et al. (1996) suggest an agency perspective and argue that equity issues by firms

with poor growth prospects reflect agency problems between managers and shareholders. If this is

the case, then stock prices would react negatively to news of equity issues. The pecking order

theory is usually interpreted as predicting that securities with more adverse selection (equity) will

result in more negative market reaction. Securities with less adverse selection (debt) will result in

less negative or no market reaction. This does of course, still rest on some assumptions about

market anticipations.

Literature offers various explanations for buybacks. One of these explanations has

theoretical backgrounds and some are formed from empirical studies. The undervaluation

hypothesis is explaining our hypotheses. Stock repurchases offer flexibility in the choice to

distribute excess funds and when to distribute these funds. This flexibility in timing is valuable

because firms can wait to repurchase until the stock price is undervalued. The undervaluation

hypothesis is based on the argument that information asymmetry between insiders and

shareholders can cause a company to be misvalued. If insiders trust that the stock is undervalued,

the firm may repurchase stock as a signal to the market or investing in its own stock and get

mispriced shares. This hypothesis implies that the market interprets the action as an indication that

the stock is undervalued (in Dittmar, 1999). Because of the asymmetric information between

managers and shareholders, announcements of share repurchase are considered to expose private

information that managers have about the value of the company.

The information/signalling hypothesis has three immediate implications: repurchase

announcements should be accompanied by positive price changes; repurchase announcements

should be followed (though not necessarily immediately) by positive news about profitability or

cash flows; and repurchase announcements should be immediately followed by positive changes in

the market‟s expectation about future profitability (Gustavo Grullon and Roni Michaely, 2002).

1.1.2. Research Motivation

How do firms finance their operations? What factors influence these choices? How do

these choices affect the stock price? And how do firms finance their operations over the firm‟s life

cycle? These are important questions that have motivated the researcher to conduct this research.

Based on theories explanation above, we understand that a firm‟s characteristics, cost and

benefit, market reaction, and a firm‟s life cycle influenced the choice of a firm‟s capital structure,

and it is important for the manager of a firm to understand the theory of capital structure. There

have been many previous studies which examine one of thatfactors in influencing the choice of a

firm‟s capital structure; however, there have been few that analyse all factors on the whole in

affecting the choice of a firm‟s capital structure.

Based on that motivation, through this research, we examine those factors on the choice

of a firm‟s capital structure by formulating research hypotheses. We examine all the following

issues, the determinants of capital structure of the firms in Indonesia, study how firms in

manufacturing sector raise capital for investments; investigate what will happen to the firm‟s stock

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price if firms issue new debt, issue new equity, and issue debt to repurchase equity; and examine

how firms in Indonesia raise capital for investments over their life cycle stages.

Our motivation to test Hypothesis 1 is that the test of determinants of capital structure of

the firms in manufacturing sector in Indonesia is important as these firms have different

characteristics. We test it on the basis of the pecking order theory and the trade-off theory. The

trade-off theory and the pecking order theory imply that growth opportunities and asset tangibility

have a positive relationship with the debt ratio, while the relationship between risk (earnings

volatility) and debt ratio is negative. The pecking order hypothesis implies that a firm‟s

profitability and size have a negative relationship with the level of debt. Under trade-off theory

size and profitability have a positive relationship with the debt ratio.

The important thing when examining hypothesis 2 in this research, is that we would like

to test how firms in the manufacturing sector in LQ45 index finance the firms‟ deficit, as these

firms are experiencing financial deficit over the period of time (see table). Our analysis is related

to Shyam-Sunder and Myers (1999) and Frank and Goyal (2003), who propose to test the standard

pecking order using a regression of debt issued on the financing deficit. The argument is that the

original pecking order predicts that firms issue debt whenever their internal cash flows are

insufficient to finance real investments (and other uses of funds such as dividends). The financing

deficit, i.e. uses of funds minus internal sources of funds, therefore drives debt issuance.

Our motivation to test hypothesis 3 is that, as empirical evidence on the effect of capital

structure choice on stock market reaction is limited, hence, we examine the relationship between

capital structure and stock price, based on the pecking order theory, the trade-off theory, the

signalling theory, and asymmetric information. Based on signalling through capital structure, the

markets normally react favourably to moderate increases in leverage and negatively to a fresh

issue of equity.

Under the trade-off theory, firms will only take actions if they expect benefits. An


positive. The market response to a leverage change could be either good news or bad news. It is

good news if the firm issues securities to take advantage of a promising new opportunity that has

not previously been anticipated. It might be bad news if the firm issues securities because the firm

actually needs more resources than anticipated to conduct operations.

The pecking order theory is usually interpreted as predicting that securities with more

adverse selection (equity) will result in a more negative market reaction. Securities with less

adverse selection (debt) will result in less negative or no market reaction. Meanwhile, the

explanations for buybacks are based on the information/signalling hypothesis that has three

immediate implications: repurchase announcements should be accompanied by positive price

changes; repurchase announcements should be followed (though not necessarily immediately) by

positive news about profitability or cash flows; and repurchase announcements should be

immediately followed by positive changes in the market‟s expectation about future profitability.

The most interesting part of this research is testing hypothesis 4. We examined capital

structure choices over the firm´s life cycle as our sample consists of 10 mature firms and 16

growth firms, where we define mature firms as firms that have 6-year dividend payment periods.

Frank and Goyal (2003) argue that the support for the standard pecking order in Shyam-Sunder

and Myers depends critically on their sample selection. Shyam-Sunder and Myers consider 157

firms that have no reporting gaps in their statement of cash-flows from 1971 to 1989. Frank and

Goyal (2003) show that the results do not extent to an unbalanced sample, i.e. when reporting gaps

17

are allowed and to the time period from 1990 to 1998. Frank and Goyal (2003) argue that the

sample selection of Shyam-Sunder and Myers picks large mature firms and that the standard

pecking order is not a good description of the capital structure decisions for small, young firms in

their larger sample. Hence, it is important to examine capital structure choices over firm life cycle.

Therefore, we then construct the following variables for our analysis: book leverage,

market leverage, net equity issued, net debt issued, financing deficit, stock price, tangibility,

profitability, risk, growth, and size. We first classify firms into two cohorts according to their life

cycle stage, namely, firms in their growth stage and firms in their mature stage. We then focus on

the pecking order theory of financing proposed by Myers (1984) and Myers and Maljuf (1984).

This theory is based on asymmetric information between investors and firm managers. Due to the

valuation discount that less-informed investors apply to newly issued securities, firms resort to

internal funds first, then debt and equity last to satisfy their financing needs. In the context of a

firm‟s life cycle, we expect that asymmetric information problems are more severe among young,

growth firms compared to firms that have reached maturity. Hence, the theory predicts that

younger, fast-growth firms should be following the pecking order more closely.

Our research findings could be the comparison to the findings of previous research and

theories. This is how this thesis adds to the scientific literature.

1.2 Problem Identification

In order to keep developing, the firms in the manufacturing sector need to finance their

financial deficit or even new projects, hence it is important to firms to implement the theories of

capital structure described earlier in choosing carefully their capital structure for financing the

investment. Firm managers can consider the cost and benefit of each capital structure preferences

based on the theories as each preference will affect market reaction which is reflected by the firm‟s

stock price valued by the market and the firm‟s life cycle which influences the choice of the firm‟s

capital structure.

Table 1.1. GDP Sectors (in Billion Indonesian Rupiah, IDR)

Sector 1994 1995 1996 1997

Agriculture 66071.5 77896.2 88791.7 100150.5

Mining 33507.1 40194.7 46088.1 54509.9

Manufacturing Industry 89240.7 109688.7 136425.8 159747.7

Electricity 4577.1 5655.4 6892.7 7939.3

Building 28016.9 34451.9 42024.8 46181.1

Trade 63858.7 75639.8 87137.4 103762.8

Transportation 27352.6 30795.1 34926.4 42231.8

Financial Institutions 34505.6 39510.4 43981.6 58691.2

Services 35089.4 40681.9 46299.5 52291.7

Sources: Indonesia Stock Exchange, IDX (2011)

We choose firms in the manufacturing sector as our sample because the sector has grown

faster than any other sector in the Indonesian economy in 1994-1997. However, the GDP

decreased significantly in 1998, but within the years 1999-2007, the GDP was unstable. For

instance, in 1994 (see table 1.1), the GDP of the sector was only 89240.7. By 1994, the sector had

increased to 109688.7. In 1996, the sector increased to 136425.8 and in 1997 reached the level of

159747.7 (IDX, 2011).

18

Table 1.2a. GDP Sectors (%)

Sector 1998 1999 2000 2001 2002

Agriculture –1,3 2,7 1,7 0,6 3,2

Mining –2,8 -2.4 2.3 -0,6 1.0

Manufacturing Industry –11,4 3,8 6,2 4,3 5.3

Electricity 3,0 8,3 8.8 8,4 8,9

Building –36,4 -0.8 6.7 4,0 5,5

Trade –18,2 0,1 5,7 5,1 3.9

Transportation –15,1 -0,8 9.4 7,5 8,4

Financial Institutions –26,6 -7,5 4,7 3,0 6,4

Services –3,8 1,9 2,2 2,0 3,8

Sources: Indonesia Stock Exchange (2011)

Table 1.2b. GDP Sectors (%)

Sector 2003 2004 2005 2006 2007

Agriculture 3,8 2,8 2,7 3,4 3,5

Mining -1,4 -4,5 3,2 1,7 2,0

Manufacturing Industry 5,3 6,4 4,6 4,6 4,7

Electricity 4,9 5,3 6,3 5,8 10,4

Building 6,1 7,5 7,5 8,3 8,6

Trade 5,4 5,7 8,3 6,4 8,5

Transportation 12,2 13,4 12,8 14,4 14,4

Financial Institutions 6,7 7,7 6,7 5,5 8,0

Services 4,4 5,4 5,2 6,2 6,6

Sources: Indonesia Stock Exchange (2011)

In 1998, the contribution of manufacturing industries to total GDP was -11.4%, and

increased to 3.8% in 1999. By 2000, the sector had increased to 6.2 percent of GDP. In 2001, the

sector decreased to 4.3 percent of GDP. In 2002 and 2003, the contribution of manufacturing

industries to total GDP was 5.3%, and increased to 6.4% in 2004. However, in 2005, the

contribution of manufacturing industries to total GDP was decreased to 4.6% in 2005 and 2006,

and increased to only 4.7% in 2007. Meanwhile, the total export in 1980 of manufacturing

industries was 2.3% (World Bank, 2003). The export from manufacturing industries continued to

increase and by 1990 it was accounted for 35.5% of the total export in that year. In 2001 more than

56% of the total export was from manufacturing industries (Indonesia Statistical Centre, 2003).

Therefore, firms in the manufacturing sector of the LQ45 Index need to implement the

theories of capital structure to choose their capital structure for financing the investment, so that

they could increase the production and profit. Additionaly, we choose the LQ45 Index as the index

represents 45 of the most liquid stocks. To date, the LQ45 Index covers at least 70% of market

capitalisation and transaction values in the Regular Market and it consists of 45 stocks that have

passed the liquidity and market capitalisation screenings (Indonesia Stock Exchange, 2011).

19

1.3 Research Questions

The research is going to answer the following major and minor research questions:

1.3.1 Major Research Questions

Our major research questions are as follow:

1. What are the determinants of capital structure of the firms in the manufacturing sector in

Indonesia?

2. How do firms in the manufacturing sector in Indonesia raise capital for investments,

internally or externally (with debt, equity, or debt to repurchase equity)?

3. Does debt policy matter?

4. In the context of firm‟s life cycle, can we expect that growth [and small] firms follow the

pecking order theory more closely than mature [and large] firms?

1.3.2 Minor Research Questions

Our minor research questions are as follow:

1. What are the determinants of capital structure of the firms in the manufacturing sector in

Indonesia?

a. As implied by the trade-off theory and the pecking order theory, do growth

opportunities have a positive relationship with the debt ratio?

b. As in the pecking order hypothesis, does the firm‟s profitability have a negative

relationship with the level of debt? And as implied by the trade-off theory, does the

firm‟s profitability have a positive relationship with the debt ratio?

c. In accordance with the pecking order theory and trade-off theory, is there a negative

relationship between risk (earnings volatility) and debt ratio?

d. As suggested by the trade-off theory, does size have a positive relationship with the

debt ratio? And as suggested by the pecking order theory of the capital structure, is

there a negative relationship between the level of debt and the size of the firm?

e. In accordance with the trade-off theory, is there a positive relationship between the

asset tangibility and the level of debt?

2. How do firms in the manufacturing sector in Indonesia raise capital for investments,

internally or externally (with debt, equity, or debt to repurchase equity)?

3. Does debt policy matter?

Based on the asymmetric information, the firms use equity financing only as the last

resort and based on signalling theory, the markets normally react favourably to moderate

increases in leverage and negatively to a fresh issue of equity.

20

(a) If a firm issues new debt, what will happen to the firm‟s stock price?

(b) If a firm issues new equity, what will happen to the firm‟s stock price?

(c) If a firm issues debt to repurchase equity, what will happen to the firm‟s stock price?

4. In the context of firm‟s life cycle, can we expect that growth [and small] firms follow the

pecking order theory more closely than mature [and large] firms?

1.4 Research Objectives

Based on research questions, the objectives of this research are to:

1. Determine the determinants of capital structure of firms in the manufacturing sector in the

Indonesian capital market.

a. Investigate the relationship between growth and debt ratios as implied by the trade-off

theory and the pecking order theory.

b. Examine the relationship between a firm‟s profitability and debt ratios as implied by

the trade-off theory and the pecking order theory.

c. Determine the relationship between risk (earnings volatility) and debt ratios as implied

by the trade-off theory and the pecking order theory.

d. Investigate the relationship between size and debt ratios as suggested by the trade-off

theory and the pecking order theory.

e. Analyse the relationship between asset tangibility and debt ratios as implied by the

trade-off theory.

2. Investigate how firms in manufacturing sector raise capital for investments, internally or

externally (with debt, equity, or debt to repurchase equity).

3. Examine whether debt policy matters:

(a) Analyse if a firm issues new debt, what will happen to the firm‟s stock price.

(b) Analyse if a firm issues new equity, what will happen to the firm‟s stock price.

(c) Analyse if a firm issues debt to repurchase equity, what will happen to the firm‟s

stock price.

4. Examine in the context of firm‟s life cycle, do growth [and small] firms follow the pecking

order theory more closely than mature [and large] firms.

1.5 Scope and Limitation of the Study

The scope of the study is to investigate the determinants of capital structure of the firms

in the manufacturing sector in Indonesia, examine how firms in the manufacturing sector raise

capital for investments, internally or externally (with debt, equity, or debt to repurchase equity),

investigate if debt policy matters, what will happen to the firm‟s stock price if firms issue new

debt, issue new equity, and issue debt to repurchase equity. Finally, we examine in the context of

21

firm‟s life cycle, do growth [and small] firms follow the pecking order theory more closely than

mature [and large] firms.

Manufacturing companies that exist throughout the 13-year period with no missing data

are included in the study. Data availability is a major limitation in capital structure studies in

emerging capital markets. We use data of the Indonesia Stock Exchange Main Board companies,

with the selected time period of 1994-2007 to capture the differences in economic conditions of

the Indonesian economy. To enlighten it, we explain those periods that describe the differences in

economic conditions.

Before the Crisis Period (Before 1997)

Before the economic crisis triggered by the financial crisis in mid 1997, Indonesia was

among the few developing countries which were rated as highly successful in its development.

Within thirty years, from 1965 to 1995, GDP per capita in real terms grew on average by 6.6%

annually (World Bank 1997). The role of manufacturing industry in GDP experienced a significant

increased, from 7.6% in 1973 to nearly 25% in 1995.

Crisis Period (1997-1998)

In 1995 Indonesia was still enjoying an economic growth of 8.2%, later in 1996, or the

last year before the crisis happened, it still grew with 7.8%, and in 1997 dropped to 4.9%. So, until

1997, the year of the crisis, at least, economic growth still remained positive despite showing a

declining trend. The crisis that began with the fall of the Thai Baht in July 1997, then gave a direct

result on the value of IDR which depreciated exponentially, from Rp2.400 per Dollar in mid-1997

to Rp16.000 per Dollar in June 1998. The decline in food production triggered high inflation in

1998, added pressuring on foreign exchange reserves that have already declined.

In 1998, when the crisis reached its peak, Indonesia's economic growth contracted by

13.6% and other macroeconomic indicators showed worsening values, such as inflation which

increased to 77.6%. The crisis that hit Indonesia since mid 1997 has gradually decreased and by

the end of the reporting year 1998/99 Indonesian economy began to show improvement.

Inflationary pressures continued to decline from October 1998 onwards, so that the annual

inflation rate had reached 82.4% in September 1998, and was successfully reduced to 45.4% at the

year-end report. The success in reducing inflationary pressures reflected in the strengthening trend

of the IDR.

1999-2000

In 1999, inflation rate was under control, from almost 80% in 1998 to 2% in the

following year, With these conditions, the interest rate could drop from about 80% to 11-12%. By

mid-1999, the economic crisis in Indonesia had surpassed its lowest point and began to grow

again. Throughout the year, the economy grew slightly with an increase in GDP of 0.3%.

Entering early 2000, the process of economic recovery had begun to appear since the

third quarter of 1999. Monetary stability was also controllable, as reflected in the achievement of

low inflation and stronger exchange rate until the end of 1999. Economic growth was increasing

higher than forecasted to 4.8%. The IDR tended to weaken and volatile since May 2000.

Meanwhile, pressure on the inflation rate increased and inflationary pressures also emerged as a

result of the weakening of the IDR.

22

2001-2004

During the 2001, economic and monetary conditions in general showed a deteriorating

trend. Worsening the economic and monetary conditions, among others, indicated by the slowing

economic growth, a weakening exchange rate, and high inflation pressures. During 2001,

Indonesia's economy grew only by 3.3%, the exchange rate depreciated by 17.7% so that to

achieve an average of Rp.10.255 per USD Dollar, and CPI inflation reached 12.55%.

During 2002, the general economic condition in Indonesia showed a positive growth

which was indicated by more stable macroeconomic conditions. Overall, in 2002, the exchange

rate appreciated significantly by 10.10% so as to achieve an average of Rp 9.316 per US Dollar.

These stable monetary conditions have affected the level of CPI inflation during 2002,

experiencing a declining trend to reach 10.03%. Overall, during 2002 the Indonesian economy

only grew by 3.7%.

In 2003, to face the challenges, the Government and the Bank of Indonesia have taken a

series of policies to encourage the process of economic recovery while maintaining

macroeconomic stability. In the process, various policies have contributed significantly in

supporting the achievement of stable macro economic conditions during 2003, which indicated by

the strengthening of the IDR and declining of inflation rate. The year 2004 brang hope, optimism,

as well as a new challenge. In 2004, macroeconomic stability maintained, international confidence

increased, and clarity of the economic agenda eached.

2005-2007

The year 2005 was a dynamic and challenging one for the economy of Indonesia. On the

average, the Rupiah reached Rp 9.713 per U.S. Dollar during 2005, or a depreciation by 8.6%

compared to an average of 2004. Meanwhile, the CPI inflation, which until the third quarter of

2005 was recorded at 9.1% (year on year, yoy) had increased to 17.1% (yoy) in late 2005. Overall

economic growth in Indonesia in 2005 reached 5.6% or achieved an increase of 5.1% from the

previous year.

Entering the beginning of 2006, Indonesia economic conditions are still very influenced

by the rising of fuel prices (fuel) and high interest rates. Inflation rate of consumer price index

(CPI) which is very high in early 2006 reached 17.03% (yoy) gradually decreased to 6.60% (yoy)

in late 2006 and maintained stability in the rupiah. With inflation and interest rates which

gradually declined, since the beginning of the second half of 2006, the economy grew in the good

trend so as the overall in 2006, growth reached 5.5% (yoy), slightly lower than the previous year.

Entering 2007, Indonesia's economy to regain macroeconomic stability. The Rupiah, in

the second half of 2007 was depreciated significantly and reached the weakest level in August

2007, with a monthly average of Rp 9372 per U.S. Dollar. Maintained macroeconomic stability

kept a high economic growth in 2007, and even reached the highest level in the post-crisis period,

namely 6.32%.

1.6 Expected Contribution

By conducting this research, we expected some contributions for the firms. In this research, the

purpose of our research will not be to produce a theory that is generalisable to all populations. Our

objective is trying to explain what is happening in the Indonesian capital market with

manufacturing firms of the LQ45 Index, regarding how firms finance their operations. What

factors influence the choices of capital structure? How do these choices affect the stock price? And

23

how do firms finance their deficit over a firm‟s life cycle? The findings of this study will lead

firms to make the decision of choosing capital structure by considering the firm‟s characteristics,

market reaction reflected by stock price, and the life cycle stage of the firm.

1.7 Organisation of the Study

Figure 1.1. Organisation of the Study

The structure of the thesis is illustrated in the above figure. In more detail, chapter 1

provides an introduction consisting of the background of the research, problem identification and

research problems, research questions, research objectives, and significance of study, which

include scope and limitations of the study, expected contribution, and organisation of the study.

The work in chapter 2 reflects an overview of a firm‟s capital structure in Indonesia.

Chapter 3 explains literature review. Chapter 4 provides conceptual framework and research

methodology. This chapter clearly identifies and analyses gaps in the literature as well as it

demonstrates the theory from which we derive each hypothesis, and identify dependent and

independent variables and link these to relevant research questions and respective hypotheses.

Chapter 5 analyses research methodology. Chapter 6 should integrate both presentation of

data and analysis of results. Chapter 7 draws the conclusions, recommendations, and suggestion

for further research. Finally, the appendix will give a depiction of statistical information gathered

during the research, and figures of the firms.

CHAPTER 1. Introduction CHAPTER 2. An Overview of

Firm’s Capital Structure in

Indonesia

CHAPTER 3. Literature Review CHAPTER 4. Conceptual

Framework

CHAPTER 6. Presentation of Data

and Analysis of Results

CHAPTER 7. Conclusion,

Recommendations, and

Suggestion for Further Research

Appendices

CHAPTER 5. Research

Methodology

24

2. AN OVERVIEW OF THE CAPITAL STRUCTURE OF INDONESIAN

MANUFACTURING FIRMS

2.1. Indonesian Capital Market

The capital market plays an important role in the economy of a country, including

Indonesia, because it serves two functions at the same time. First, the capital market serves as an

alternative for a company's capital resources. The capital gained from the public offering can be

used for the company's business development, expansion, and so on. Second, the capital market

serves as an alternative for public investment. People could invest their money according to their

preferred returns and risk characteristics of each instrument.

2.1.1 History of Indonesia Stock Exchange

Below is the brief history of the Indonesia Stock Exchange. The first Stock Exchange in

Indonesia was built in Batavia (currently known as Jakarta) in December 1912. The Batavia Stock

Exchange was closed during the years 1914 -1918. It was re-opened in 1925 and new stock

exchanges were established in Semarang and Surabaya. However, between 1919 and 1924, the

Indonesia Stock Exchange (IDX) was still closed.

The Jakarta Stock Exchange (JSX) was re-closed during the years 1942 – 1952. On

August 10, 1977, the Exchange was re-activated by President Soeharto. It was supervised under

the management of the Capital Market Supervisory Agency (Badan Pengawas Pasar Modal, or

BAPEPAM). The re-activation of the capital market was also marked by the going public of PT

Semen Cibinong as the first issuer listed in the JSX. July 10th is celebrated as the anniversary of

the Capital Market in Indonesia.

In 1977 – 1987, the activity of stock trading in the JSX was dull. There were only 24

listed companies in the JSX. Most people preferred to invest their money in banks rather than the

capital market. December Package 1987) was issued to give ways for companies to go public and

for foreign investors to invest their money in Indonesia in 1987. In 1988 – 1990, deregulation

packages in banking and capital market were made. The JSX welcomed foreign investors. The

activities of the JSX were improving. On June 16, 1989, the Surabaya Stock Exchange started to

operate and was managed by the Surabaya Stock Exchange Inc.

On July 13, 1992, the JSX was privatised, and this date is celebrated as the anniversary of

the Jakarta Stock Exchange. The JSX introduced its computerized Jakarta Automatic Trading

System (JATS) on May 22, 1995. On November 10, 1995, the Government of Indonesia issued

Regulations No. 8 year 1995 on the capital market. This regulation was effective from January

1996. The JSX started to implement the remote trading system in 2002. In 2007, the Surabaya

Stock Exchange was merged into Jakarta Stock Exchange. As a result, the JSX changed its name

into the Indonesia Stock Exchange.

2.1.2 Stock Price Index in the Indonesian Capital Market

In order to give more complete information on the stock exchange development to the

public, the Indonesian Stock Exchange (IDX) has spread the indicators of the stock price

25

movement through the printed and electronic media. One indicator of the stock price movement is

the Stock Price Index. At present, the JSX has 9 constituent Stock Price Indices and 10 sectors:

Composite Stock Price Index (CSPI), Main Board Index (MBX), Kompas 100, Liquid 45

(LQ45), Jakarta Islamic Index, Development Board Index (DBX), Indonesian Securties Rating

Agency (PEFINDO25), BISNIS-27, and Sustainable Responsible Investment-Indonesian

Biodiversity Foundation. The sectors include mining, agriculture, consumers, miscellaneous-

industry, manufacture, infrastructure, finance, trade, basic-industry, and property.

The following indices are guidelines for investors to make stock investment in the

Indonesian capital market.

1. The Composite Stock Price Index (CSPI), the index that uses all of the Companies Listed as a

component of index calculation. The Composite Stock Price Index (CSPI) was introduced the

first time on April 1st, 1983 as an indicator of the movement of all listed stock prices in the

JSX, for both the regular and the preferred stocks. The base day for the CSPI‟s calculation is

on August 10th

, 1982. At that date, the index was determined at 100, and the listed number of

stocks at that time was thirteen.

2. The JSX LQ45 Index was created to provide the market with an index that represents 45 of

the most liquid stocks. To date, the LQ45 Index covers at least 70% of the market

capitalisation and transaction values in the Regular Market. The LQ45 Index of historical

calculation was defined on July 13, 1994, with a base value of 100. The index consists of 45

stocks that have passed the liquidity and market capitalisation screenings.

3. The Jakarta Islamic Index was launched on July 3, 2000. The index consists of 30 stocks that

have passed the selection under the direction of the Sharia Supervisory Board of the Majelis

Ulama Indonesia. Stocks from listed companies with business activities that comply with the

Islamic sharia can be included into the index.

4. The Kompas 100 Index is an index consisting of 100 shares of Listed Companies that are

selected, based on considerations of liquidity and market capitalisation, in line with

predetermined criteria.

5. The Index BUSINESS-27 is a collaboration between the IDX and Bisnis Indonesia Daily. The

Stock index Listed Companies are selected based on fundamental criteria, technical or

liquidity of transactions and accountability and corporate governance.

6. The PEFINDO-25 Index is a collaboration between the BEI and the PEFINDO rating

agencies, which is intended to provide additional information for investors, especially for the

shares of small and medium-sized listed companies (Small Medium Enterprises/SME).

7. The Sustainable Responsible Investment-KEHATI Index is the index established for

cooperation between the BEI and the Indonesian Biodiversity Foundation (KEHATI). This

index is expected to provide additional information to investors who want to invest in stocks

that have excellent performance in encouraging sustainable business, and have awareness of

the environment and run good corporate governance.

8. The Main Board Index (MBX) and the Development Board Index (DBX). On July13th

, 2000,

the JSX launched a new rule on stock listing: the Two Board Listing System. This system is

implemented to stimulate the Indonesian Capital Market and also to recover public confidence

for the Exchange through the arrangement of good corporate governance.

9. Sectoral indices, the index that uses all the Listed Companies included in each sector. Today

there are 10 sectors in the IDX, namely agriculture; mining;, primary industrie;, miscellaneous

industry;, consumer goods; property; infrastructure; finance; trade and services; and

manufacture.

26

2.1.3. Description of the LQ45 Index

We chose the LQ45 Index as our population as LQ45 Index consists of 45 stocks with

high liquidity. The Indonesia Stock Exchange regularly monitors the performance progress of the

stock components which are included in the calculation of the LQ45 index. Every three months an

evaluation on the movement sequence of the shares is conducted. Replacement shares will be

conducted every six months, i.e. at the beginning of February and August. Therefore, we chose the

LQ45 index as our population in this research.

Since its launch in February 1997, the primary measure of liquidity transaction is the

value of transactions in the regular market. In accordance with market developments, and to

sharpen further the criteria of liquidity, since the review in January 2005, the number of trading

days and the frequency of transactions has been included as a measure of liquidity. Thus, the

criterium of stock that is to be included in the calculation of the LQ45 Index is as follows:

1. Has been listed on the Stock Exchange at least 3 months

2. Log in 60 stocks based on the value of transactions in the regular market

3. Of the 60 stocks, 30 stocks with the largest transaction value will automatically be included on

the calculation of the index LQ45.

4. To get 45 shares 15 shares will be selected again by using the criteria of day transaction in

regular market, frequency of transaction in regular market and market capitalisation. 15 stocks

selection methods are the following:

a. 30 of the remaining stocks, 25 stocks are selected based on transactions day in the regular

market.

b. 25 of the stocks 20 stocks will be selected based on the frequency of transactions in the

regular market.

c. 20 of the stocks will be selected 15 stocks based on market capitalisation, so it will get 45

shares for calculation of the LQ45 Index.

5. In addition to considering the liquidity criteria and market capitalisation mentioned above, will

be seen also the financial condition and prospects of the company's growth.

2.2. Characteristics of the Research Sample

We constructed two samples of firms according to their life cycle stage, namely, firms in

their growth stage and firms in their mature stage. Bulan and Yan (2009) defined the growth stage

as the first six-year-period after the year of the firm‟s initial public offering (IPO). They treated the

IPO as the starting point of the growth stage (or the “new growth” stage). Hence, we follow them

to identify the growth stage. We took Grullon, Michaely and Swaminathan (2000), DeAngelo,

DeAngelo and Stulz (2005) and Bulan, Subramanian and Tanlu as the references (2007) who

found that firms initiated dividends were mature firms. Thus, we identified firms in their mature

stage by their dividend history.

Meanwhile, we defined six years old or younger as young firms and seven years or older

as old firms. We followed Bulan and Yan (2007) and Evans (1987) to set the length of each stage

to be 6 years. Finally, we defined "small" as firms with total assets of less than $150 million, and

large firms that have total asset of more than $150 millions (Hufft, JR), it equals to IDR

1,081,028.68 or 1,086,876.61.

27

1. Astra International Tbk (ASII)

ASII is a company engaged in the sector of miscellaneous industry, by the industrial sub

sector of Automotive and Components. It was established on February 20, 1957 and was listed at

the IDX on April 4, 1990. Its IPO price was IDR 14850. In the period of 1996 to 2009 the price

was very volatile. In July 2010 the price was increased significantly to IDR 50700.

The average total asset of ASII in the year 1994 to 2007 is 30,934,935.6 million. Hufft,

JR defines a small firm as the one that has total assets of less than USD 150 million. That means a

firm with total assets of more than USD 150 million is considered a large firm. Hence ASII is a

large firm. The average values of variable capital structure that consists of profitability, tangibility,

size, risk, and growth are 0.08788698, 0.23579566, 17.1321592, 0.07680013, and 1.16396919.

While the average short-term, long-term, total, and its market leverage are 0.30517311,

0.2847916, 0.6952388, and 0.6213082.

Bulan, Subramanian, and Tanlu (2007) find that firms that initiate dividends are mature

firms. Thus Bulan and Yan (2007) identify firms in their mature stage by their dividend history.

We take Bulan and Yan (2007) the references to construct the sample, deeming 6-year dividends

payment periods as the mature stage of a firm‟s life cycle. This 6-year requirement is to ensure that

whatever reason for the dividend omission, the firm has fully recovered and re-emerged as a

regular dividend payer. ASII has paid the dividend during 2002 to 2007 and 1994 to 1996, thus it

is categorised as a mature firm.

2. Astra Otoparts Tbk (AUTO)

AUTO is a company engaged in the sector of miscellaneous industry, by the industrial

sub-sector of automotive and components. The company was established on September 20, 1991

and was listed at the IDX on October 1, 1993. Its IPO price was IDR 575. In 1998 its price had

been decreased to IDR 375. During 1999 to 2002 its price was slightly volatile.

The total assets of AUTO in the period of 1994 to 2007 was IDR 1.347.318 million. Thus

we take AUTO as a small firm. The average values of variable capital structure that consists of

profitability, tangibility, size, risk, and growth are 0.089465, 0.239452, 14.04898, 0.102035, and

0.82562826. The average short-term, long-term, total, and its market leverage are 0.450023,

0.113362, 0.563793, and 0.729617, AUTO was listed in 1993 and has consecutively paid the

dividend for seven years during 2001 to 2007. So we consider AUTO as a mature firm.

3. Polychem Indonesia Tbk (ADMG)

ADMG is a company engaged in the sector of miscellaneous industry, by the industrial

sub-sector of textile and garment. It was established on April 25, 1986 and was listed at the IDX

on October 20, 1993. Its IPO price was IDR 4250 and the price was slightly volatile during 2001

to 2005. In August 2010, its share price was decreased significantly to IDR 164.

The average total assets of this company during 1994 to 2007 were IDR 6,191,532.33

million or more than USD 150 million, so it is a large firm. Its average values of the variable

capital structure that consists of profitability, tangibility, size, risk, and growth are -0.03673,

0.628035, 15.61444, 0.10666, and 0.80465162. The average short-term, long-term, total, and its

market leverage are 0.415207, 0.617711, 1.032918, and 0.950757. As ADMG has not paid

dividend in the period of 1994-2007, or in the 6-year period, hence, it was categorised as a growth

firm.

28

4. Barito Pacific Tbk (BRPT)

BRPT is a company engaged in the sector of basic industry and chemicals, by the

industrial sub-sector of chemicals. It was established on April 4, 1979 and was listed at the IDX on

October 1, 1993. Its IPO price was IDR 7200 and the price was very volatile during 1996 to 2007.

In 2001 it hit the lowest price of IDR 50, but in July 2010, it bounced to IDR 1060. But still it is

lower than its IPO price.

We take BRPT as a large firm since the average total assets during 1994 to 2007 were

IDR 4,107,897.43 million or over USD 150 million. The average values of variable capital

structure that consists of profitability, tangibility, size, risk, and growth are -0.01767, 0.147128,

15.121, 0.0757, and 1.5739773. The average short-term, long-term, total, and its market leverage

are 0.4942, 0.179966, 0.673681, and 0.750922. BRPT has paid dividend to shareholders in 1994-

1996, thus, it is categorised as a growth firm.

5. Budi Acid Jaya Tbk (BUDI)

BUDI is a company engaged in the sector of basic industry and chemicals, by the

industrial sub-sector of chemicals. It was established in Jan 15, 1979 and was listed at the IDX on

May 8, 1995. Its IPO price was IDR 3000 and the price during 199 to 2000 was very volatile. In

August 2010 it was decreased significantly to IDR 335.

We take this company as small firm since its average total assets in 1994 to 2007 were

IDR 496,726.67 million or less than USD 150 million. Its average values of variable capital

structure that consists of profitability, tangibility, size, risk, and growth, are 0.095584, 0.504481,

12.89337, 0.092448, and 0.72631332. The average short-term, long-term, total, and its market

leverage are 0.283404, 0.344108, 0.571919, and 0.812384. BUDI has paid dividend in 1994 to

1996, 1999, and 2006 to 2007, but it was not paid in the six year period consecutively thus, it is

categorised as a growth firm.

6. Charoen Pokphand Indonesia Tbk (CPIN)

CPIN is a company engaged in the sector of basic industry and chemicals, by the

industrial sub-sector of pet food. It was established on January 7, 1973 and was listed at the IDX

on March 18, 1991. The IPO price was IDR 5100 and the price was quite volatile in 1996 to 2000

and in 2003 to 2007. But in August 2010, it was increased to IDR 6450 million, or more than USD

150 million. Its average values of variable capital structure that consists of profitability, tangibility,

size, risk, and growth, are 0.048659, 0.30468, 14.31341, 0.080364, and 1.25243854. CPIN is a

growing firm since it paid the dividend not in 6 consecutive years in 1994 to 1996, and 2006.

7. Dankos Laboratories Tbk (DNKS)

DNKS is a company engaged in the sector of pharmaceuticals. It was established on

March 25, 1974 and was listed at the IDX on November 13, 1989. The IPO price was IDR 6500.

The price was quite volatile in 1996 to 2002 and hit the lowest price of IDR 250.

DNKS is a small firm since its average total assets in 1994 to 2007 was IDR 377,072.67

million or less than USD 150 million. Its average values of variable capital structure that consists

of profitability, tangibility, size, risk, and growth, are 0.108408, 0.136731, 12.72363, 0.095927,

and 0.96989997. The average short-term, long-term, total, and its market leverage are 0.36937,

0.27362, 0.584694, and 0.724032. DNKS is a growing firm since it did not pay the dividend in 6

consecutive years.

29

8. Fajar Surya Wisesa Tbk (FASW)

FASW is a company engaged in the sector of basic industry and chemicals, by the

industrial sub-sector of pulp and paper. It was established on June 13, 1987 and was listed at the

IDX on December 19, 1994. The IPO price was IDR 3200 and it was slightly volatile in 1996 to

2000. In August 2010 it was IDR 2300.

FASW is a large firm since its average total assets during 1994 to 2007 were IDR

1,811,608.00 million, or more than USD 150 million. Its average values of variable capital

structure that consists of profitability, tangibility, size, risk, and growth, are -0.01695, 0.650274,


leverage are 0.356373, 0.323662, 0.680035, and 0.972888. FASW is categorised as a growing firm

since it paid the dividend only for 1994, 1995, and 1999.

9. Gudang Garam Tbk (GGRM)

GGRM is a company engaged in the sector of consumer goods industry, by the industrial

sub-sector of tobacco manufacturers. It was established on June 26, 1958 and was listed at the IDX

on August 27, 1990. The IPO price was IDR 10250 and during 1995 to 2007 the price was quite

stable. In July 2010, the price was increased significantly, almost triple, to IDR 35000.

GGRM is large firm since its average total assets in 1994 to 2007 were IDR

10,846,690.67 million or more than USD 150 million. Its average values of variable capital

structure that consists of profitability, tangibility, size, risk, and growth, are 0.198719, 0.227875,


leverage is 0.385222, 0.022822, 0.402338, and 0.592554. GGRM is a mature firm since it has paid

the dividend in 1994 to 1998, 2000 to 2004, and 2006 to 2007.

10. Gajah Tunggal Tbk (GJTL)

GJTL is a company engaged in the sector of miscellaneous industry, by the industrial

sub-sector of automotive and components. It was established on August 24, 1951 and was listed at

the IDX on May 8, 1990. The IPO price was IDR 5500 and during 1996 to 2007 the price was

quite stable. But in August 2010 the price decreased significantly below its IPO price to IDR 1720.

GJTL is a large firm since it had total assets of IDR 9,353,014.00 million or more than

USD 150 million during 1994 to 2007. Its average values of variable capital structure that consists

of profitability, tangibility, size, risk, and growth are -0.00404, 0.52936, 15.89235, 0.066167, and


0.44278, 0.796466, and 0.840463. GJTL paid dividend in 1994 to 1996 and 2005 to 2007, but

since it was not paid in 6 consecutive years, we categorise this firm as a growing firm.

11. Hanjaya Mandala Sampoerna Tbk (HMSP)

HMSP is a company engaged in the sector of consumer goods industry, by industrial sub-

sector of tobacco manufacturers. It was established on March 27, 1905 and was listed at the IDX

on August 15, 1990. The IPO price was IDR 12600 and its price was quite volatile during 1996 to

2002. However, the price was increased significantly to IDR 19800 in August 2010.

HMSP is a large firm since its average total assets in the year of 1994 to 2007 was IDR

5,418,818.33 million or over the USD 150 million. Its average value of variable capital structure

that consists of profitability, tangibility, size, risk, and growth are 0.171768, 0.205564, 15.2918,

30

0.113402, and 1.13077064. The average short-term, long-term, total, and its market leverage are

0.258828, 0.242296, 0.501123, and 0.566535. HMSP is a mature firm. It paid the dividend in 1994

to 1996 and 1999 to 2007.

12. Indofood Sukses Makmur Tbk (INDF)

INDF is a company engaged in the sector of consumer goods industry, by the industrial

sub sector of food and beverages. It was established on August 14, 1990 and was listed at the IDX

on July 14, 1994. The IPO price was IDR 6200 and during 1996 to 2007 the price was quite

volatile. It reached the lowest price of IDR 625 in 2001. In July 2010 it reached IDR 4625, but still

it was below the IPO price.

INDF is a large firm since its average total assets in 1994 to 2007 was IDR 11,630,675.64

million or more than USD 150 million. Its average values of variable capital structure that consists

of profitability, tangibility, size, risk, and growth are 0.08905, 0.413513, 16.09895, 0.036389, and


0.365482, 0.673448, and 0.669065. INDF is a mature firm since it paid the dividend 6 years in a

row in 1994 to 1996 and in 2000 to 2007.

13. Indorama Synthetics Tbk (INDR)

INDR is a company engaged in the sector of miscellaneous industry, by the industrial

sub-sector of textile and garments. It was established on April 3, 1974 and was listed at the IDX on

August 3, 1990. The IPO price was IDR 12500 and during 1996 to 2002 the price was slightly

volatile. In August 2010 the price decreased significantly to IDR 640.

INDR is a large firm since its average total assets in 1994 to 2007 was IDR 3,472,316.56

million, or more than USD 150 million. Its average values of variable capital structure that

consists of profitability, tangibility, size, risk, and growth are 0.039658, 0.553526, 14.88937,


0.283312, 0.326115, 0.609427, and 0.915434. INDR is a growing firm since it did not pay the

dividend in 6 consecutive years.

14. Indah Kiat Pulp and Paper Tbk (INKP)

INKP is a company engaged in the sector of basic industry and chemicals, by the

industrial sub-sector of pulp and paper. It was established on December 7, 1976 and was listed at

the IDX on July 16, 1990. The IPO price was IDR 10,600 and during 1996 to 2007 the price was

slightly volatile. In August 2010 the price decreased significantly to IDR 640

INKP is a large firm since its average total assets in 1994 to 2007 was IDR 38,541,160.07

million or more than USD 150 million. Its average value of variable capital structure that consists

of profitability, tangibility, size, risk, and growth are -0.00344, 0.666521, 17.22972, 0.021112, and


0.352736, 0.611648, and 0.88383. Since INKP did not pay dividend in 6 consecutive years, it is

categorised as growing firm.

15. Indofarma Tbk (INAF)

INAF is a company engaged in the sector of consumer goods industry, by the industrial

sub-sector of pharmacy. It was established on January 2, 1996 and was listed at the IDX on April

31

17, 2001. The IPO price was IDR 250 and it was quite stable from 2001 to 2002 and from 2006 to

2007.

INAF is a small firm since its average total assets in 1994 to 2007 was IDR 549,373.33


of profitability, tangibility, size, risk, and growth are 0.191893, 0.161847, 13.08003, 0.106093,


0.088814, 0.429693, and 0.810453. INAF has paid dividend in 2000-2001, thus we decide to

categorise it as a growth firm.

16. Indocement Tunggal Prakasa Tbk (INTP)

INTP is a company engaged in the sector of basic industry and chemicals, by the

industrial sub sector of cement. It was established on January 16, 1985 and was listed at the IDX

on December 5, 1989. The IPO price was IDR 10,000 and during 1998 to 2007 the price was quite

volatile. In July 2010 the price increased significantly to IDR 16900.

INTP is a large firm since its average total assets in 1994 to 2007 was IDR 6,510,362.43




0.456302, 0.721511, and 0.839875. INTP has paid dividend in 2005-2007, thus we decide to


17. Kalbe Farma Tbk (KLBF)

KLBF is a company engaged in the sector of consumer goods industry, by the industrial

sub sector of pharmacy. It was established on September 10, 1966 and was listed at the IDX on

July 30, 1991. Its IPO price was IDR 7800 and it was slightly volatile in 1996 to 2007. In July

2010 the price decreased significantly to IDR 2450.

KLBF is a large firm since its average total assets in 1994 to 2007 was IDR 2,564,165.14




0.17985, 14.63738, 0.08441, and 1.22087525. KLBF is categorised as a growing firm since it did

not pay the dividend in 6 consecutive years.

18. Komatsu Indonesia Tbk (KOMI)

KOMI is a company engaged in the sector of miscellaneous industry, by the industrial

sub sector of machinery and heavy equipment. It was established on December 13, 1982 and was

listed at the IDX on October 31, 1995 but delisted on January 2, 2006. The IPO price was IDR

2100 and it was slightly volatile in 1996 to 2009.

KOMI is a small firm since its average total assets in 1994 to 2007 was IDR 323,486.67

million or less than USD 150 million. Its average values of variable total assets in 1994 to 2007

that consists of profitability, tangibility, size, risk, and growth were 0.196864, 0.228239, 12.58617,


0.277463, 0.064212, 0.330973, and 0.681892. KOMI has paid dividend in 1996-1997, and 1999,

thus we decide to categorise it as a growth firm.

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19. Kimia Farma Tbk (KAEF)

KAEF is a company engaged in the sector of consumer goods industry, by the industrial

sub sector of pharmacy. It was established on January 23, 1969 and was listed at the IDX on July

4, 2001. The IPO price was IDR 200 and it was quite stable from 2001 to 2002 and from 2006 to

2007.

KAEF is a small firm since its average total assets in 1994 to 2007 was IDR 914,511.60




0.057775, 0.498936, and 0.779572. KAEF is a mature firm as it paid dividend in 6 consecutive

years from 2001 to 2007.

20. Bentoel International Investama Tbk (RMBA)

RMBA is a company engaged in the sector of consumer goods industry, by the industrial

sub sector of tobacco manufacturers. It was established on January 19, 1979 and was listed at the

IDX on March 5, 1990. The IPO price was IDR 3380 and during 1998 to 2002 the price was quite

volatile. In August 2010 the price increased significantly to IDR 520.

RMBA is a small firm since its average total assets in 1994 to 2007 was IDR 946,449.00

million or less than USD 150 million. Its average value of variable capital structure that consists of

profitability, tangibility, size, risk, and growth are 0.037075, 0.118263, 11.74052, 0.061184, and

0.9686089. The average short-term, long-term, total, and its market leverage are 0.31318, 0.16422,

0.477395, and 0.57557. RMBA is a growing firm as it has not paid the dividend in 6 consecutive

years.

21. Holcim Indonesia Tbk (SMCB)

SMCB is a company engaged in the sector of basic industry and chemicals, by the

industrial sub sector of cement. It was established on June 15, 1971 and was listed at the IDX on

August 10, 1977. The IPO price was IDR 10,000 and the price is quite stable in 1996 to 2007. In

July 2010 the price decreased significantly to IDR 2375.

SMCB is a large firm since its average total assets in 1994 to 2007 was IDR 6,335,029.07


consists of profitability, tangibility, size, risk, and growth are -0.10941, 0.750432, 15.55944,


0.40837, 0.530324, 0.862933, and 0.848687. SMCB has paid dividend in 1994, and 1996 to 1997,


22. Semen Gresik Persero Tbk (SMGR)

SMGR is a company engaged in the sector of basic industry and chemicals, by the

industrial sub sector of cement. It was established on March 25, 1953 and was listed at the IDX on

July 8, 1991. The IPO price was IDR 7000 and it was slightly volatile in 1996 to 2002. In 2006 it

was surprisingly increased to IDR 36300 but in July 2010 it decreased to IDR 9250.

SMGR is a large firm since its average total assets in 1994 to 2007 was IDR 5,729,074.22


consists of profitability, tangibility, size, risk, and growth are 0.052998, 0.608051, 15.41616,

33


0.187995, 0.295363, 0.50234, and 0.73531. SMGR has paid the dividend but not in 6 consecutive

years. So we categorise it as a growing firm.

23. Pabrik Kertas Tjiwi Kimia Tbk (TKIM)

TKIM is a company engaged in the sector of basic industry and chemicals, by the

industrial sub sector of pulp and paper. It was established on October 2, 1972 and was listed at the

IDX on April 3, 1990. The IPO price was IDR 9,500 and it was quite volatile during 1996 to 2007.

In August 2010 the price decreased significantly to IDR 3050.

TKIM is a large firm since its average total assets in 1994 to 2007 was IDR

14,313,941.33 million or more than USD 150 million. Its average values of variable capital

structure that consists of profitability, tangibility, size, risk, and growth are 0.006803, 0.606256,


leverage are 0.352695, 0.355147, 0.711, and 0.912051. TKIM has paid dividend in 1994 to 1996,


24. Tempo Scan Pacific Tbk (TSPC)

TSPC is a company engaged in the sector of miscellaneous industry, by the industrial sub

sector of pharmacy. It was established on May 20, 1970 and was listed at the IDX on June 17

1994. The IPO price was IDR 8250 and was quite volatile in 1996 to 2007. It decreased

significantly to IDR 425 in from 1997 to 1998.

TSPC is a small firm since its average total assets in 1994 to 2007 was IDR 1,056,275.78




0.134959, 0.332066, and 0.589492. TSPC is a growing firm since it did not pay the dividend in 6

consecutive years.

25. Unilever Indonesia Tbk (UNVR)

UNVR is a company engaged in the sector of consumer goods industry, by the industrial

sub sector of cosmetics and household. It was established on December 5, 1933 and was listed at

the IDX on January 11, 1982. The IPO price was IDR 3175 and it was quite volatile in 1998 to

2007. In July 2010 the price increased significantly to IDR 16950.

UNVR is a large firm since its average total assets from 1994 to 2007 were IDR

2,996,968.27 million, or more than USD 150 million. Its average values of variable capital

structure that consists of profitability, tangibility, size, risk, and growth are 0.449567, 0.28991,


leverage are 0.396292, 0.046104, 0.442157, and 0.56772. UNVR has paid dividend in 1997-2007,

thus we decide to categorise it as a mature firm.

26. Sumalindo Lestari Jaya Tbk (SULI)

SULI is a company engaged in the sector of basic industry and chemicals, by the

industrial sub sector of wood industry. It was established on April 14, 1980, and was listed at the

IDX on March 21, 1994. The IPO price was IDR 9000 and the price was slightly volatile in 2003

to 2007. In August 2010, it decreased significantly to IDR 99.

34

SULI is a large firm since its average total assets in 1994 to 2007 were IDR 1,401,294.80


consists of profitability, tangibility, size, risk, and growth are -0.02377, 0.592113, 14.13982,


0.429306, 0.469651, 0.898957, and 0.610912. SULI has not paid dividend, thus we decide to


2.3 Leverage Analysis

Four debt ratios we used in this study are total leverage, short-term leverage, long-term

leverage, and market leverage. These measures of debt ratios examine the capital employed and

thus, best represent the effects of past financing decisions. The independent variables we chose are

tangibility of assets, firm size, growth opportunities, profitability, and risk. The tangibility of

assets represents the effect of the collateral value of assets of the firm‟s gearing level. There are

various conceptions for the effect of tangibility on leverage decisions. If debt can be secured

against assets, the borrower is restricted to using debt funds for specific projects. Creditors have an

improved guarantee of repayment, but without collateralised assets, such a guarantee does not

exist.

Firm size provides a measure of the agency costs of equity and the demand for risk

sharing. Firm size is likely to capture other firm characteristics as well (e.g., their reputation in

debt markets or the extent to which their assets are diversified). For growth opportunities, the

trade-off theory suggests that firms with more investment opportunities have less leverage because

they have stronger incentives to avoid under-investment and asset substitution that can arise from

stockholder-bondholder agency conflicts (Drobetz and Fix, 2003). Jensen‟s (1986) free cash flow

theory similarly discusses that firms with more investment opportunities have less need for the

disciplining effect of debt payments to control free cash flows.

Meanwhile, profitability plays an important role in leverage decisions. Profitability is

proxied by return on assets. ROA represents the contribution of the firm‟s assets on profitability

creation. Profitability is a measure of earning power of a firm. The earning power of a firm is

generally the basic concern of its shareholders. Finally, earnings volatility measures the variability

of the firm's cash flows as a proxy for the costs of monitoring managers and of the risk of an

insider's position. The use of longer time periods causes a significant loss of the sample size.

Firms that have the highest short-term leverages are BRPT, AUTO, KAEF, CPIN, SULI,

and ADMG. Firms that have the lowest short-term leverages are SMGR, TSPC, HMSP, INKP,

INTP, BUDI, and KOMI. Firms that have the highest long-term leverages are ADMG, SMCB,

SULI, INTP, GJTL, and INDF. Firms that have the lowest long-term leverages are GGRM,

UNVR, KAEF, KOMI, INAF, and AUTO. Firms that have the highest market leverages are

FASW, ADMG, INDR, TKIM, INKP, SMCB, CPIN, and INTP. Firms that have the lowest

market leverages are HMSP, RMBA, UNVR, GGRM, KLBF, and TSPC. More firms are using

short-term leverages more than long-term leverages. Market leverages are more widely used by the

firm rather than total leverages.

Long-term leverage and tangibility have significant positive correlation, this means that

the firms with high asset tangibility have higher long-term leverage. Long-term leverage and size

have strong positive correlation, this means that a larger firm has more long-term leverage than a

small firm. Total leverage, short-term leverage, long-term leverage, and market leverage are

negatively correlated with profitability. This means that firms with high profitability will have

lower leverage. Profitability and tangibility have significant negative correlation. This indicates

that the firm with low profit can use more leverage with one condition it has a high tangibility of

35

asset to secure the leverage. Tangibility and size have significant positive correlation, meaning that

large firm should have high asset tang, so that it can use a high leverage. Growth and total leverage

have significant positive correlation and the growth is negatively correlated with market leverage.

This means that firms with higher growth use more total leverage than market leverage. Risk and

growth have significant positive correlation, this means that firms with high growth have high risk.

Profitability and risk have significant negative correlation, this means that firms with low

profitability have low risk.

The firms that have the highest profitability are UNVR, GGRM, INAF, KOMI, HMSP,

and KAEF. The firms that have the lowest profitability are SMCB, ADMG, SULI, BRPT, FASW,

and GJTL. The firms that have the highest size are ASII, INKP, TKIM, INTP, GGRM, and INDF.

The firms that have the lowest size are RMBA, KOMI, DNKS, BUDI, KAEF, and INAF. The

firms that have the highest tangibility are INTP, SMCB, INKP, FASW, ADMG, SMGR, and

TKIM. The firms that have the lowest tangibility are RMBA, DNKS, TSPC, BRPT, INAF, and

KLBF. The firms that have the highest growth are SULI, BRPT, CPIN, KLBF, ASII, HMSP, and

INDF. The firms that have the lowest growth are INAF, KAEF, INDR, FASW, and INKP. The

firms that have the highest risk are SMCB, HMSP, INAF, ADMG, AUTO, KOMI, and TSPC. The

firms that have the lowest risk are SMGR, INKP, INDR, INDF, SULI, and TKIM.

36

3. LITERATURE REVIEW

3.1 Theories of Capital Structure

One of the most insightful and important concerns in corporate finance is to determine

how firms should finance their investments and operations. This is known as the “capital

structure” problem. The study on the theory of capital structure endeavours to enlighten the use of

the mix of securities. What the theory of capital structure concerns about should be the relative

amounts of issued by firms of given securities, primary debt and equity.

In Van Horne (1998), the theory of capital structure analysed the impact of the financing

mix on the valuation of the firm. The theory also attempted to discover whether there existed an

optimal capital structure for a firm. There are broadly two schools of thought. One school believes

that the composition of the financing mix does not affect the cost of capital so that the capital

structure has no relevance in the valuation of the firm. The proponents of the other school believe

that the cost of capital is determined by the composition of the capital structure. The application of

leverage results in a change in the cost of capital. They try to determine the optimal capital

structure, at which level the overall cost of capital is minimal.

3.1.1 Modigliani-Miller Theory

Modigliani and Miller suggest that the composition of the capital structure is an irrelevant

factor in the company's market valuation. They have really attacked the traditional position that

companies have the optimal capital structure. In Modigliani and Miller (1958) „The Cost of

Capital, Corporation Finance and the Theory of Investment‟, they have strengthened the net

operating income approach by adding a behavioural dimension to it. They have been awarded the

Nobel Prizes (Franco Modigliani in 1985, and Merton Miller in 1990) for their widely recognised

contributions to financial theory.

In Van Horne (1998), the Modigliani-Miller (MM) position is based on the following

assumptions: (1) The fundamental building blocks for the hypothesis of MM is a perfect capital

market. There is a free flow of information in the market that can easily be accessed by investors.

There are no costs involved in obtaining the information. (2) Securities issued and traded in the

market are infinitely divisible. (3) No transaction costs such as flotation costs, underpricing major

issues, brokers, transfer taxes, etc.. (4) All participants in the market are rational that they are

trying to maximise profits or minimise their losses. (5) All investors have homogeneous

expectations about future earnings of all firms in the market. (6) The company can be classified

into the class `equivalent return '. Firms in each class have the exact same profile of business risk.

So the company can be taken as perfect substitutes for one another. All companies in a particular

class have a common level of capitalisation rate. (7) There is no corporate tax.

Modigliani and Miller (1958) have stated the arbitration process to support their position

that the value of the company with leverage cannot be higher than the value of a company with no

leverage. On the other hand, the value of a company with no leverage cannot be higher than the

value of a company with leverage. The substance of this argument is that investors can replicate

any combination of capital structure by substituting the company leverage with the `home-made '

leverage. Home-made leverage refers to individual loans prepared by investors in the equivalent

ratio as the company with leverage. Therefore, leverage of company is not something that is

distinctive that investors cannot carry out it alone. Therefore, the leverage in the capital structure

37

has no importance in a perfect capital market. It implies that, firms that are identical in all respects,

except for their capital structure, must have the equal value. In the event that they have a different

valuation, the arbitration process will initiate. This will maintain to occur until the two companies

command the same valuations. At this position, the market reaches equilibrium or stability.

A. Taxes and the Capital Structure

The introduction of the tax element brings the complexity theory of capital structure. The

assumption that there is no tax is relaxed to evaluate the validity of the hypothesis. Interests

payable on debt are tax deductible substances, while retained earnings and dividends payable to

equity do not enjoy the fiscal benefits. Therefore, every time the company employs debt in its

capital structure, it gets a certain tax shield (Modigliani and Miller, 1963). Thus, the sum

availables for sharing to the shareholders more in the case of a company with leverage than in a

company with no leverage.

However, utilisation of tax shields by the company is uncertain. A company's taxable

income may fall or the company may experience losses in the future. In such a circumstance,

companies do not have advantage of the tax shield available. Corporate tax rates can be reduced in

the future, which will reach in a lower tax shield. This office can be liquidated, and the tax shield

will not have any realisable value unlike any other asset. Alternatives such as leasing tax shelters,

depreciation, investment allowances, etc., may be presented to the company, and will generate

excessive tax shield (De Angelo and Masulis, 1980). Thus, the uncertainty associated with it can

lead to decline in value of tax shields. The greater the uncertainty, the lower will be the value of

tax shields. The presence of personal taxes can reduce the value of tax shields. This is because

capital gains are normally taxed at a lower rate than regular income. In extreme cases the company

retains all the profits, shareholders had no tax liability. Further, tax on capital gains is paid only if

the security is sold.

B. Merton Miller Hypothesis

Merton Miller (1977) held that the capital structure decision is irrelevant even in the

presence of corporate taxes and personal. Changes in capital structure had no impact on corporate

valuation. This stands significantly different to the article “Corporate Income Taxes and the Cost

of Capital: A Correction” jointly written by Modigliani and Miller (1963) in which they agreed

debts have the advantage of substantial tax benefits. According to him, the influence of corporate

taxes and personal taxes tend to get cancelled and the hypothesis of MM continues to apply even

in the presence of taxes. Miller (1977) indicates that different investors have different rates of

personal income tax. The tax-exempt investors prefer to invest in debt, while investors in tax

brackets higher preferred equity investments. Miller (1977) argues that when the market is in a

state of imbalance, the company will change their capital structure to confirm with the incidence of

tax on investors.

As companies increases the quantum of debt in their capital structure, debt supply in the

market increases. This will deplete the capacity of 'clients' tax-free (investors) to absorb the debt.

These companies would then sell their debt to investors in the next tax bracket. This process is

continued to the stage where the company covers the investor classification in the same tax bracket

income tax rates. Markets are required to be equilibrium when the personal tax rate investors are

the same as the corporate income tax rate, at which point it is no longer potential for companies to

improve the evaluation by changing the capital structure.

3.1.2. The Capital Structure Theory

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The “irrelevance capital structure” theory by Modigliani and Miller (1958) was a

milestone from which several related theories developed by relaxing the assumptions made by the

study and adding new conditions of, among others, asymmetric information and agency costs

(Leland and Pyle, 1977 ; Myers, 1984 ; La Porta, et al. , 1996, 1997). Thus, by relaxing the

assumptions of Modigliani and Miller (1958), capital structure is relevant to firm value.

A. Pecking Order Theory

According to this hypothesis, the company follows a specific order of preferences in

financing decisions (Myers, 1984; Myers and Majluf, 1984). The most popular mode of financing

is retained earnings. The advantage of financing through retained earnings is that it has no related

flotation costs. Additionally, retained earnings do not require external supervision by the provider

of capital. When the internal accruals are not adequate to finance the proposed investment, then

the company resorts to debt financing. The issue of debt does not result in dilution of equity capital

and has no implications on stock ownership. The next way of financing in the hierarchy is the

issuance of preference capital. This was followed by a variety of hybrid instruments like

convertible instruments. The least preferred mode of financing is issue of equity (Donaldson,

1961; Myers, 1984; Myers and Majluf, 1984). This is only reliable as a last option. Pecking order

theory is a behavioural approach to capital structure. This is based on the principle that financing

decisions are made in a way that causes the least difficulty to the management.

B.3.1. Trade-off Theory

The major benefit of debt financing is that it provides a tax shelter that increases the

available remaining to be distributed to shareholders of equity. Nevertheless, the main

disadvantage related with debt financing is the risk of bankruptcy (Warner, 1977; Haugen and

Senbet, 1978, Andrade and Kaplan, 1998). Increased levels of leverage, while resulting in the

availability of a larger tax shields also necessitate a higher cost line of financial distress. The

company is trying to trade-off between the size of the tax shelter and financial distress costs.

Higher probability of financial distress is in terms of start-ups and high growth businesses. The

company is exposed to the risk of uncertain cash flow streams and low tangible asset base.

Therefore, these type of companies should not place high confidence on the debt in their capital

structure. On the other hand, firms with a stable revenue stream and sound asset base facing a

lower risk of bankruptcy. This company can apply a moderately higher level of leverage in their

capital structure.

B.3.2. Bankruptcy Costs and the Capital Structure

Various theories of capital structure is not attended to the existence of bankruptcy costs.

In a perfect capital market, it is assumed that all company assets can be sold on their economic

value without acquiring the costs of liquidation. Nevertheless, in actual situations, such as

liquidation costs, legal fees and administration are significant (Warner, 1977; Haugen and Senbet,

1978, Andrade and Kaplan, 1998). Moreover, assets may be sold at distress prices below their

economic value. Thus, its net realisable value is less than the economic value, which is a 'dead

weight loss' to the system. The lenders will bear the cost of ex post bankruptcy, but they will

continue the ex ante bankruptcy costs for firms in the form of high cost of debt. In the end, the

shareholders bear the problem of ex ante bankruptcy costs and lower valuation due from the

company.

A company with leverage has a larger probability of bankruptcy than firms with no

leverage. Hence, the cost of bankruptcy for firms with high leverage is higher. However, the cost

of bankruptcy is not a linear function of leverage. When a company employs low levels of

39

leverage in capital structure, bankruptcy risk is not considerable. Thus, there is no influence of

bankruptcy cost on corporate valuation, until the threshold is reached. Conversely, after a

threshold level of leverage, bankruptcy becomes an existent threat. The possibility of bankruptcy

significantly increases with further application of leverage. Bankruptcy costs rose at an increased

rate beyond the stage of threshold.

C.3.1. Asymmetric Information

This hypothesis is based on the principle that the manager/person in having personal

information about the characteristics of the flow back in a company or an investment opportunity.

Thus capital structure is intended to reduce inefficiencies caused by asymmetric information.

Stewart Myers and Nicholas Majluf (1984) in a pioneering article„Corporate Financing and

Investment Decisions When Firms have Information That Investors Do not Have‟ argues that, if

the investor is less well-informed than people in the company on company valuation, equity may

be mispriced by the market.

If the company is funding new projects by issuing equity, underpricing may be so strict

that new investors capture more than the net present value (NPV) of the new project, which results

in a net loss to existing shareholders. In this case, the project was rejected even though the NPV is

positive (Myers, 1977). Underinvestment problems can be avoided if the company can finance

investment by issuing securities that would have less or nil undervaluation. For instance, internal

accruals do not have an element of undervaluation and in terms of the debt will be less severe

undervaluation. Consequently, the firm uses equity financing only as a last choice.

C.3.2. Signalling through Capital Structure

Some theories suggest that changes in capital structure have information content about

the valuation of the firm. These theories give explanations that capital structure changes are

explicit signals about the firm‟s valuation, sent purposely by the management (Ross, 1977). An

increase in the debt composition of the capital structure is commonly indicated as a signal of

undervaluation of the firm. As the increased level of leverage is accompanied by a higher risk of

bankruptcy, the increased level of debt implies the confidence of the management in the future

prospects of the firm. Hence, it brings greater conviction than a simple announcement of

undervalution of the firm by the management (Leland and Pyle, 1977; Myers and Majluf, 1984).

On the other hand, an issue of equity is an indication that the firm is overvalued. The market

interpretes that the management has decided to issue equity because it is valued higher than its

intrinsic valued by the market. The markets normally respond favourably to moderate increases in

leverage and negatively to new issue of equity (Ross, 1977).

C.3.3. Agency Costs and the Capital Structure

A significant amount of research during the last two decades has been dedicated to

models in which capital structure is determined by agency costs, costs due to conflict of interest

(Harris and Raviv, 1991). Firstly, conflicts of interest between shareholders and managers begin

because managers are not allowed to 100% of the residual claims. Consequently the managers do

not capture the entire gain from the profit enhancement activities, but they do accept the entire

costs of these activities. The managers may hence put in less efforts in value enhancement

activities and may also undertake to maximise their private gains by lavish perquisites, plush

offices, „empire building‟ through sub-optimal investments, etc (Jensen, 1986). While the

managers would have the entire costs of refraining from such inefficiencies, they are entitled to

only a portion of the gains. The increase in the manager‟s stake in the firm decreases these

inefficiencies.

40

Secondly, conflicts also come up between the interests of debt holders and equity holders

(Jensen and Meckling, 1976). If an investment financed with debt yields high returns (higher than

the cost of debt), equity holders are allowed to the gains. On the other hand, if the investment fails,

the debt holders experience the losses due to limited liability of the equity holders. As a

consequence, equity holders may gain from investing in very risky projects even if they are value

decreasing. Such investments result in a decline in the value of debt. The loss in the value of

equity from regrettable investments can be more than compensated by the gains in equity value at

the cost of the lenders. The lenders to the firm protect themselves against expropriation by

impressive certain conditions on the firm. These circumstances are called as protective covenants

and stay in strong point till the debt is repaid. These conditions may relate to limitations on further

borrowings by the firm, cap on payment of dividends, managerial payment, sale of assets,

limitations on new investment, etc. These conditions may guide to sub-optimal operations

resulting in inefficiencies. Additionally, the lenders put in place tough monitoring and corrective

mechanisms to implement the debt covenants. The monitoring and enforcement costs are approved

on to the firms in the kind of higher cost of debt.

These expenses together with the cost of inefficiencies (due to the covenants) are called

agency costs (Jensen and Meckling, 1976). As residual owners, the shareholders have an incentive

to make sure that agency costs are minimised. The existence of agency costs work as a

disincentive to the issuance of debt. The agency cost may be practically non-existent at low levels

of leverage. Nevertheless, after the entry point, the lenders initiate perceiving the firm to be

increasingly risky. This may result in an unequal increase in the agency costs due to the necessitate

for widespread monitoring.

3.2. The Conclusions What Variables We Use for Our Research, and Why These, Theories

Predictions of the Relationship between Variables, and Some Previous Research Findings

The following sub-sections imply the conclusions what variables we use for our research

and the reasons, theories predictions of the relationship between variables, and some previous

research findings.

3.2.1 Selected Variables regarding Capital Structure for Research Question 1a, 1b, 1c, 1d,

and 1e

After reviewing the pecking order theory and trade-off theory, we test the theories by

using selected variables. As our research question that stated in chapter 1, what are the

determinants of capital structure of the firms in the manufacturing sector in Indonesia? Hence, our

minor research questions are as follows: as implied by the trade-off theory and the pecking order

theory, do growth opportunities have positive relationship with debt ratio?; As the pecking order

hypothesis, does firm‟s profitability has a negative relationship with level of debt? And as implied

by the trade-off theory, does firm‟s profitability has a positive relationship with debt ratio?; In

accordance with the pecking order theory and trade-off theory, is there a negative relationship

between risk (earnings volatility) and debt ratio?; As suggested by the trade-off theory, does size

has a positive relationship with debt ratio? And as suggested by the pecking order theory of the

capital structure, is there a negative relationship between level of debt and size of the firm?; In

accordance with the trade-off theory, is there a positive relationship between asset tangibility and

level of debt?

Therefore, the relevant variables we used are: debt ratios as the dependent variable, and

the growth opportunities, profitability, risk, size, asset tangibility as the independent variables. The

selection of independent variables is also conducted by previous empirical studies such as Pandey

(2001), Sogorb-Mira and López-Gracia (2003), and Huang and Song (2002).

41

The test of determinants of capital structure of the firms in the manufacturing sector in

Indonesia is important as these firms have different characteristics. We test it based on pecking

order theory and trade-off theory. We choose four debt ratios in this study, they are total leverage,

short-term leverage, long-term leverage, and market leverage. These measures of debt ratios

examine the capital employed and thus, best represent the effects of past financing decisions.

We chose tangibility of assets, as the tangibility of assets represents the effect of the

collateral value of assets of the firm‟s gearing level. There are various conceptions for the effect of

tangibility on leverage decisions. If debt can be secured against assets, the borrower is restricted to

using debt funds for specific projects. Creditors have an improved guarantee of repayment, but

without collateralised assets, such a guarantee does not exist.



debt markets or the extent their assets are diversified).

For growth opportunities, the trade-off theory suggests that firms with more investment

opportunities have less leverage because they have stronger incentives to avoid under-investment

and asset substitution that can arise from stockholder-bondholder agency conflicts. Jensen‟s (1986)

free cash flow theory similarly discusses that firms with more investment opportunities have less

need for the disciplining effect of debt payments to control free cash flows.

Meanwhile, profitability plays an important role in leverage decisions. Profitability is

proxied by return on assets. ROA represents the contribution of the firm‟s assets on profitability

creation. Profitability is a measure of earning power of a firm. The earning power of a firm is

generally the basic concern of its shareholders.

Finally, we choose earnings volatility as it measures the variability of the firm's cash

flows as a proxy for the costs of monitoring managers and of the risk of an insider's position. The

use of longer time periods causes a significant loss of the sample size.

The following is the theories prediction of the relationship between variables and some

previous research findings.

Growth Opportunities

According to pecking order theory hypothesis, a firm will use first internally generated

funds which may not be sufficient for a growing firm. And the next option for the growing firms is

to use debt financing which implies that a growing firm will have a high leverage (Drobetz and Fix

2003). Applying pecking order arguments, growing firms place a greater demand on the internally

generated funds of the firm. Consequently, firms with relatively high growth will tend to issue less

security subject to information asymmetries, i.e. short-term debt. This should lead to firms with

relatively higher growth having more leverage.

The same relationship is supported by trade-off theory, too. According to this theory,

growth causes firms to shift financing from new equity to debt, as they need more funds to reduce

the agency problem. Following trade-off theory, for companies with growth opportunities, the use

of debt is limited as in the case of bankruptcy, the value of growth opportunities will be close to

zero, growth opportunities are a particular case of intangible assets (Myers, 1984; Williamson,

1988 and Harris and Raviv, 1990). Firms with less growth prospects should use debt because it has

a disciplinary role (Jensen, 1986; Stulz, 1990). Firms with growth opportunities may invest sub-

optimally, and therefore creditors will be more reluctant to lend for long horizons. This problem

42

can be solved by short-term financing (Titman and Wessels, 1988) or by convertible bonds (Jensen

and Meckling, 1976; Smith and Warner, 1979). According to trade-off theory, the retained

earnings of high growth firms increase and they issue more debt to maintain the target debt ratio.

Thus, positive relationship between debt ratio and growth is expected based on this argument.

The signalling hypothesis is based on the impact of information asymmetries on debt

policies. Firms with higher growth opportunities face greater information disparities and therefore

are expected to have higher debt levels to signal higher quality (Gul, 1999).

According to agency costs, on the other hand, Myers (1977) argued that, due to agency

problems, firms investing in assets that might generate high growth opportunities in the future,

faced difficulties in borrowing against such assets. For this reason, we may now instead expect a

negative relationship between growth and leverage.

Previous research findings have different conclusion. For example, Huang and Song

(2002) argued that sales growth rate was the past growth experience, while Tobin‟s Q better

proxied future growth opportunities, although sales growth rate as well as Tobin‟s Q (market-to-

book ratio of total assets) were employed to measure growth opportunities in this study.

Jung, Kim and Stulz (1996) showed, if management pursued growth objectives,

management and shareholder interests tended to coincide for firms with strong investment

opportunities. But for firms lacking investment opportunities, debt served to limit the agency costs

of managerial discretion as suggested by Jensen (1986) and Stulz (1990). The findings of Berger,

Ofek, and Yermack (1997) also confirmed the disciplinary role of debt.

The findings of Kim and Sorensen (1986), Smith and Watts (1992), Wald (1999), Rajan

and Zingales (1955), and Booth et al. (2001) suggested growth opportunities were negatively

related with leverage. Titman and Wessels (1988) found a negative relationship.

Myers (1977) argued that high-growth firms might hold more real options for future

investment than low-growth firms. If high-growth firms need extra equity financing to exercise

such options in the future, a firm with outstanding debt may forgo this opportunity because such

an investment effectively transfers wealth from stockholders to debt holders. So firms with high

growth opportunity may not issue debt in the first place and leverage is expected to be negatively

related with growth opportunities. Jensen and Meckling (1976) also suggested that leverage

increased with lack of growth opportunities.

However, Kester (1986), Rajan and Zingales (1995) reported a positive relationship

between leverage and growth. Huang and Song found that firms experienced a high growth rate in

the past tend to have higher leverage, while firms that had a good growth opportunity in the future

(a higher Tobin‟s Q) tend to have lower leverage.

Profitability

The pecking order theory, based on works by Myers and Majluf (1984) suggests that

firms have a pecking-order in the choice of financing their activities. Roughly, this theory states

that firms prefer internal funds rather than external funds. If external finance is required, the first

choice is to issue debt, then possibly with hybrid securities such as convertible bonds, then

eventually equity as a last resort (Brealey and Myers, 1991). This behaviour may be due to the

costs of issuing new equity, as a result of asymmetric information or transaction costs.

43

All things being equal, the more profitable the firms are, the more internal financing they

will have, and therefore we should expect a negative relationship between leverage and

profitability. This relationship is one of the most systematic findings in the empirical literature

(Harris and Raviv, 1991; Rajan and Zingales, 1995; Booth et al. , 2001). There are conflicting

theoretical predictions on the effects of profitability on leverage (Rajan and Zingales, 1995); while

Myers and Majluf (1984) predicted a negative relationship according to the pecking order theory,

Jensen (1986) predicted a positive relationship. Following the pecking order theory, profitable

firms, which have access to retained profits, can use these for firm financing rather than accessing

outside sources.

In the pecking order model, higher earnings should result in less book leverage. Firms

prefer raising capital, first from retained earnings, second from debt, and third from issuing new

equity. This behaviour is due to the costs associated with new equity issues in the presence of

information asymmetries. Debt typically grows when investment exceeds retained earnings and

fall when investment is less than retained earnings. Accordingly, the pecking order model predicts

a negative relationship between book leverage and profitability. The pecking order theory predicts

that firms with a lot of profits and few investments have little debt. Since the market value

increases with profitability, the negative relationship between book leverage and profitability also

holds for market leverage.

However, in a trade-off theory framework, an opposite conclusion is expected. When

firms are profitable, they should prefer debt to benefit from the tax shield. In addition, if past

profitability is a good proxy for future profitability, profitable firms can borrow more as the

likelihood of paying back the loans is greater. From the trade-off theory point of view more

profitable firms are exposed to lower risks of bankruptcy and have greater incentive to employ

debt to exploit interest tax shields.

According to the trade-off theory, agency costs, taxes, and bankruptcy costs push more

profitable firms toward higher book leverage. First, expected bankruptcy costs decline when

profitability increases. Second, the deductability of corporate interest payments induces more

profitable firms to finance with debt. Finally, in the agency models of Jensen and Meckling

(1976), Easterbrook (1984), and Jensen (1986), higher leverage helps to control agency problems

by forcing managers to pay out more of the firm‟s excess cash. The trade-off theory predicts that

leverage increases with profitability. Since the market value also increases with profitability, this

positive relation does not necessarily apply for market leverage.

The strong commitment to pay out a larger fraction of their pre-interest earnings to debt

payments suggests a positive relationship between book leverage and profitability. This notion is

also consistent with the signalling hypothesis by Ross (1977), where higher levels of debt can be

used by managers to signal an optimistic future for the firm. Meanwhile, based on agency theory,

there are two possible explanations. Jensen (1986) predicted a positive relationship, if the market

for corporate control was effective. However, if it was ineffective, He predicted a negative

relationship between profitability and leverage, and a positive relationship between profitability

and financial leverage if the market for corporate control was effective because debt reduced the

free cash flow generated by profitability.

Much theoretical work has been done since Modigliani and Miller (1958), no consistent

predictions have been reached of the relationship between profitability and leverage. Myers (1977)

stated that firms preferred raising capital from retained earnings rather than from debt or from

issuing equity. This is the so-called “pecking order theory”. If pecking order holds true, then,

higher profitability will correspond to lower debt-equity ratio. Myers (1984) pecking order theory

of capital structure showed that if a firm was profitable then it would be more likely that financing

44

came from internal sources rather than external sources. More profitable firms were expected to

hold less debt, since it was easier and more cost effective to finance internally.

In contrast to theoretical studies, most empirical studies showed that leverage was

negatively related to profitability. Friend and Lang (1988), and Titman and Wessels (1988)

obtained such findings from US firms. Kester (1986) found that leverage was negatively related to

profitability in both the US and Japan. More recent studies using international data also confirmed

this finding (Rajan and Zingales (1995), and Wald (1999) for developed countries,

Wiwattanakantang (1999) and Booth et al. (2001) for developing countries. Long and Maltiz

(1985) found leverage to be positively related to profitability, but the relationship was not

statistically significant. Wald (1999) even claimed that profitability has the largest single effect on

debt/asset ratios. Huang and Song (2002) found that profitability was strongly negatively related

with total leverage.

Chang (1999) showed that the optimal contract between the corporate insider and outside

investors could be interpreted as a combination of debt and equity, and profitable firms tended to

use less debt. Meanwhile, Jensen, Solberg and Zorn (1992) found a positive one (supporting the

trade-off theory).

Risk

According to pecking order theory and tradeoff theory, earning volatility is considered to

be either the inherent business risk in the operations of a firm or a result of inefficient management

practices. In either case earning volatility is proxy for the probability of financial distress and the

firm will have to pay risk premium to outside fund providers. To reduce the cost of capital, a firm

will first use internally generated funds and then outsider funds. This suggests that earning

volatility is negatively related with leverage. This is the combined prediction of trade-off theory

and pecking order theory.

According to pecking order theory and tradeoff theory, income variability is a measure of

business risk. Since higher variability in earnings indicates that the probability of bankruptcy

increases, we can expect that firms with higher income variability have lower leverage. Therefore,

the trade-off model allows the same prediction, but the reasoning is slightly different. More

volatile cash flows increase the probability of default, implying a negative relationship between

leverage and volatility of cash flows. As expected, the relationship between leverage and volatility

is negative. This supports both the trade-off theory (more volatile cash flows increase the

probability of default) and the pecking order theory (issuing equity is more costly for firms with

volatile cash flows).

Cools (1993) said that agency theory suggested positive relationship between earning

volatility and leverage. He said that the problem of underinvestment decreased when the volatility

of firm returns increased. Booth et al. , (2001), Bradley et. al., (1984), Chaplinsky and Niehaus,

(1993), Wald, (1999), and Titman and Wessels (1988), all these studies found that business risk

was negatively correlated with leverage. Huang and Song (2002) found that the positive relation

between total liabilities ratio and volatility was consistent with Hsia‟s (1981) view that firms with

higher leverage level tended to make riskier investment.

Size

According to tradeoff theory, first, large firms don‟t consider the direct bankruptcy costs

as an active variable in deciding the level of leverage as these costs are fixed by constitution and

constitute a smaller proportion of the total firm‟s value. And also, larger firms being more

diversified have lesser chances of bankruptcy (Titman and Wessels 1988). Following this, one

45

may expect a positive relationship between size and leverage of a firm. The trade-off theory

predicts an inverse relationship between size and the probability of bankruptcy. Hence, there is a

positive relationship between size and leverage. Second, contrary to first view, Rajan and Zingales

(1995) argued that there was less asymmetrical information about the larger firms. This reduced

the chances of undervaluation of the new equity issue and thus encouraged the large firms to use

equity financing. This means that there is negative relationship between size and leverage of a

firm. Following Rajan and Zingales (1995), we expect a negative relationship between size and

leverage of the firm. Therefore, the pecking order theory of the capital structure predicts a negative

relationship between leverage and size, as larger firms exhibiting increasing preference for equity

relative to debt.

Meanwhile, previous research also has different results. Titman and Wessels (1988) and

Drobetz and Fix (2003) measure of size was the natural logarithm of net sales. However, they

stated that net sales was a better proxy for size, because many firms attempted to keep their

reported size of asset as small as possible, e.g., by using lease contracts.

Size can be regarded as a proxy for information asymmetry between firm insiders and the

capital markets. Large firms are more closely observed by analysts and should therefore be more

capable of issuing informationally more sensitive equity, and have lower debt.

Akhtar and Oliver (2006) found that more profitable firms had significantly less leverage

regardless of whether they were MNCs or DCs. This supports the pecking-order theory of capital

structure for both MNCs and DCs. Rajan and Zingales (1995) and Wald (1999) found that larger

firms in Germany tended to have less debt.

Meanwhile, many studies suggest there is a positive relation between leverage and size.

Drobetz and Fix (2003) said that size was positively related to leverage, indicating that size was a

proxy for a low probability of default. Empirical studies, such as Marsh (1982), Rajan and

Zingales (1995), Wald (1999), and Booth et al. (2001), generally found that leverage was

positively correlated with company size. Huang and Song found that size was positively related

with total liability.

Marsh (1982) found that large firms more often chosen long-term debt while small firms

chosen short-term debt. Large firms may be able to take advantage of economies of scale in

issuing long-term debt, and may even have bargaining power over creditors. So the cost of issuing

debt and equity is negatively related to firm size. However, size may also be a proxy for the

information that outside investors have. Fama and Jensen (1983) argued that larger firms tended to

provide more information to lenders than smaller ones. Rajan and Zingales (1995) argued that

larger firms tended to disclose more information to outside investors than smaller ones. Overall,

larger firms with less asymmetric information problems should tend to have more equity than debt

and thus have lower leverage. However, larger firms are often more diversified and have more

stable cash flow; the probability of bankruptcy for large firms is smaller compared with smaller

ones, ceteris paribus. Both arguments suggest size should be positively related with leverage.

According to Whited (1992) small firms could not access long-term debt markets since

their growth opportunities exceeded their collateralizable assets. Titman and Wessels (1988)

argued that larger firms had easier access to capital markets.

Tangibility

From a pecking order theory perspective, firms with few tangible assets are more

sensitive to informational asymmetries. These firms will thus issue debt rather than equity when

46

they need external financing (Harris and Raviv, 1991), leading to an expected negative relation

between the importance of intangible assets and leverage.

According to trade-off hypothesis, tangible assets act as collateral and provide security to

lenders in the event of financial distress. Hence, the tradeoff theory predicts a positive relationship

between measures of leverage and the proportion of tangible assets. On the relationship between

tangibility and capital structure, theories generally state that tangibility is positively related to

leverage.

Tangibility is almost always positively correlated with leverage. This supports the

prediction of the trade-off theory that the debt-capacity increases with the proportion of tangible

assets on the balance sheet.

Based on the agency problems between managers and shareholders, Harris and Raviv

(1990) suggested that firms with more tangible assets should take more debt. This is due to the

behaviour of managers who refuse to liquidate the firm even when the liquidation value is higher

than the value of the firm as a going concern. Indeed, by increasing the leverage, the probability of

default will increase which is to the benefit of the shareholders. In an agency theory framework,

debt can have another disciplinary role: by increasing the debt level, the free cash flow will

decrease (Grossman and Hart, 1982; Jensen, 1986; Stulz, 1990). As opposed to the former, this

disciplinary role of debt should mainly occur in firms with few tangible assets, because in such a

case it is very difficult to monitor the excessive expenses of managers.

Harris and Raviv (1990) predicted that firm with higher liquidation value would have

more debt. Firms with more tangible assets usually have a higher liquidation value although we are

aware that assets specificity may play a role and result in some distortion. In general, firms with a

higher proportion of tangible assets are more likely to be in a mature industry thus less risky,

which affords higher financial leverage.

In Drobetz and Fix (2003), previous empirical studies by Titman and Wessels (1988),

Rajan and Zingales (1995) and Fama and French (2000) argued that the ratio of fixed to total

assets (tangibility) should be an important factor for leverage. The tangibility of assets represents

the effect of the collateral value of assets of the firm‟s gearing level.

Huang and Song (2002) found that debt ratio was positively correlated with tangibility,

the change of total liabilities ratio was significantly positively correlated with the change of

tangibility. Empirical studies that confirm the above theoretical prediction include Marsh (1982),

Long and Malitz (1985), Friend and Lang (1988), Rajan and Zingales (1995), and Wald (1999). As

the non-debt portion of liabilities does not need collateral, tangibility is expected to affect the long-

term debt or total debt ratio rather than total liabilities ratio.

3.2.2 Selected Variables for Research Question 2

Accordingly, after reviewing the pecking order theory, we test the second research

question: how do firms in the manufacturing sector in Indonesia raise capital for investments,

internally or externally (with debt, equity, or debt to repurchase equity).

Hence, the relevant variables we used are as follow: financial deficit as independent

variable and net debt issue, net equity issue, and net debt issue to repurchase equity as dependent

variables.

47

Why do we test hypothesis 2 in this research is that, how do firms in the manufacturing

sector in LQ45 index financing the firms‟ deficit as these firms are experiencing financial deficit

over the period of time (see table).

We chose net debt issue, net equity issue, and net debt issue to repurchase equity as

dependent variables as pecking order theory suggests firms to prefer internal financing to external

financing, and prefer debt to equity.


previous research findings. The theories prediction is as follows. Based on asymmetric

information, the underinvestment problem can be avoided if the firm can finance the investment

by issuing securities that will have lesser or nil undervaluation. For example, internal accruals do

not have any element of undervaluation and in case of debt the undervaluation will be less severe.

Therefore, firms use equity financing only as a last resort.

Pecking order theory states that changes in debt have played an important role in

assessing the pecking order theory. This is because the financing deficit is supposed to drive debt

according to this theory.

Shyam-Sunder and Myers (1994, 1999) paper tested traditional capital structure models

against the alternative of a pecking order model of corporate financing. The basic pecking order

model, which predicts external debt financing driven by the internal financial deficit, has much

greater explanatory power than a static trade-off model which predicts that each firm adjusts

toward an optimal debt ratio.

Shyam-Sunder and Myers (1994) summarized main conclusions regarding pot as follows.

(1) The pecking order is an effective first-order descriptor of corporate financing behaviour. (2)

The co-efficient and significance of the pecking order variable change hardly at all. (3) The strong

performance of the pecking order does not occur just because firms fund unanticipated cash needs

with debt in the short run.

Shyam-Sunder and Myers (1999) summarized the main conclusions regarding pot as

follows. (1) The pecking order is an excellent first-order descriptor of corporate financing

behaviour, for their sample of mature corporations. (2) The strong performance of the pecking

order does not occur just because firms fund unanticipated cash needs with debt in the short run.

Their (1994, 1999) results suggested that firms planned to finance anticipated deficits with debt.

Previous research from Indonesia, Ari Christianti (2008), concluded that: (1) The results

of this study does not fully support the pecking order theory in explaining the behaviour of firm

financing in the IDX especially the manufacturing sector. This can be explained from the results of

the estimation that shows a negative and significant co-efficient of pecking order theory. (2) It

may be explained from the results of this study is the Indonesian capital market conditions that are

different from capital markets in developed countries studied by Shyam-Sunder and Myers (1999),

Frank and Goyal (2003) and Jong, Verbeek, and Verwijmeren (2005). In addition, the impact of

economic crisis in 1997 still affected the economic condition of Indonesia until 2005.

Cotei and Farhat (2008) investigated the models used in testing the trade-off and pecking

order theories at the industry level as well as across all industries. Under the pecking order model,

firms in financing deficit used debt to finance their new investment whereas firms in financing

surplus ended up retiring debt rather than repurchasing equity. Hence, their results showed that for

the pecking order model, they rejected the hypothesis that firms had a symmetric behaviour

regardless of the sign of the financing variable. Their results showed that firms had the tendency to

48

reduce debt by a significantly higher proportion when they had financing surplus compared to the

proportion of debt issued when they had financing deficit.

Joher, Ahmed, and Hisham (2009) paper draw on studies from finance and accounting

literature to revisit pecking order and static trade-off-hypothesis in the context of Malaysia capital

market. Their evidence from pecking order model suggested that the internal fund deficiency was

the most important determinant that possibly explained the issuance of new debt. Hence pecking

order hypothesis is well explained in Malaysian capital market despite the lower predicting power.

The expanded pecking order model provides more vibrant explanation for debt issuance with

higher predictive power. Meanwhile, their result for static trade-off-model was not fit to explain

the issuance of new debt issue in Malaysian capital market. This is an interesting findings that

confirm the fact that Malaysian firms do not too much care about tax-shield benefit derive from

employ both debt and non-debt tax-shield.

3.2.3 Selected Variables for Research Question 3a, 3b, and 3c

After reviewing pecking order theory, trade-off theory, signalling theory, and asymmetric

information, we test the third research questions: if debt is a policy matter, what will happen to the

firm‟s stock price if firms issue new debt, new equity, or issue debt to repurchase equity.

Therefore, the relevant variables we examined are the firm‟s stock price as dependent variable, and

as independent variables are net debt issue, net equity issue, and net debt issue to repurchase

equity.

The test of hypothesis 3 is important to conduct as empirical evidence on the effect of

capital structure choice on stock market reaction is limited. Hence, we examine the relationship

between capital structure and stock price based on pecking order theory, trade-off theory,

signalling theory, and asymmetric information. When a firm issues, repurchases or exchanges one

security for another, it changes its capital structure and will give influence on stock market

reaction.


previous research findings. Based on signalling through capital structure, the increased level of

leverage is accompanied by a higher risk of bankruptcy, the increased level of debt indicates the

confidence of the management in the future prospects of the firm. Hence, it carries greater

conviction than a mere announcement of undervaluation of the firm, by the management. On the

other hand, an issue of equity is a signal that the firm is overvalued. The market concludes that the

management has decided to offer equity because it is valued higher than its intrinsic worth by the

market. The markets normally react favourably to moderate increases in leverage and negatively to

fresh issue of equity.

Under the trade-off theory, firms will only take actions if they expect benefits. An


positive. The market response to a leverage change confounds two pieces of information: the

revelation of the fact that the firm‟s conditions have changed, necessitating financing, and the

effect of the financing on security valuations. The information included in security issuance

decisions could be either good news or bad news. It is good news if the firm issues securities to

take advantage of a promising new opportunity that has not previously anticipated. It might be bad

news if the firm issues securities because the firm actually needs more resources than anticipated

to conduct operations. A firm may also issues securities now in anticipation of a change in future

needs. This implies that the trade-off theory by itself places no obvious restrictions on the market

valuation effects of issuing decisions. Everything depends on the setting.

49

Jung et al. (1996) suggested an agency perspective and argued that equity issues by firms

with poor growth prospects reflected agency problems between managers and shareholders. If this

is the case, then stock prices will react negatively to the news of equity issues.


adverse selection (equity) will result in more negative market reaction. Securities with less adverse

selection (debt) will result in less negative or no market reaction. This is of course, still rest on

some assumptions about market anticipations.

Meanwhile, literature offers multiple explanations for buybacks. Some of these

explanations have theoretical backgrounds and some are formed from empirical studies. The

following theory is explaining our hypotheses. Based on the undervaluation hypothesis, stock

repurchases offer flexibility not only for distributing the excess of funds but also the timing of

distributing these funds. This flexibility in timing is beneficial because firms can wait to

repurchase until the stock price is undervalued. The undervaluation hypothesis is based on the

premise that information asymmetry between insiders and shareholders may cause a firm to be

misvalued. If insiders believe that the stock is undervalued, the firm may repurchase stock as a

signal to the market or to invest in its own stock and acquire mispriced shares. According to this

hypothesis, the market interprets the action as an indication that the stock is undervalued (Amy K.

Dittmar (1999).

Because of the asymmetric information between managers and shareholders, share

repurchase announcements are considered to reveal private information that managers have about

the value of the company (in Smura).





the market‟s expectation about future profitability (in Gustavo Grullon and Roni Michaely, 2002).

Some previous empirical evidence regarding to debt issue on stock price are the

following. Announcements of ordinary debt issues generate zero market reaction on average (see

Eckbo (1986) and Antweiler and Frank (2006)). The zero market reaction to corporate debt issues

is robust to various attempts to control for partial anticipation.

Ross (1977) showed that good corporate performance could give a signal with a high

portion of debt in their capital structure. Ross (1977) assumed the firms that are less well

performaning would not use debt in large portion as it would be followed by the high chance of

bankruptcy. By using these assumptions in which the company will use the good performance of

higher debt, while firms that are less well performaning will use more of equity. Ross (1977)

assumed that investors would be able to distinguish the company's performance by looking at the

company's capital structure and they would give a higher value on the company with larger debt

portion. It indicated that the result did not support the stated signalling theory. The result indicated

that the greater the leverage, the greater the possibility of financial distress leading to bankruptcy.

When the company went bankrupt, shareholders would lose money they have invested in the

company (Peirson et al, 2002).

Exchange of common for debt/preferred stock generates positive stock price reactions

while exchange of debt/preferred for common stock generates negative reactions (Masulis, 1980a).

50

Summarizing the event study evidence, Eckbo and Masulis (1995) concluded that announcements

of security issues typically generated a non-positive stock price reaction.

In Indonesia, the regression coefficient between leverage and stock price is significantly

negative. The use of high leverage will be responded by the market with a fall in stock prices. The

results are consistent with the findings of a negative relationship between leverage and stock price

as proposed by Frank and Goyal (2003). Relationship between the two variables will be positive at

the time the company has many tangible assets that will secure leverage of companies.

Announcements of convertible debt issues result in mildly negative stock price reactions

(Dann and Mikkelson, 1984 ; and Mikkelson and Partch, 1986). The valuation effects are the most

negative for common stock issues, slightly less negative for convertible debt issues and least

negative (zero) for straight debt issues. The effects are more negative the larger the issue.

Some previous empirical evidence regarding the equity issue on stock price are the

following. Announcements of equity issues result in significant negative stock price reactions

(Asquith and Mullins Jr., 1986; Masulis and Korwar, 1986; and Antweiler and Frank, 2006). The

negative market reaction to equity issues and zero market reaction to debt issues is consistent with

adverse selection arguments. Indeed, there are other interpretations. Jung et al. (1996) showed that

firms without valuable investment opportunities experienced a more negative stock price reaction

to equity issues than did firms with better investment opportunities. Thus, agency cost arguments

could also explain the existing evidence on security issues. Further support for the agency view

came from the finding that firms without valuable investment opportunities issuing equity invest

more than similar firms issuing debt and that firms with low managerial ownership have worse

stock price reaction to new equity issue announcements than firms with high managerial

ownership do.

The impact of equity issues appears to differ between countries. Several studies find

positive market reaction to equity issues around the world (Eckbo et al. , 2007) for a summary). To

understand this evidence, Eckbo and Masulis (1992) and more recently Eckbo and Norli (2004)

examine stock price reactions to equity issues conditional on a firm‟s choice of flotation method.

Firms can issue equity using uninsured rights, standby rights, firm commitment underwriting and

private placements. The stock price reactions to equity issues depend on the floatation method. For

U.S. firms Eckbo and Masulis (1992) found that the average announcement-period abnormal

returns were insignificant for uninsured rights offerings and they were significantly negative for

firm-commitment underwritten offerings. Eckbo and Norli (2004) studied equity issuances on the

Oslo Stock Exchange. They found that uninsured rights offerings and private placements resulted

in positive stock price reactions while standby rights offerings generated negative market

reactions. These papers interpreted the effect of the flotation method as reflecting different degrees

of adverse selection problems.

Some previous empirical evidence regarding the stock repurchases on stock price are as

follows. Many studies show that repurchases are associated with a positive stock price reaction.

Vermaelen (1981), Dann (1981), and Comment and Jarell (1991) found the positive stock price

reaction at the announcement of a stock repurchase program should correct the misevaluation.

Ikenberry, Lakonishok and Vermaelen (1995) showed that this increase might not be

sufficient to correct the price since repurchasing firms, particularly low market to book firms,

earned a positive abnormal return during the four years subsequent to the announcement. The

amount of information available and the accuracy of the valuation of firms by the market could

affect firms‟ repurchase decisions.

51

According to Jensen (1986), firms repurchased stock to distribute excess cash flow.

Stephens and Weisbach (1998) supported this hypothesis, as they found a positive relation

between repurchases and levels of cash flow. Stephens and Weisbach also showed that repurchase

activity was negatively correlated with prior stock returns, indicating that firms repurchased stock

when their stock prices were perceived as undervalued. This result agrees with Vermaelen‟s

(1981) findings that firms repurchase stock to signal undervaluation. Thus, firms repurchase stock

when they are undervalued and have the excess cash to distribute.

Masulis (1980b), Dann (1981), and Antweiler and Frank (2006) also found that the

announcement effects were positive when common stock is repurchased. According to Brav et al.

(2005.b.) discovered on their survey that only 22.5 percent of executives believed that reducing

repurchases had negative consequences. On the other hand, almost 90 percent thought that

reducing dividends had negative consequences.

3.2.4 Selected Variables for Research Question 4

Accordingly, after reviewing the pecking order theory, we test the theories by raising the

following research question: in the context of firm‟s life cycle, do younger and growth firms

follow the pecking order more closely. The objective of testing hypothesis 4 is to examine, in the

context of firm‟s life cycle, whether younger and growth firms follow the pecking order more

closely as implied by the pecking order theory of financing proposed by Myers (1984) and Myers

and Maljuf (1984).

It is important to examine the firm‟s capital structure over the life cycle of the firm in

solving the problem of the firm‟s financing deficit. Firms in different life cycle stages have

different characteristics especially regarding information asymmetry. Mature firms have less

information asymmetry whereas growth firms have more information asymmetry. Firms with less

information asymmetry are suggested to choose equity, while firms with more information

asymmetry are suggested to retain earning as their capital structure.

Therefore, the relevant variables are newly retained earnings, net debt issued, and net

equity issued as dependent variables, and for the independent variable is financing deficit. We test

hypothesis 4 to examine which firm‟s life cycle follow the pecking order more closely. It is the

most interesting part of this research as firm life cycle has different capital structure choices as

implied by pecking order theory.


previous research findings. As implied by the pecking order theory of financing of Myers (1984)

and Myers and Maljuf (1984), the theory was based on asymmetric information between investors

and firm managers. Due to the valuation discount that less-informed investors apply to newly

issued securities, firms resort to internal funds first, then debt and equity last to satisfy their

financing needs. In the context of a firm‟s life cycle, we expect that asymmetric information

problems are more severe among young, growth firms compared to firms that have reached

maturity. Older and more mature firms are more closely followed by analysts and are better known

to investors, and should suffer less from problems of information asymmetry. Hence, the theory

predicts that younger, fast-growing firms should be following the pecking order more closely.

The theory‟s prediction that firms with the greatest information asymmetry problems

(specifically young growth firms) are especially those that should be making financing choices

based on the pecking order.

52

The trade-off theory stated that debt created a tax shield advantage through interest

payments (DeAngelo and Masulis, 1980), which was balanced by the cost of bankruptcy (Baxter,

1967; Stiglitz, 1972; Kraus and Litzenberger, 1973; and Kim, 1978) to reach the optimal capital

structure. According to the theory, the retained earnings of high growth firms increased and they

issued more debt to maintain the target debt ratio. Thus, positive relationship between debt ratio

and growth was expected based on this argument.

However, according to the agency theory of Jensen and Meckling's (1976) and Jensen's

(1986), the issuance of debt by low growth firms provides a device for monitoring and controlling

managers by determining the market reaction to debt issuance by firm's with different growth

rates. Therefore, following JM's and Jensen's arguments, low growth firms should increase debt

levels in their capital structure.

Many previous research of capital structure of the firms have been studied over life cycle

stages in the context of the pecking order theory, trade-off theory (presence taxes and bankruptcy

costs), and agency cost theory.

The empirical evidence for the pecking order theory has been mixed. Shyam-Sunder and

Myers (1999) proposed a direct test of the pecking order and found strong support for the theory

among a sample of large firms. Myers (1977) argued that firms with high growth opportunity

might not issue debt in the first place and leverage was expected to be negatively related with

growth opportunities. Frank and Goyal (2003) found that large firms fitted the pecking order

theory better than of small firms.

Bulan and Yan (2007) findings showed that older, more stable and highly profitable firms

with few growth opportunities and good credit histories were more suited to use internal funds

first, and then debt before equity for their financing needs. Overall, they found that the pecking

order theory described the financing patterns of mature firms better than of growth firms. This is

contrary to the theory‟s prediction that firms with the greatest information asymmetry problems

(specifically young, growth firms) are precisely those that should be making financing choices

according to the pecking order. Overall, Bulan and Yan (2007) found that the pecking order theory

described the financing patterns of mature firms better than that of younger growth firms.

Bulan and Yan (2009) examined the central prediction of the pecking order theory of

financing among firms in two distinct life cycle stages, namely growth and maturity. They found

that within a life cycle stage, where levels of debt capacity and external financing needs were more

homogeneous, and after sufficiently controlling for debt capacity constraints, firms with high

adverse selection costs followed the pecking order more closely, consistent with the theory.

Diamond (1989) showed that mature firms had a good reputation so that they were able to

obtain better loan rates compared to their younger firm counterparts. Helwege and Liang (1996)

followed a sample of recent IPO firms and found that these firms‟ decisions to access the external

finance markets as well as their choice of type of external finance was inconsistent with the

pecking order. Petersen and Rajan (1995) presented evidence that older and more mature firms had

access to a lower cost of debt, all else equal. Furthermore, mature firms generally have more

internal funds due to higher profitability and lower growth opportunities. Hence, by nature of their

life cycle stage, they concluded that mature firms were in a better position to following the

pecking order.

Hatfield, Cheng, and Davidson (1994), stated that, one might expect that a high growth

firm could afford to have greater financial leverage because it could generate enough earnings to

53

support the additional interest expense. On the other hand, it may be riskier for a low growth firm

to increase its financial leverage as its earnings may not increase enough to cover the additional

fixed obligations.

The empirical evidence for the agency theory also has been documented from the

research findings of Voz and Forlong (1998), which concluded that, at the IPO stage, the IPO

process performed a similar role to debt in reducing agency costs, and consequently, debt loses

much of its agency advantage. Instead, the tax advantage of debt appears to be extremely

significant in determining an IPO firm optimal debt level. Meanwhile, the mature-listed stage is

associated with an increase in debt levels which appear to be in response to a new ownership

structure. It appears that there is a very strong agency advantage of debt which surpasses the tax

advantage. However, if a firm's growth options are high, this agency advantage appears to be

outweighed by the need to maintain financial slack. Overall, they show the findings that debt has a

significant but minor agency advantage (defined as reducing agency costs of equity) at the IPO

stages and a significant advantage at the mature listed stage.

54

4. CONCEPTUAL FRAMEWORK

4.1 Conceptual Framework for Research Question 1a, 1b, 1c, 1d, and 1e

Conceptual framework is a schematic research model to help researchers answering the

research problems based on theory and relevance previous research. We formulate our conceptual

framework for hypotheses 1, 2, 3, and 4 as follows:

4.1.1 Previous Research regarding Capital Structure Determinants

The variables that we tested regarding the determinants of capital structure are including

collateral value of assets, growth, profitability, earning volatility, and size. Then, we draw the

figure of conceptual framework for research questions 1a, 1b, 1c, 1d, and 1e. Based on our

conceptual framework for research questions 1a, 1b, 1c, 1d, and 1e, we analysed the previous

research findings for each variable.

Figure 4.1. Conceptual Framework for Research Question 1a, 1b, 1c, 1d, and 1e

Growth Opportunities

Sogorb-Mira and López-Gracia (2003) tested leverage predictions of the trade-off and

pecking order models. They used panel data to test the empirical hypotheses over a sample of 6482

Spanish SMEs during the five-year period between 1994 and 1998. Their results showed a positive

and statistically significant impact between growth opportunities and firm leverage. This result is

consistent with the Michaelas et al. (1999) argument, based on the idea that in SMEs the trade off

Tangibility

Debt Ratio: short-term leverage, long-term leverage, total leverage, and market leverage

Determinants of Capital Structure

Dependent

Variables

Independent

Variables

Size

Risk

Profitability

Growth

Based onTheories of Capital Structure : - Pecking Order Theory - Trade-Off Theory

55

between independence and financing availability is more pronounced and the major part of debt

financing is short term. Sogorb-Mira and López-Gracia (2003) argued that this positive sign could

be affected by the proxy used to measure growth opportunities (the proportion represented by

intangible assets over total assets), which included, according to Spanish accounting rules, a large

proportion of tangible assets, such as assets financed by leasing, patents, trademarks, etc., and

therefore constituted an imperfect measure of the cited variable.

According to the study of Huang and Song (2002), which contains the market and

accounting data from more than 1000 Chinese listed companies up to the year 2000, to document

the characteristics of these firms in terms of capital structure, concluded that the static trade-off

model seemed better than pecking order hypothesis in explaining the features of capital structure

for Chinese listed companies. They used sales growth rates to measure the past growth experience

and Tobin‟s Q to measure a firm‟s growth opportunity in the future. Their finding showed that

firms with a high growth rate in the past tended to have a higher leverage, while firms that had a

good growth opportunity in the future (a higher Tobin‟s Q) tended to have a lower leverage. They

further explained that firms with brighter growth opportunity in the future preferred to keep

leverage low so they would not give up profitable investment because of the wealth transfer from

shareholders to creditors, also the fast growth firms meant that these firms had good investment

opportunities in the past and had used more debt to finance their investment 1.

Pandey (2001) examined the determinants of capital structure of Malaysian companies

using data from 1984 to 1999. He classified data into four sub-periods that corresponded to

different stages of the Malaysian capital market. Debt was decomposed into three categories:

short-term, long-term, and total debt. Both book value and market value debt ratios were

calculated. The results of pooled OLS regressions showed that growth variable had positive

significant influence on all types of book and market value debt ratios. This finding supports both

trade-off and pecking order theories. He further explained that Malaysian firms have higher short-

term than long-term debt ratios. Thus, it seems that they employ short-term debt to finance their

growth.

Sbeiti (2010) found a negative relation between growth opportunities and leverage and it

was consistent with the predictions of the agency theory that high growth firms used less debt,

since they did not wish to be exposed to possible restrictions by lenders. His explanation was that

growing firms had more options of choosing between risky and safe sources of funds and

managers as agents to shareholders went for risky projects in order to maximise the return to their

shareholders. Creditors, however, would be reluctant to provide funds to such firms as they would

bear more risk for the same return. They would thus demand a higher premium from growing

firms. Faced with this prospect and in order to avoid the extra cost of debt, growing firms will tend

to use less debt and more equity. Hence, the relatively large magnitude of the growth coefficient

may be indicative of a higher degree of information asymmetries in these markets, restricting the

ability of managers to raise external debt capital. He further explains that it is also important to

note that the firm-specific coefficients (such as size, liquidity, profitability and tangibility) are

almost identical. However, variables such as market to book ratio reflect the capital market

valuation of the firm, which in turn is affected by the conditions of the capital market.

1 The Determinants of Capital Structure: Evidence from China Samuel G. H. Huang and Frank M. Song

56

Shah and Khan (2007) found that growth variable was significant at a 10% level and was

negatively related to leverage. As they expected, this negative coefficient of -0.0511 showed that

growing firms did not use debt financing. They concluded that their results were in conformity

with the result of Titman and Wessels (1988); Barclay, et al. (1995) and Rajan and Zingales

(1995). They explained that growing firms had more options of choosing between safe and risky

firms. Managers, being agent to shareholders, would try to go for risky projects and increased

return to shareholders. Creditors would be unwilling to give funds to such firms as they would

bear more risk for the same return. To compensate for the additional risk in growth companies,

creditors would demand a risk premium. Facing extra cost of debt, growing firms would use less

debt and more equity.

Shah and Khan (2007) further explained that, since growing firms ran more risk of

project failure as compared to businesses that were static and were run in conventional ways,

managers might not want to add financial risk in addition to the high operational risk of the new

projects. Thus, the managers' unwillingness to add financial risk to firm resulted in lower debt

ratio for growing firms. Çağlayan and Şak (2010), on the other hand, found that market to book

has positive effect on book leverage found that market to book has positive effect on book

leverage.

A positive sign of the market to book was also along the lines of the pecking order theory.

They explained that theoretical expectations about the relationship of size and leverage, on the

other hand, was ambiguous. Han-Suck Song (2005) either expected a positive relationship between

expected growth and leverage, due to higher demand for funds, or a negative relationship, due to

higher costs of financial distress. However, the results they obtained here showed that there existed

no relationship between expected growth and leverage that was of economic significance. They

indicated that one possible explanation might be the effects of the two different theories

neutralising each other, the measurement used here, the percentage changed in total assets did not

reflect future growth possibilities, only past growth. Thus, other more significant results might be

obtained by using another measure for expected growth, for instance market-to-book ratio, a

commonly used proxy for expected growth.

The study of Gaud, Jani, Hoesli and Bender (2003), found the negative sign of growth

and confirmed the hypothesis that firms with growth opportunities were less levered. To analyse

this relationship further, they divided their sample in two sub-samples using the median growth as

cut-off. The negative sign and significance of the coefficient remained irrespective of the leverage

measure for the high growth firms. Concerning the low growth firms, which were typically no

growth firms as the market-to-book ratio was below one, they observed a negative relationship

between growth and leverage when market values were used, and a positive relation when

leverage was measured with book values.

Drobetz and Fix (2003) tested leverage predictions of the trade-off and pecking order

models using Swiss data. They found that firms with more investment opportunities applied less

leverage, which supported both the trade-off model and a complex version of the pecking order

model. They found that among all proxy variables, the strongest and most reliable relationship was

between investment opportunities and leverage. They explained that companies with high market-

to-book ratios had significantly lower leverage than companies with low market-to-book ratios.

Their result was consistent with both the trade-off theory and the extended version of the pecking

order theory.

Sogorb-Mira and López-Gracia (2003) tested leverage predictions of the trade-off and

pecking order models using Spanish data. They found that firms with more investment

57

opportunities applied less leverage, which supported both the trade-off model and a complex

version of the pecking order model.

According to Pandey (2001), the multivariate-pooled OLS regression results showed that

the coefficient of investment opportunity (market-to-book value ratio) variable was insignificant

throughout. This contradicted the pecking order theory of Myers (1977, 1984) that suggested that

companies with high market-to-book value would have lower long-term debt ratios because of the

problem of under-investment. However, his correlation matrix showed that investment opportunity

variable had inverse relation with book and market value short-term debt and long-term debt

ratios. He explained that correlation implied firms with larger investment opportunities were

perceived by lenders to have higher risk (bankruptcy costs).

Therefore, our hypothesis 1 is as follows.

Hypotheses 1a: “As implied by the trade-off theory and the pecking order theory, we

hypothesise that growth opportunity is positively related to debt ratios”.

Profitability

Drobetz and Fix (2003) tested leverage predictions of the trade-off and pecking order

models using Swiss data. Their results confirmed the pecking order model but contradicted with

the trade-off model, more profitable firms used less leverage. They found that profitability was

negatively correlated with leverage, both for book and market leverage. This result reliably

supported the predictions of the pecking order theory. According to Huang and Song (2002), the

results were consistent with the predictions of theoretical studies and the results of previous

empirical studies. Profitability had strong negative relation with total liabilities ratios.

Pandey (2001) results showed that profitability had a significant inverse relation with all

types of book and market value debt ratios. He showed that the results confirmed findings of

earlier studies and were consistent with pecking order theory (Myers, 1984) that postulated a

negative relationship between profitability and debt ratio. The negative relationship between

profitability and debt ratios contradicted with the tax shield hypothesis. He also showed that

profitability seemed to be the most dominant determinant of debt ratios of Malaysian firms as it

generally had high beta coefficients and t-statistics that were significant at 1% level of

significance.

Rebel A. Cole (2008) measured profitability by the winsorised return on assets, and

showed a consistent negative relation with the loan-to-asset ratio. The coefficients for ROA were

significant at the 0.05 level for three of the four surveys, with 1998 being the exception. As a

robustness test, they replaced return on asset with a simple zero-one indicator for profitable firms.

They found that this variable had a negative and highly significant coefficient in each of the four

surveys. These latter findings were strongly supportive of the pecking order theory, which

predicted that profitable firms used less debt because they could fund projects with retained

earnings, but it was inconsistent with the trade-off theory, which predicted that profitable firms

used more debt to take advantage of the debt tax shield, and because they had lower probability of

financial distress.

Sbeiti (2010) found that firm profitability seemed to have a statistically negative and

significant relationship with both the book and market leverage in the three countries. The

negative coefficient of profitability was indicative of the presence of informational asymmetries

which could lead to higher external financing premiums and pecking order behaviour under which

58

firms preferred internal financing from external, but it may also support the view that the lack of

well-developed financial markets forces firms to rely mostly on internal financing.

He further explained that the latter explanation was consistent with Booth et al. (2001)

who reported the same results for the profitability variable and argued that the importance of

profitability was related to the significant agency and informational asymmetry problems in

developing countries. Booth et al. (2001) indicated that it was also possible that profitability was

correlated with growth opportunities so that the negative correlation between profitability and

leverage, proxied the difficulty in borrowing against intangible growth opportunities. Thus, firms

that generated relatively high internal funds generally tended to avoid gearing. The results were

also consistent with Titman and Wessels (1988), Rajan and Zingales (1995), Cornelli et al. (1996),

Bevan and Danbolt (2002) in developed countries, Pandey (2001), Um (2001), Wiwattanakantang

(1999), Chen (2004), Deesomsak, Paudyal and Pescetto (2004) and Antoniou et al. (2007).

In the Shah and Khan (2007) study, the most important explanatory variable was beyond

doubt the profitability variable which had a very high t-statistics of -21.68 and p-value of 0.0000.

The coefficient was -0.7945. The negative sign and statistical significance validated the acceptance

of our fourth hypothesis. The prediction of information asymmetry hypothesis by Myers and

Majluf (1984) was approved by the negative sign whereas the predictions of bankruptcy theory

and free-cash flow hypothesis by Jensen (1984) were not substantiated. It was thus proved that the

pecking order theory dominated trade-off theory. Frydenberg (2001b) describes retained earning as

the most important source of financing. Good profitability thus reduces the need for external debt.

Shah and Khan (2007) explanations were as follows: One possible bias in the finding

could come from the fact that many firms were family controlled in Pakistan. They inflated the

cost of production and the controlling shareholders took out profit in forms other than dividend.

The result was the unreal negative profit figure in income statement. The year to year negative

profit figure reduced the owner‟s equity and increased the debt percentage in overall financing. In

their initial sample, 32% of all observations for profit were negative. Even though they removed

outliers from our analysis that were 3 standard deviations from the overall mean, still they had a

20.1% negative observation for the profitability variable. This was also evident from the fact that

the average profitability ratio was negative for four industries in the sample years. To check for

this bias, they removed all observation of negative profitability and ran regression, the coefficient

for profitability was still negative, but this time the p-value was 0.83 against a very small t-value

of -0.17. This showed that profitability has no significant relationship with leverage. This is why

the results of their main regression model should be interpreted with care with regard to

profitability.

In the Çağlayan and Şak (2010) study, the paper examined the capital structure of banks,

from the perspective of the empirical capital structure literature, for non-financial firms by using

the panel data analysis method ; investigated which capital structure theories could explain the

capital structure choice of the banks; and identified two sub-periods to determine the differences

across determinants of capital structure in the different periods for Turkish banks after the

financial crises and restructuring periods. Their findings showed that profitability was found to

have negative effect on the book leverage. A negative relationship between profitability and

leverage was observed in the majority of empirical studies. This study provided similar results

confirming the pecking order theory rather than static trade-off theory.

In the Han-Suck Song (2005) study, they found that profitability was negatively

correlated with all three leverage measures, which was in line with the pecking-order theory; firms

preferred using surplus generated by profits to finance investments. Han-Suck Song (2005)

explained that the result might also indicate that firms in general preferred internal funds rather

59

than external funds, irrespective of the characteristic of an asset that should be financed (e.g.

tangible or non-tangible asset).

Gaud, Jani, Hoesli, and Bender (2003) concluded that as reported in several other studies,

the profitability variable was negative and significant in all cases (Rajan and Zingales, 1995;

Booth et al. , 2001; Frank and Goyal, 2002). This finding provides support for the pecking order

theory.

As the contradiction of the pecking order theory and the trade-off theory and also previous

research findings, we hypothesise that:

Hypothesis 1b: “As the pecking order hypothesis, we hypothesise that profitability has a

negative relationship with debt ratios and, based on the trade-off theory, we hypothesise that

profitability has a positive relationship with debt ratio”.

Risk

Drobetz and Fix (2003) found, as expected, the relationship between leverage and

volatility negative. They also showed that their finding supports both the trade-off theory (more

volatile cash flows increased the probability of default) and the pecking order theory (issuing

equity was more costly for firms with volatile cash flows). Huang and Song (2002) results showed

that there was the positive relation between total liabilities ratio and volatility. It was consistent

with Hsia‟s (1981) view that firms with higher leverage level tended to make riskier investment.

They found that the companies with high leverage in China tended to make riskier investments.

They further explained that in China, the credit market was still regulated and the term structures

of interest rates were decided by the central bank rather than by the market force such as the

borrower‟s credibility. Banks only had the right to decide whether borrower‟s application was

approved or not and the listed companies generally were regarded as best companies in China. As

a result, the companies with high business risk still could get bank loans at regulated interest rate,

which was lower than market rate if interest rate was deregulated.

Pandey (2001) found that there was a negative relation of earnings volatility with book

and market value long-term debt ratio, which was consistent with the trade-off theory. And it also

revealed a positive relation between risk and short-term debt ratios. In Shah and Khan (2007)

study, the coefficient for earning volatility was 0.0000 and had a very large p-value of 0.869. They

explained that volatility of income had no impact on the debt level. The magnitude of earning

volatility was a sign of expected bankruptcy. Firms with higher volatility were considered risky

because they could go bankrupt. The cost of debt for such firm should be more and thus, these

firms would employ low level of leverage. They further added that court processes were slow in

Pakistan and there were very few cases of bankruptcy, this could be the possible explanation for

the insignificant relationship between earning volatility and leverage. Creditors did not consider

the income source or the variation in income for the repayment of loan and interest by the firm.

They relied more on the security of fixed assets.

Han-Suck Song (2005) revealed that the effect of income variability on debt was

approximately zero, but still statistically significant. Lööf (2003) also obtained similar results,

according to him, this might be due to the fact that the time period studied (1991 to 1998; this

study: 1992 to 2000) coincided with a period of strong economic recovery and a generally positive

trend in revenues. Gaud, Jani, Hoesli and Bender (2003) concluded that the positive impact of risk

for the fixed effects estimation when using market data implied that firms, which performed below

60

average, were less levered. In other words, companies with a high operating risk tried to control

total risk by limiting financial risk.

Based on the same prediction of the theories but with slightly different reason, our

hypothesis is as follows.

Hypotheses 1c: “In accordance with the pecking order theory and trade-off theory, we

hypothesise a negative relationship between risk (earnings volatility) and debt ratio”.

Size

Drobetz and Fix (2003) found that size was positively related to leverage, indicating that

size was a proxy for a low probability of default. However, the estimated coefficients on size were

generally not significant. They also found that it was in contrast to the results in Rajan and

Zingales (1995), where firms in Germany tended to be liquidated more easily than in the Anglo-

Saxon countries. Large firms had substantially less debt than of small firms. Therefore, Drobetz

and Fix (2003), interpreted their results for Switzerland as size being a proxy for low expected

costs of financial distress, where small firms in Switzerland were especially wary of debt. Again,

they concluded that this result supported the trade-off theory, suggesting that large firms exhibited

lower probability of default.

Sogorb-Mira and López-Gracia (2003) found that firm size and leverage were found to be

positively related. They explained that this relationship could come from the fact that small-

medium enterprise (SMEs) had to face higher bankruptcy costs, greater agency costs and bigger

costs to resolve the higher informational asymmetries. Even within this firm category, SMEs of

greater size could access a higher leverage. They also found that this result was the same as that

obtained by a considerable number of previous studies (Ocaña et al. , 1994; Hutchinson, 1995;

Chittenden et al. , 1996; Berger and Udell, 1998; Michaelas et al. , 1999; Romano et al. , 2000).

Huang and Song (2002) concluded that, on the relationship between size and leverage, if

size was interpreted as a reversed proxy for bankruptcy cost, it should have less or no effect on

Chinese firms‟ leverage because the state kept around 40% of the stocks of these firms and,

because of soft budget constraint, state-controlled firms should have much less chance to go

bankrupt. They argued that although the state was still a controlling shareholder for most listed

firms, these firms were limited corporations; it was unlikely that the state would bail them out,

even in case of trouble, because the central government was only a legal representative of state

shareholder. The beneficiaries of state shares in these listed firms might be local governments,

who could behave just like big private shareholders. They believed the economic force worked

quite well even in an environment where the state was the controlling shareholder.

Pandey (2001) found that size was positively related to all types of book and market

value debt ratios and all of coefficients were significant at 0.01level of significance. He showed

that the positive correlation between size and debt ratios confirmed the hypothesis, that larger

firms tended to be more diversified and less prone to bankruptcy and the direct cost of issuing debt

or equity was smaller. This is consistent with the trade-off theory.

Rebel A. Cole (2008) investigated firm size, as measured by the natural logarithm of total

assets, and found size was inversely related to firm leverage, and this relation was significant at

better than the 0.001 level in each survey. He explained that larger firms used significantly less

debt in their capital structure, and his result was at odds with what Frank and Goyal (2006) cited as

one of the “core set of seven factors that are correlated with cross-sectional differences in

leverage.” Cross-sectional studies of publicly traded firms found that leverage was “robustly

61

related” to firm size, as measured by the log of assets. He added that, clearly, this result did not

hold for privately held firms. This result also is inconsistent with the trade-off theory, which

predicts larger firms should use more leverage than smaller firms.

Sbeiti (2010) investigated the determinants of capital structure in the context of three

GCC countries and the impact of their stock markets' developments on the financing choices of

firms operating in these markets. He found that the coefficient values of the size variable remained

positive and were statistically significant in relation to both book and market leverage ratios across

the three countries. These results confirmed the importance of the size variable as a determinant of

the capital structure decisions of firm operating in the GCC markets.

He added that his result was in line with the results reported by Rajan and Zingales

(1995), Wiwattanakantang (1999), Booth et al. (2001), Pandey (2001), Prasad et al. (2001),

Deesomsak, Paudyal and Pescetto (2004), Antoniou et al. (2007), the size of the coefficient was

positive and statistically significant in the case of all three countries and for both measures of

leverage. He explained that these results were consistent with the theoretical prediction that larger

firms tended to be more diversified, less prone to bankruptcy with smaller direct cost for issuing

debt or equity. If size was a proxy for the inverse probability of bankruptcy, then the positive

relation between size and leverage complied with the predictions of the trade-off theory. This is

because larger firms can diversify their investment projects on a broader basis and limit their risk

to cyclical fluctuations in any one particular line of production. Moreover, informational

asymmetries tend to be less severe for larger firms than for smaller ones; hence, larger firms find it

easier to raise debt finance. It is also noticed that size seems to have only a limited impact on the

capital structure of firms in Oman as compared to Kuwaiti and Saudi Arabia firms. This result may

indicate smaller differences in informational asymmetries between large and small companies in

Oman.

In Shah and Khan (2007) study, size had a positive coefficient but was insignificant, with

the coefficient value of 0.0002, the very small t-value of 0.07, and the p-value of 0.940. They

showed that size variable was not a proper explanatory variable of debt ratio. Their second

hypothesis was based on the Rajan and Zingales‟ (1995) argument that there was less asymmetric

information about the larger firms which reduced the chance of undervaluation of new equity.

Their finding did not confirm to the Titman and Wessels‟ (1988) argument as well that larger firms

were more diversified and have lesser chances of bankruptcy that should motivate the use of debt

financing.

Shah and Khan (2007) explained why their finding on size of a firm with relation to the

leverage ratio did not confirm to the established theories. Trade off theory suggested that firm size

should matter in deciding an optimal capital structure because bankruptcy costs constituted a small

percentage of the total firm value for larger firms and greater percentage of the total firm value for

smaller firms. As debt increased the chances of bankruptcy, hence smaller firms should have lower

debt ratio. In case of Pakistan, the court process was very slow. Negative equity figure in the

balance sheet of a firm year after year and the firm still managed to survive. Among total

observations of equity figure, 15% were in negative. This meant that firms were not much fearful

of bankruptcy. They managed to survive even with negative equity figure. In the given scenario,

size was not a matter. Facing no or very low bankruptcy costs, firms would employ debt regardless

of its size.

They further explained that initial public offerings are negligible in Pakistan both for

small and large firms. There were only a few cases of selling ownership in government owned

enterprises to public in the recent past. It meant that size was not the determinant of new equity

62

issue rather other factors like family control, capital market development, managerial control, etc.,

determine the issue of new equity. Hence, Shah and Khan (2007) concluded that size should not

necessarily be a significant determinant of leverage ratio.

Rajan and Zingales(1995) argued that the problem of undervaluation of new equity issue

for large firm was not severe as there was less information asymmetry about them. Hence size

should be negatively related to leverage. Çağlayan and Şak (2010) research concluded that size

was found to have positive relationships with the leverage of banks. The findings of the

relationship with the size were in line with the static trade-off and agency cost theory.

In the Han-Suck Song (2005) study, the result revealed that size was a significant

determinant of leverage. They explained that while size was positively related to both total debt

and short-term debt ratio, it was negatively correlated with long-term debt ratio, although, the

economic significance was rather small for the latter case. They added that even if the data did not

allow them to further decompose short-term debt, they might still find the results of Bevon and

Danbolt (2000) interesting. They found that while size was positively correlated with both trade

credit and equivalent and short-term securitized debt, it was negatively correlated with short-term

bank borrowing. This might indicate that small firms were supply constrained, in that they did not

have sufficient credit ranking to allow them to long-term borrowing.

Gaud, Jani, Hoesli, and Bender (2003) analysed the determinants of the capital structure

for a panel of 106 Swiss companies listed in the Swiss stock exchange. Both static and dynamic

tests were performed for the period 1991-2000. They found that the size of companies, the

importance of tangible assets and business risk were positively related to leverage, while growth

and profitability were negatively associated with leverage. The sign of these relations suggested

that both the pecking order theory and trade off hypothesis were at work in explaining the capital

structure of Swiss companies, although more evidence existed to validate the latter theory. Their

analysis also showed that Swiss firms adjusted toward a target debt ratio, but the adjustment

process was much slower than in most other countries.

Gaud, Jani, Hoesli, and Bender (2003) found positive impact of size on leverage. They

explained that it was consistent with the results of many empirical studies (Rajan and Zingales,

1995; Booth et al., 2001; Frank and Goyal, 2002). It led them to reject the hypothesis that size

acted an inverse proxy for informational asymmetries, but could suggest that size acted an inverse

proxy for the probability of bankruptcy. They added that the variable size was not significant any

more when leverage was computed with long-term debt only. One possible explanation from them

was that large companies had easier access to the bond markets (Ferri and Jones, 1979). The

development of financial markets has pushed large companies to search for better credit

conditions. Consequently, there has been a tendency for banks to grant more loans to small and

medium size companies. The market for short term debt securities is not well developed in

Switzerland. This allows banks to select between borrowers. As banks will prefer large firms to

small ones, the sign of the size coefficient is positive.

For these differences, we test the following hypothesis.

Hypotheses 1.d: “As suggested by the trade-off theory, we hypothesise that size has a positive

relationship with debt ratio, and as suggested by the pecking order theory of the capital

structure there is a negative relationship between debt ratio and size”.

Tangibility

63

Drobetz and Fix (2003), found that tangibility was almost always positively correlated

with leverage. They showed that the regression coefficient on tangibility was significant in about

half of all regressions and this supported the prediction of the trade-off theory that the debt-

capacity increased with the proportion of tangible assets on the balance sheet. Sogorb-Mira and

López-Gracia (2003) tested leverage predictions of the trade-off and pecking order models using

Spanish data. They showed that at an aggregate level, leverage of Spanish firms was

comparatively low, but the results depended crucially on the exact definition of leverage. The

result confirmed the pecking order model but contradicted with the trade-off model.

Huang and Song (2002) found that, in contrast to theoretical predictions, tangibility was

negatively related with total liability. They explained that the reason for that might be the non-debt

part of total liability did not need collaterals. Long-term debt ratio was positively correlated with

tangibility. Pandey‟s results (2001) indicated a significant negative relation of tangibility (fixed

asset to-total asset ratio) with book and market value short-term debt ratios. The relation of

tangibility with the market value long-term debt ratio was also significantly negative while with

the book value long-term ratio, it was not statistically significant. These results contradicted the

trade-off theory that postulated a positive correlation between long-tem debt ratio and tangibility

since fixed assets act as collateral in debt issues. He also concluded that his results were consistent

with DeAngelo and Masulis (1980) who suggested an inverse correlation between tangibility and

debt ratio.

Rebel A. Cole (2008) studied the tangibility, as measured by the ratio of property, plant

and equipment to total assets. The result was positive across each of the four surveys and was

statistically significant at better level than the 0.05 level for each survey except for the result in

2003. The coefficients ranged from 0.073 to 0.171, indicating that a 100 basis point increased in

the tangible asset ratio was associated with a 7.3 to17.1 basis point increase in the loan-to-asset

ratio. According to Frank and Goyal (2006), the relation between tangibility and leverage was

reliably positive in cross-sectional studies of publicly traded firms. Their results for privately held

firms were broadly consistent with this finding.

Sbeiti (2010) found that the stylized fact that the tangibility variable was positively

related to the availability of collateral and leverage was not consistent with the findings in the

paper, where tangibility was negative and statistically significant in relation to both book and

market value of leverage in the three countries. She added that this negative association between

leverage and tangibility could be explained by the fact that those firms that maintained a large

proportion of fixed assets in their total assets tended to use less debt than those which did not. This

could be due to the fact that a firm with an increasing level of tangible assets might have already

found a stable source of income, which provided it with more internally generated funds and

avoided using external financing.

She further explained that another explanation for this relationship could be the view that

firms with higher operating leverage (high fixed assets) would employ lower financial leverage,

and overall the results were consistent with Cornelli et al. (1996), Hussain and Nivorozhkin

(1997), Booth et al. (2001), and Nivorozhkin (2002) who also suggested a negative relation

between tangibility and debt ratio. Finally, the relatively larger coefficient value of tangibility for

the Saudi firms might indicate that firms in this country had an effective guarantee against

bankruptcy.

In Shah and Khan (2007) study, they used two variants of panel data analysis, attempted

to find the determinants of capital structure of listed none-financial firms for the period 1994-

2002. Pooled regression analysis was applied with the assumption that there were no industry or

64

time effects. They used six explanatory variables to measure their effect on leverage ratio. Three

of their variables were significantly related to leverage ratio whereas the remaining three variables

were not statistically significant in having relationship with the debt ratio. Their results approved

the prediction of trade-off theory in case of tangibility variable whereas the earning volatility and

depreciation variables failed to confirm to trade-off theory. The growth variable confirmed the

agency theory hypothesis whereas profitability approved the predictions of pecking order theory.

Size variable neither confirmed to the prediction of trade-off theory nor to asymmetry of

information theory.

Shah and Khan (2007) found that tangibility, with a coefficient of 0.1304 was

significantly related to debt. It had the second highest t-value of 5.56 against a very low p-value of

0.0000. This showed that tangibility was one of the most important determinants of leverage ratio

in Pakistan. Thus their first hypothesis was confirmed by the statistically significant positive

relationship between tangibility and leverage. This finding was in contrast to the earlier finding by

Shah and Hijazi (2004). They found that tangibility was not significantly related to leverage ratio.

Çağlayan and Şak (2010) investigated the relationship between tangibility and book

leverage, and it was found to be negative in this study. They explained that this significant

negative relationship between tangibility and leverage provided further support for the agency cost

theory and the existence of conflict between debt holders and shareholders.

In Han-Suck Song (2005) study, the paper analysed the explanatory power of some of the

theories that have been proposed in the literature to explain variations in capital structures across

firms. In particular, this study investigated capital structure determinants of Swedish firms based

on a panel data set from 1992 to 2000 comprising about 6000 companies. Swedish firms were on

average very highly leveraged, and furthermore, short-term debt comprised a considerable part of

Swedish firms‟ total debt. An analysis of determinants of leverage based on total debt ratios might

mask significant differences in the determinants of long and short-term forms of debt. Therefore,

their paper studied determinants of total debt ratios as well as determinants of short-term and long-

term debt ratios. The results indicated that most of the determinants of capital structure suggested

by capital structure theories appeared to be relevant for Swedish firms.

The coefficients of tangibility are highly statistically significant for all three debt

measures. But while the results show that tangibility has a positive relationship with total debt

ratio and long-term debt ratio, as expected according to the theoretical discussion above,

tangibility is negatively related to the short-term debt ratio. This finding is consistent with the

results of Bevan and Danbolt (2000), Huchinson et al. (1999), Chittenden et al. (1996) and the

Van der Wijst and Thurik (1993) report (see also Michaleas et.al., 1999).

Gaud, Jani, Hoesli, and Bender (2003) concluded that the coefficient of the TANG

variable was positive and significant for the panel data estimations, and this result was similar to

those reported in previous research (Rajan and Zingales, 1995; Kremp et al., 1999; Frank and

Goyal, 2002). Their result suggested that firms used tangible assets as collateral when negotiating

borrowing, especially long term borrowing. The observed sign of the relationship did not confirm

the sign that would be expected when using the pecking order theory framework. In such a

framework, firms with smaller tangible assets were more subject to informational asymmetries,

and were more likely to use debt - principally short term debt - when they needed external

financing.

Our hypothesis, based on theory and previous research, is as follow.

65

Hypotheses 1e: “In accordance with the trade-off theory, we hypothesise a positive relationship

between asset tangibility and debt ratio”.

4.1.2 Conceptual Framework for Research Question 2

The variables that we tested regarding the choice of capital structure as implied by the

pecking order theory are including retained earning, net debt issue, net equity issue, and debt to

repurchase equity. Then, we construct the figure of conceptual framework for research questions 2.

Based on our conceptual framework for research questions 2, we analysed the previous research

findings for each variable.

The relationship between variables is shown by the following figure.

Figure 4.2 Conceptual Framework for Research Question 2, 3a, 3b, and 3c

The pecking order theory states that changes in debt have played an important role in


according to this theory. Shyam-Sunder and Myers (1999) examined how debt responded to short-

term variation in investment and earnings. The theory predicted that when investments exceeded

earnings, debt grew, and when earnings exceeded investments, debt fell. Tests of the pecking order

theory defined financing deficit as investments plus change in working capital plus dividends less

internal cash flow. The theory predicted that in a regression of net debt issues on the financing

deficit, the estimated slope coefficient should be one. The slope coefficient indicated the extent to

which new debt issues were explained by financing deficits.

External Financing

Internal Financing

Financing Decision

Issue Debt to Repurchase Equity

Equity Debt

Firm’s Stock Price

Based on Theories of Capital Structure :

- Pecking Order Theory - Trade-off Theory - Signalling Theory - Asymmetric Information

Dependent

Variables

Independent

Variables

66

Shyam-Sunder and Myers found strong support for pot prediction in a sample of 157

large firms. The coefficient was 0.75 with an R2 of 0.68. They interpreted this evidence to imply

that “pecking order was an excellent first order descriptor of corporate financing behavior”

(Shyam-Sunder and Myers, 1999).

Previous research from Indonesia, Ari Christianti (2008), concluded that: (1) The results

of this study did not fully support the pecking order theory in explaining the behaviour of firms

financing in the Indonesia Stock Exchange (IDX) especially the manufacturing sector. This could

be explained from the results of the estimation that showed a negative and significant coefficient

of pecking order. (2) It may be explained from the results of this study, is the Indonesian capital

market conditions that are different from capital markets in developed countries studied by

Shyam-Sunder and Myers (1999), Frank and Goyal (2003) and Jong, Verbeek, and Verwijmeren

(2005). In addition, the impact of economic crisis in 1997 still affected the economic condition of

Indonesia until 2005.

Previous research from other country: Leary and Roberts (2005) empirically examined

the pecking order theory of capital structure using a new empirical model that was motivated by

the pecking order's decision rule and implied financing hierarchy. A power study of their

associated hypothesis test revealed that the test could distinguish pecking order behaviour from

non-pecking order behaviour, as well as quantify the degree to which firms adhered to the

financing hierarchy. They found that 62% (29%) of the firms in the sample were following the

pecking order in their decision between internal and external (debt and equity) financing and that

most of the equity issuing violations were not due to debt capacity concerns, as suggested by the

modified version of the pecking order. Leary and Roberts (2005) showed empirically that the

pecking order did not seem to be an implication of information asymmetry.

Francisco Sogorb-Mira and José López-Gracia (2003) explored two of the most relevant

theories that explained financial policy in small and medium enterprises (SMEs): pecking order

theory and trade-off theory. Panel data methodology was used to test the empirical hypotheses

over a sample of 6482 Spanish SMEs during the five-year period 1994–1998. Their results

suggested that both theoretical approaches contributed to explain capital structure in SMEs.

However, while they found evidence that SMEs attempted to achieve a target or optimum leverage

(trade-off model), there was less support for the view that SMEs adjusted their leverage level to

their financing requirements (pecking order model). Cotei and Farhat (2008) investigated the

models used in testing the trade-off and pecking order theories. Specifically, for the pecking order

theory, they examined the symmetric behaviour assumption. For the pecking order model, the test

results rejected the symmetric behaviour assumption at the industry level as well as across all

industries.

Under the pecking order model, firms in financing deficit used debt to finance their new

investment, whereas firms in financing surplus ended up retiring debt rather than repurchasing

equity. Hence, their results showed that for the pecking order model, they rejected the hypothesis

that firms had a symmetric behaviour regardless of the sign of the financing variable. Their results

showed that firms had the tendency to reduce debt by a significantly higher proportion when they

had financing surplus compared to the proportion of debt issued when they had financing deficit.

Joher, Ahmed, and Hisham (2009) paper drew on studies from finance and accounting

literature to revisit pecking order and static trade-off-hypothesis in the context of the Malaysia

capital market, using a sample of 102 list firms over a four-year time frame (1999-2003). Their

evidence from the pecking order model suggested that the internal fund deficiency was the most

important determinant that possibly explained the issuance of new debt. Hence, the pecking order

67

hypothesis was well explained in the Malaysian capital market despite the lower predicting power.

This could be the evidence from both pecking order models that exhibited a significant coefficient

for financing deficit, significant at the conventional level, but with very low R2. To address this

issue of low predicting power, the pecking order model was expanded by including the component

of internally generated fund deficiency such as dividend, debt repayment, capital expenditure,

investment on working capital and operating cash flow. The expanded pecking order model

provided more vibrant explanation for debt issuance with higher predictive power.

Meanwhile, their result for static trade-off-model was not fit to explain the issuance of

new debt issue in Malaysian capital market. That was an interesting findings that confirmed the

fact that Malaysian firms did not too much care about tax-shield benefit derived from employing

both debt and non-debt tax-shields. The firm‟s size, which was used to neutralise the size effect,

appeared to provide some explanation for the variation in its capital structure policy choice.

In the Bharath, Pasquariello, and Wu (2008) study, using a novel information asymmetry

index based on measures of adverse selection developed by the market microstructure literature,

they tested whether information asymmetry was an important determinant of capital structure

decisions, as suggested by the pecking order theory. They found that information asymmetry did

affect the capital structure decisions of U.S. firms over the sample period 1973–2002. Overall, this

evidence explained why the pecking order theory was only partially successful in explaining all of

firms‟ capital structure decisions.

The Shyam-Sunder and Myers (1994) paper tested traditional capital structure models


model, which predicted external debt financing driven by the internal financial deficit, had much

greater explanatory power than a static trade-off model which predicted that each firm adjusts

toward an optimal debt ratio. They summarized main conclusions regarding pot as follows: (1)

The pecking order is an effective first-order descriptor of corporate financing behaviour. (2) The

coefficient and significance of the pecking order variable change hardly at all. (3) The strong

performance of the pecking order does not occur just because firms fund unanticipated cash needs

with debt in the short run. Their results indicated that firms planned to finance anticipated deficits

with debt.

Medeirosa and Daherb tested two models with the purpose of finding the best empirical

explanation for the capital structure of Brazilian firms. The models tested were developed to

represent the static tradeoff theory and the pecking order theory. The sample consisted of firms

listed in the São Paulo (Brazil) stock exchange from 1995 through 2002. By using panel data

econometric methods, they aimed at establishing which of the two theories had the best

explanatory power for Brazilian firms. The analysis of the outcomes led to the conclusion that the

pecking order theory provided the best explanation for the capital structure of those firms.

The pecking order theory established that the financial deficit was covered by debt,

permitting the issue of new shares in exceptional cases only. The Frank and Goyal model stated

that the deficit coefficient must be equal to zero in order to validate the strong form of the pecking

order theory. Therefore, the most important test was the one which determined the value of this

coefficient. The results obtained in Medeirosa and Daherb study supported the pecking order

theory in its semi-strong form, since both the aggregate and the disaggregate equations were led to

accept the null that the slopes were equal to one, but to reject the null that the intercepts were equal

to zero.

68

The Lakshmi Shyam-Sunder and Stewart C. Myers (1999) paper tested traditional capital

structure models against the alternative of a pecking order model of corporate financing. The basic

pecking order model, which predicts external debt financing driven by the internal financial

deficit, has much greater timeseries explanatory power than a static tradeoff model, which predicts

that each firm adjusts gradually toward an optimal debt ratio.

Instead, they view the theories as contending hypotheses and examine their relative

explanatory power. The attention to statistical power is an important methodological point. They

summarized the main conclusions regarding pot as follows. (1) The pecking order is an excellent

first-order descriptor of corporate financing behaviour, for their sample of mature corporations. (2)

The strong performance of the pecking order does not occur just because firms fund unanticipated

cash needs with debt in the short run. Their results suggested that firms planned to finance

anticipated deficits with debt.

Therefore, our hypothesis 2 is as follows:

Hypothesis 2: “Firms in the manufacturing sector raise capital for investments externally (with

debt, equity, or debt to repurchase equity)”.


The variables that we tested regarding the effect of capital structure choices on stock

price as implied by some theories are including firm‟s stock price, net debt issue, net equity issue,

or issue debt to repurchase equity. Then, we construct the figure of conceptual framework for

research questions 3. Based on our conceptual framework for research questions 3, we analysed

the previous research findings for each variable. The relationship between variables is shown by

the figure 4.2.

When a firm issues, repurchases or exchanges one security for another, it changes its

capital structure. There are several theories which explain the relationship between capital

structure and stock price.

Theories which explain the relationship between net debt and equity issue and stock price

are as follow.

Net Debt Issue

Theories implied different implication for the issuance of new debt on firm‟s stock price.

Ross (1977) introduces the notion of signalling in the capital structure theory. According to his

theory, the managers know the true distribution of the company returns, but investors do not. He

argues that higher financial leverage can be used by the managers to signal an optimistic future of

the company since the debt is a contractual obligation to repay both principal and interests. The

failure to make those payments could lead to bankruptcy and by consequence the managers would

lose their jobs. Therefore adding more debt to the capital structure could be interpreted as a good

signal of the managers‟ optimism about their companies.

The issuance that new debt will positively influence a firm‟s stock price is based on

signalling theory through capital structure, the increased level of leverage is accompanied by

higher risk of bankruptcy, the increased level of debt indicates the confidence of the management

in the future prospects of the firm. Hence, it carries greater conviction than a mere announcement

of undervaluation of the firm, by the management. The markets normally react favourably to

moderate increases in leverage.

69

On the other hand, the issuance of new debt will less negatively influence a firm‟s stock

price than the issuance of new equity, or no market reaction, is as implied by the pecking order

theory. The pecking order theory is usually interpreted as predicting that securities with less

adverse selection (debt) will result in less negative or no market reaction.

However, under the trade-off theory, the market response to a leverage change confounds

two pieces of information. Under the theory, firms will only take actions if they expect benefits.

The information contained in security issuance decisions could be either good news or bad news. It

would be good news if the firm is issuing securities to take advantage of a promising new

opportunity that was not previously anticipated. It might be bad news if the firm is issuing

securities because the firm actually needs more resources than anticipated to conduct operations. A

firm may also issue securities now in anticipation of a change in future needs. This implies that the

trade-off theory by itself places no obvious restrictions on the market valuation effects of issuing

decisions. Everything depends on the setting.

Net Equity Issue

Meanwhile, based on signalling theory, agency perspective, and pecking order theory, the

issuance of new equity will negatively influence a firm‟s stock price. As implied by signalling

through capital structure, an issue of equity is a signal that the firm is overvalued. The market

concludes that the management has decided to offer equity because it is valued higher than its

intrinsic worth by the market. The markets normally react negatively to fresh issue of equity.

Jung et al. (1996) suggested an agency perspective and argued that equity issues by firms

with poor growth prospects reflected agency problems between managers and shareholders. If this

is the case, then stock prices will react negatively to the news of equity issues. However, the

pecking order theory is usually interpreted as predicting that securities with more adverse selection

(equity) will result in more negative market reaction.

Myers and Majluf (1984) assumed that company managers have always more information

about the true value of the company than the other investors. Managers will therefore time a new

equity issue if the market price exceeds their own assessment of the stock value – if the stocks are

overvalued by the market. Since investors are aware of the existence of the information

asymmetry, they will interpret the announcement of an equity issue as a signal that the listed

stocks are overvalued, which subsequently will cause a negative price reaction. The managers can

use the information asymmetry to their profit and to reinforce their entrenchment strategy in their

respective companies. Besides, they can use their informational advantage in order to get more

benefits and to maximise their income (Stiglitz and Edlin, 1992). With this intention, the managers

can reduce the threat of the competition of the potential managers on the labor market by two

possible manners: either by setting up investments strongly dependent on their specific

information, or by investing in projects with high information asymmetry.

Based on the undervaluation hypothesis, stock repurchases offer flexibility not only for

distributing the excess of funds but also the timing of distributing these funds. This flexibility in

timing is beneficial because firms can wait to repurchase until the stock price is undervalued. The

undervaluation hypothesis is based on the premise that information asymmetry between insiders

and shareholders may cause a firm to be misvalued. If insiders believe that the stock is

undervalued, the firm may repurchase stock as a signal to the market or to invest in its own stock

and acquire mispriced shares. According to this hypothesis, the market interprets the action as an

indication that the stock is undervalued (Dittmar, 1999). Because of the asymmetric information

70

between managers and shareholders, share repurchase announcements are considered to reveal

private information that managers have about the value of the company (in Smura).





the market‟s expectation about future profitability (in Gustavo Grullon and Roni Michaely, 2002).

Some previous empirical evidence regarding to debt issue on stock price are the

following: Announcements of ordinary debt issues generate zero market reaction on average

(Eckbo (1986) and Antweiler and Frank (2006)). The zero market reaction to corporate debt issues

is robust to various attempts to control for partial anticipation.


portion of debt in their capital structure. Ross (1977) assumed the firms that are less good

performance would not use debt in large portion as it would be followed by the high chance of


higher debt, while firms that are less good performance will use more of equity. Ross (1977)


company's capital structure and they would give a higher value on the company with larger debt

portion. It indicated that the result did not support the stating of the signalling theory. The result

indicated that the greater the leverage, the greater the possibility of financial distress leading to

bankruptcy. When the company went bankrupt, shareholders would lose money they have invested

in the company (Peirson et al, 2002).

Exchange of common for debt/preferred stock generates positive stock price reactions

while exchange of debt/preferred for common stock generates negative reactions (Masulis, 1980a).

Summarising the event study evidence, Eckbo and Masulis (1995) concluded that announcements

of security issues typically generated a nonpositive stock price reaction.

In Indonesia, the regression coefficient between leverage and stock price is significantly

negative. The use of high leverage will be responded by the market with a fall in stock prices.The

results are consistent with the findings of a negative relationship between leverage and stock price

as proposed by Frank and Goyal (2003). Relationship between the two variables will be positive at


Announcements of convertible debt issues result in mildly negative stock price reactions

(see Dann and Mikkelson (1984) and Mikkelson and Partch (1986)). The valuation effects are the

most negative for common stock issues, slightly less negative for convertible debt issues and least

negative (zero) for straight debt issues. The effects are more negative the larger the issue.

Some previous empirical evidence regarding the equity issue on stock price are the

following: Announcements of equity issues result in significant negative stock price reactions

(Asquith and Mullins Jr., 1986; Masulis and Korwar, 1986; and Antweiler and Frank, 2006).

The negative market reaction to equity issues and zero market reaction to debt issues are

consistent with adverse selection arguments. Indeed, there are other interpretations. Jung et al.

(1996) showed that firms without valuable investment opportunities experienced a more negative

stock price reaction to equity issues than did firms with better investment opportunities. Thus,

agency cost arguments could also explain the existing evidence on security issues. Further support

for the agency view came from the finding that firms without valuable investment opportunities

71

issuing equity invest more than similar firms issuing debt and that firms with low managerial

ownership have worse stock price reaction to new equity issue announcements than do firms with

high managerial ownership.

The impact of equity issues appears to differ between countries. Several studies find

positive market reaction to equity issues around the world (Eckbo et al., 2007). To understand this

evidence, Eckbo and Masulis (1992) and more recently Eckbo and Norli (2004) examine stock

price reactions to equity issues conditional on a firm‟s choice of flotation method. Firms can issue

equity using uninsured rights, standby rights, firm commitment underwriting and private

placements. The stock price reactions to equity issues depend on the floatation method. For U.S.

firms, Eckbo and Masulis (1992) found that the average announcement-period abnormal returns

were insignificant for uninsured rights offerings and they were significantly negative for firm-

commitment underwritten offerings. Eckbo and Norli (2004) studied equity issuances on the Oslo

Stock Exchange. They found that uninsured rights offerings and private placements resulted in

positive stock price reactions while standby rights offerings generated negative market reactions.

These papers interpreted the effect of the flotation method as reflecting different degrees of

adverse selection problems.

Some previous empirical evidence regarding the stock repurchases on stock price are as

follows: Many studies show that repurchases are associated with a positive stock price reaction.

Vermaelen (1981), Dann (1981), and Comment and Jarell (1991) found the positive stock price

reaction at the announcement of a stock repurchase program should correct the misevaluation.

Ikenberry, Lakonishok and Vermaelen (1995) showed that this increase might not be sufficient to

correct the price since repurchasing firms, particularly low market to book firms, earned a positive

abnormal return during the four years subsequent to the announcement. The amount of information

available and the accuracy of the valuation of firms by the market could affect firms‟ repurchase

decisions.

According to Jensen (1986), firms repurchased stock to distribute excess cash flow.


between repurchases and levels of cash flow. Stephens and Weisbach also showed that repurchase

activity was negatively correlated with prior stock returns, indicating that firms repurchased stock

when their stock prices were perceived as undervalued. This result agrees with Vermaelen‟s

(1981) findings that firms repurchase stock to signal undervaluation. Thus, firms repurchase stock

when they are undervalued and have the excess cash to distribute. Masulis (1980b), Dann (1981),

and Antweiler and Frank (2006) also found that the announcement effects were positive when

common stock is repurchased. According to Brav et al. (2005.b.) it was discovered on their survey

that only 22.5 percent of executives believed that reducing repurchases had negative

consequences. On the other hand, almost 90 percent thought that reducing dividends had negative

consequences.

Therefore, our hypotheses 3 are as follow:

Hypotheses 3:

(a) If firms issue new debt, then the firm’s stock price will be higher.

(b) If firms issue new equity, then the firm’s stock price will be lower.

(c) If firms issue debt to repurchase equity, then the firm’s stock price will be higher.

72


The variables that we tested regarding the choice of capital structure over firm‟s life cycle

as implied by the pecking order theory are including net debt issue, net equity issue, and issue debt

to repurchase equity. Then, we construct the figure of conceptual framework for research

questions 4. Based on our conceptual framework for research questions 4, we analysed the

previous research findings for each variable. The relationship between variables is shown by the

figure 4.3.

Figure 4.3 Conceptual Framework for Research Question 4

Research question 4 focusses only on the pecking order theory as only the pecking order

theory, which specifically explains about the specific preference order of firms‟ capital structure

over firms‟ life cycles.

It is important examining the firm capital structure over the life cycle of the firm in

solving the problem of firm financing deficit. Firms in different life cycle stage have different

characteristics especially regarding information asymmetry and dividend payment. Mature firms

have less information asymmetry, whereas growth firms have more information asymmetry. We

test hypothesis 4 to examine which firm‟s life cycle follow the pecking order more closely. It is the

most interesting part of this research as firm in different life cycle stage has different capital

structure choices by considering the characteristics, and information asymmetry as implied by

pecking order theory.

The empirical evidence for the pecking order theory over a firm‟s life cycle has been

mixed. Helwege and Liang (1996) followed a sample of recent IPO firms and found that these

firms‟ decision to access the external finance markets as well as their choice of type of external

finance is inconsistent with the pecking order. Shyam-Sunder and Myers (1999) proposed a direct

test of the pecking order and found strong support for the theory among a sample of large firms.

Frank and Goyal (2003) argued that the Shyam-Sunder and Myers test rejected the pecking order

for small public firms. They concluded that this finding was in contrast to the theory since small

Newly

Retained

Earnings

Financial Deficit

Net

Equity

Issued

Net Debt

Issued

Capital structure

over firm’s life

cycle as implied

by Pecking Order

Theory

Independent

Variables

Dependent Variables

Over firms life cycle stages : growth and mature firms

73

firms were thought to suffer most from asymmetric information problems and hence, should be the

ones following the pecking order.

More recent work by Lemmon and Zender (2004) and Agca and Mozumdar (2004) have

shown that the Shyam-Sunder and Myers test did not account for a firm‟s debt capacity, a

constraint that was particularly binding for small firms. Thus, it was not surprising that this test

failed to find support for the pecking order among small firms. To address this shortcoming,

Lemmon and Zender and Agca and Mozumdar used sub-samples of firms that were the least debt-

constrained and they found support for the pecking order. In addition, once debt capacity

constraints were accounted for, they found that the pecking order performed well even for small

firms.





adverse selection costs followed the pecking order more closely, consistent with the theory.

More importantly, they found that growth firms had greater financing deficits but smaller

debt capacity. It implied that growth firms would reach their debt capacities more often than

mature firms. They argued that within a broad sample of firms, inference regarding the empirical

performance of the pecking order theory was weakened if differences in these two key attributes

were unaccounted for in the empirical test.

Their results were consistent with firms following the pecking order: the coefficient on

the deficit was positive and the coefficient on the deficit-squared was negative. Both growth and

mature firms were issuing debt first, while equity was the residual source of financing once they

reached their debt capacities. Comparing across life cycle stages however, they found that mature

firms had significantly higher debt-deficit sensitivities indicating that mature firms followed the

pecking order more closely. This was contrary to conventional wisdom since they would expect

growth firms to suffer more from information asymmetry problems. Bulan and Yan (2009)

documented this result as a maturity effect in firm financing choice. Mature firms were older,

more stable, and highly profitable with few growth opportunities and good credit histories. Hence,

mature firms were able to borrow more easily and at a lower cost. Therefore, by the very nature of

their life cycle stage, mature firms were pre-disposed to utilizing debt financing first before equity.

Bulan and Yan (2007) studied firms‟ financing behaviour over life cycle stages in the

context of the pecking order theory. They classified firms into two life cycle stages, namely

growth and maturity, and tested the pecking order theory of financing proposed by Myers (1984)

and Myers and Maljuf (1984). They used two different empirical frameworks: the Shyam-Sunder

and Myers (1999) model and the Leary and Roberts (2006) model. Under both specifications, they

identified two effects: a size effect and a maturity effect.

The size effect was consistent with Frank and Goyal (2003), who found that large firms

fitted the pecking order theory better than of small firms, contrary to the predictions of the theory.

However, Bulan and Yan (2007) found that this size effect existed only among firms in their

growth stage. For firms in their mature stage, this size effect was not significant.

When controlling for a firm‟s debt capacity, this size effect disappears altogether, while

the maturity effect remains. Overall, Bulan and Yan (2007) found that the pecking order theory

described the financing patterns of mature firms better than of growth firms. This is contrary to the

theory‟s prediction that firms with the greatest information asymmetry problems (specifically

74

young, growth firms) are precisely those that should be making financing choices according to the

pecking order. In general, the major difference between mature and young firms is not that mature

firms are larger, but because they are more “mature.” Mature firms are older, more stable, higher

profitable with few growth opportunities and good credit histories. They are thus more suited to

use internal funds first, and then debt before equity for their financing needs. These results are

robust under alternative empirical models for testing the pecking order theory.

Bulan and Yan (2007) further saw that growth firms had larger financing deficits, as

expected. The financing deficit is defined as the uses of funds minus internal sources of funds,

which, by an accounting identity, is also the sum of net debt issued and net equity issued. There

seems to be no difference in net debt issued between the two cohorts, while net equity issued is

larger for the growth firms. From this simple comparison, the evidence seems to suggest growth

firms rely more heavily on equity financing rather than debt. This finding is consistent with Agca

and Mozumdar (2004) and Lemmon and Zender (2004).

Overall, Bulan and Yan (2007) found that the pecking order theory described the

financing patterns of mature firms better than that of younger growth firms. Older and more

mature firms are more closely followed by analysts and are better known to investors, and hence,

should suffer less from problems of information asymmetry. Furthermore, mature firms generally

have more internal funds due to higher profitability and lower growth opportunities. Hence, their

findings suggest that it is firm maturation, and not adverse selection, that motivates pecking order

behaviour. Older, more stable and highly profitable firms with few growth opportunities and good

credit histories are more suited to use internal funds first, and then debt before equity for their

financing needs.

Halov and Heider‟s (2003) starting point for the analysis was the empirical puzzle that the

pecking order seems to work well when it should not, i.e., for large mature firms, and seems not to

work well when it should, i.e., for small young non-payers of dividends. They argued that the

original pecking order was based on the mis-pricing of equity caused by not knowing the value of

investments. But when outside investors also do not know the risk of investments, then debt is

mis-priced, too. They argued that asymmetric information about both, value and risk, transformed

the adverse selection logic into a theory of debt and equity.

Their main hypothesis was that firms issued more equity and less debt in situations where

risk was an important element of the adverse selection problem of outside financing. They found

robust empirical support for the hypothesis and document a strong link between asset risk and the

decision to issue debt and equity in a large unbalanced panel of publicly traded US firms from

1971 to 2001.

While Frank and Goyal expected the pecking order to work best for young, small firms

since they argued that these firms should have the most severe asymmetric information problem,

Halov and Heider (2003) explained that the standard pecking order should not work at all for

young, small firms. Risk differences, i.e., differences in failure rates and upside potential play an

important role in the adverse selection problem for young, small firms. Hence, they should issue

equity and not debt, or alternatively, rational investors demand equity and not debt from these

firms.

Suarez (2005) study concluded that, the pecking order‟s high explanatory power could be

the result of sample bias towards large and mature firms. This implies that a sample of smaller

growth firms may not provide the good fit required to establish statistical power to the pecking

order specification. He explained that it has been observed that even small growth firms that had

75

the ability to issue default free debt or venture capital (close ties with local banks) were

characterized by very low levels of debt (even zero) and high levels of equity financing. He added

that it would be interesting to carry out similar procedures with these models using a different firm

sample (i.e. composed of small venture capital firms) to then see if the pecking order model stood

the test.

Frank and Goyal (2003) examined the broad applicability of the pecking order theory.

Their evidence based on a large cross-section of US publicly traded firms over long time periods,

showed that external financing was heavily used by some firms. On average net equity issues track

the financing deficit more closely than do net debt issues.

These facts do not match the claims of the pecking order theory. Frank and Goyal (2003)

evidenced greatest support for pecking order that was found among large firms, which might be

expected to face the least severe adverse selection problem since they received much better

coverage by equity analysts. According to them, even here, the support for pecking order was

declining over time and the support for pecking order among large firms was weaker in the 1990s.

They concluded that the pecking order theory did not explain broad patterns in the data.

Therefore, we hypothesise that,

Hypothesis 4 : In the context of firm’s life cycle, we expect that growth [and small] firms

follow the pecking order theory more closely than mature [and large] firms.

76

5. RESEARCH METHODOLOGY

5.1 Research Design

The objectives of this research are to investigate the determinants of capital structure of

the firms in the manufacturing sector in Indonesian capital market, to analyse how firms in the

manufacturing sector raise capital for investments, internally or externally (with debt, equity, or

debt to repurchase equity), to examine if debt policy does matter, what will happen to the firms‟s

stock price if firms issue new debt, issue new equity, or issue debt to repurchase equity, and to

examine in the context of firm‟s life cycle, can we expect that growth-small-young firms follow

the pecking order more closely. The study is using a combination of quantitative and qualitative

approaches or strategies. The dominant strategy is quantitative. The process of the research is

described as follows:

Figure 5.1. Research Process

Source: Mark Saunders, Phillip Lewis, and Adrian Thornhill (2003)

In Mark Saunders, Phillip Lewis, and Adrian Thornhill (2003), the research process

consists of 9 steps. In this research, we add an overview of capital structure of Indonesian

manufacturing firms between step 1 and step 2.

Step 1: We formulate and clarify the research topic, it is written to assist us in the generation of

ideas, which will help to choose a suitable research topic, and offers advice on what makes a good

Wish to do

research

Formulate and clarify

research topic

Research methodology,

choose research

approach and strategy

Critically review the

literature

Plan data

collection

and collect

the data

Write project report

and prepare

presentation

Analyse data using both

of quantitative and

qualitative method

Submit report and give presentation

Conceptual framework

and hypotheses

formulation

77

research topic. As soon as we have found a research topic, we refine it into one that is feasible.

After the idea has been generated and refined, we turn this idea into clear research questions and

objectives. This step is applied in chapter 1.

Step 2: We reviewed some critical literature to outline what to include and decided on the range of

primary, secondary and tertiary literature sources available. This step is applied in chapter 3.

Step 3: At this step, we wrote the conceptual framework and the hypotheses formulation by

analysing capital structure theories and some previous research. This step is applied in chapter 4.

Step 4: We worked on the research methodology, research approach and the strategy. A clear

research strategy is crucial because the credibility of research findings and conclusions depend on

it.

Step 5: At step five, we plan data collection which is concerned with different methods of

obtaining data.

Step 6: At this stage we analyse data using both of quantitative and qualitative method, outlines,

and discusses the main approaches available to analyse data quantitatively. Steps 4, 5, and 6 are

applied in chapter 5.

Step 7: In this chapter, we write the project report and the prepare presentation with the structure,

content and style of final project report and any associated oral presentations. This step is applied

in chapter 6.

Step 8: After we finish all of the earlier steps of the research process, we hope we will submit the

research report (the thesis) and give presentation in time.

5.2Research Strategy

In this research, our research strategy for hypotheses 1, 3, and 4 is quantitative strategy,

while for hypothesis 2 we apply both quantitative and qualitative research strategy. The following

is its analysis.

5.2.1. Quantitative Strategy

In this study, we have used quantitative and combination of quantitative and qualitative

approaches or strategies as all research methods have limitations. One method can be nested

within another method to provide insight into different levels or units of analysis (Tashakkori and

Teddlie, 1998).

A quantitative approach is one in which the investigator primarily uses post-positivist

claims for developing knowledge (cause and effect thinking, reduction to specific variables and

hypotheses and questions, use of measurement and observation, and the test of theories), employs

strategies of inquiry such as experiments and surveys, and collects data on predetermined

instruments that yield statistical data (Creswell, 2003). Therefore, the following hypotheses are

treated with using quantitative approach:

Hypotheses 1a: “As implied by the trade-off theory and the pecking order theory, we hypothesise

that growth opportunity is positively related to debt ratios”.

78

Hypotheses 1b: “As in the pecking order hypothesis, we hypothesise that profitability has a

negative relationship with debt ratios and based on the trade-off theory we hypothesise that

profitability has a positive relationship with debt ratio”.

Hypotheses 1c: “In accordance with the pecking order theory and trade-off theory, we hypothesise

a negative relationship between risk (earnings volatility) and debt ratio”.

Hypotheses 1d: “As suggested by the trade-off theory, we hypothesise that size has a positive

relationship with debt ratio, and as suggested by the pecking order theory of the capital structure

there is a negative relationship between debt ratio and size.

Hypotheses 1e: “In accordance with the trade-off theory, we hypothesise a positive relationship

between asset tangibility and debt ratio.

Hypotheses 3:

(a) If firms issue new debt, then the firms‟s stock price will be higher.

(b) If firms issue new equity, then the firms‟s stock price will be lower.

(c) If firms issue debt to repurchase equity, then the firms‟s stock price will be higher.

Hypothesis 4:

In the context of firm‟s life cycle, we expect that growth [and small] firms follow the

pecking order theory more closely than mature [and large] firms.

5.2.2. Mixed Method Strategy

The following hypothesis 2 is analysed by using the mixed method approach to make

more in-depth analysis of our results. The dominant strategy used is quantitative strategy.

Hypotheses 2: Firms in the manufacturing sector raise capital for investments externally (with

debt, equity, or debt to repurchase equity).

5.3 Data Collection

Research samples that we used were all manufacturing companies which incorporated in

the LQ45 Index, one of the index in Indonesian Stock Exchange. LQ45 consists of 45 companies

with large capitalisation value. Therefore, our research samples are 26 manufacturing companies

in the LQ index from the years 1994 to 2007.

We collected the data from the book of data published by IDX. The book of data consists

of financial statement of each firm.

79

Figure 5.2. Data Analysis and Collection

Source: Creswell et al. (2003)

For hypothesis 2, we used the combination of quantitative and qualitative research

strategy; our strategy priority was quantitative. Therefore, we only collected the quantitative data,

followed by quantitative data analysis, qualitative data analysis, and interpretation of entire

analysis.

For hypotheses 1, 3, and 4, we used quantitative research strategy. Hence, we applied

quantitative data collection followed by quantitative data analysis.

5.4. Sampling Design and Procedure

We took all firms of the manufacturing sector in LQ45 during the period of 1994 to 2007.

Then we got 26 manufacturing firms as our sample. The following figure 5.3 describes our

sampling design.

Figure 5.3. Sampling Design

The JSX LQ45 Index was created to provide the market with an index that represented 45

of the most liquid stocks. To date, the LQ45 Index covers at least 70% of market capitalisation and

transaction values in the regular market. The LQ45 Index historical calculation was defined at July

LQ45 Index

from the Year 1994 to the Year 2007

Non-Manufacturing Sector

Manufacturing Sector

QUAN

Data

Collection

QUAL

Data

Collectio

n

Interpretation of Entire Analysis

QUAL

Data

Analysis

QUAN

Data

Analysis

Qualitative (QUAL)

Quantitative (QUAN)

80

13, 1994, with a base value of 100. The index consists of 45 stocks that have passed the liquidity

and market capitalisation screenings.

Table 5.1. Research Samples

Manufacturing Firms

ASII GJTL KAEF

AUTO HMSP RMBA

ADMG INDF SMCB

BRPT INDR SMGR

BUDI INKP TKIM

CPIN INAF TSPC

DNKS INTP UNVR

FASW KLBF SULI

GGRM KOMI

The firms in LQ45 index during that period we reviewed every 3 months and they could

still stay in the list or be crossed out of the list. Hence, within sampling period, we got 26

manufacturing firms sample as shown in table above.

5.5. Variables Measurement

Tested variables in our research were leverage, growth opportunity, profitability, risk,

size, asset tangibility for H1, net debt issue, net equity issue, newly retained earning, and financing

deficit for H2 and H4, and net debt issue, net equity issue, newly retained earning, and stock price

for H3. The following are the measurements of the research variables.

5.5.1. Variable of Hypothesis 1

Our research variables of hypotheses 1 are including total leverage, short-term leverage,

long-term leverage, and market leverage as dependent variables, while growth opportunity,

profitability, risk, size, and asset tangibility as independent variables. The following sub-section is

the describtion of how we measure the variables.

A. Leverage

The leverage of a firm can be measured by many different variables. For instance, Pandey

(2001) measured leverage as market value of long term debt to total asset, market value of short

term debt to total asset, market value of total debt to total asset, book value of long term debt to

total asset, book value of short term debt to total asset, and book value of total debt to total asset.

Chen and Hammes (2003) measured leverage as book capital ratio, and market capital ratio as

primary measures of leverage, where market capital ratio was market capitalisation replacing the

book equity. They used book debt ratio (total debt to total asset) as a secondary measure.

We choose four debt ratios in this study. These are total leverage, short-term leverage,

long-term leverage, and market leverage. These measures of debt ratios examine the capital

employed and thus represent the effects of past financing decisions best.

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Our measurement of book leverage is as measured by Rajan and Zingales (1995), Leary

and Roberts (2005), and Sbeiti (2010), and of market leverage is as measured by Bulan and Yan

(2009).

TLV=TD/TA

MRL = (TLV) / (TA+MV of Equity-TE)

Where TLV is total leverage, MRL is market leverage, TA is total asset, MV of equity is market

value of equity, and TE is total equity.

B. Growth Opportunities

The growth potential of a firm can be measured by many different variables. Rajan and

Zingales (1995) measured growth as Tobin‟s Q, Laarni Bulan and Zhipeng Yan (2009) measured

growth as market-to-book ratio as market equity/book equity, and Akhtar and Oliver (2006)

defined it as the average percentage change in total assets over the previous four years.

Chen and Hammes (2003), Leary and Roberts (2005), and Sbeiti (2010) measured growth

opportunities as the ratio of market value of assets (book value of assets plus market value of

equity less book value of equity) to book value of assets.

We measure growth opportunities as:

Growth = the ratio of market value of assets (book value of assets plus market value of

equity less book value of equity) to book value of assets.

C. Profitability

Profitability plays an important role in leverage decisions. Profitability is proxied by

return on assets. ROA represents the contribution of the firm‟s assets on profitability creation.

Profitability is a measure of earning power of a firm. The earning power of a firm is generally the

basic concern of its shareholders.

Akhtar and Oliver (2006) measured profitability as the average net income to total sales

for the past four years. Wafaa Sbeiti (2010) measured profitability as the ratio of operating profit

to book value of total assets. Titman and Wessels (1988), Drobetz and Fix (2003) measured it as

the ratio of operating income over total assets (ROA) and the ratio of operating income over sales.

Chen and Hammes (2003), Rajan and Zingales (1995), Abimbola Adedeji, Francisco

Sogorb-Mira y José López-Gracia (2003) measured profitability as earnings before interest and

taxes divided by total asset.

We measure profitability as:

Profitability = earnings before interest and taxes divided by total asset.

D. Risk

Earnings volatility measures the variability of the firm's cash flows as a proxy for the

costs of monitoring managers and of the risk of an insider's position. The use of longer time

periods causes a significant loss of the sample size.

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Several measures of volatility were used in different studies, such as Bradley, Jarrell and

Kim (1984), Drobetz and Fix (2003) used variability as the standard deviation of the first

difference in annual earnings, scaled by the average value of the firm‟s total assets over time,

Booth et al. , (2001) the standard deviation of the return on sales.

Leary and Roberts (2005) measured cash flow volatility as the standard deviation of

earnings before interest and taxes, however they were based on (up to) the previous 10 years of

data for a given firm-year observation while we were up to the previous 3 years of data for a given

firm-year observation.

Risk = coefficient of variation in earnings before interest and taxes (EBIT) over three years.

E. Size



debt markets or the extent their assets are diversified).

Titman and Wessels (1988) and Drobetz and Fix (2003) measured firm size as the natural

logarithm of net sales. Chen and Hammes (2003) measured firm size as in Rajan and Zingales

(1995) that is the natural logarithm of total turnover.

Akhtar and Oliver (2006), Leary and Roberts (2005), Francisco Sogorb-Mira y José López-Gracia

(2003), and Sbeiti (2010) measured size as the natural logarithm of total assets.

Size = the natural logarithm of total assets.

F. Tangibility

The tangibility of assets represents the effect of the collateral value of assets of the firm‟s

gearing level. There are various conceptions for the effect of tangibility on leverage decisions. If

debt can be secured against assets, the borrower is restricted to using debt funds for specific

projects. Creditors have an improved guarantee of repayment, but without collateralised assets,

such a guarantee does not exist.

Leary and Roberts (2005), Bulan and Yan (2009) measured tangibility as net property,

plant and equipment divided by total assets. Huang and Song, Drobetz and Fix (2003), Abimbola

Adedeji, Dilek Teker,Ozlem Tasseven, and Ayca Tukel (2009) measured tangibility as fixed assets

divided by total assets.

Tangibility = fixed assets divided by total assets.

5.5.2 Measuring Variables of Hypotheses 2, 3, and 4

A. Financing Deficits

Bharath, Pasquariello, and Wu (2008) measure firms‟ financing deficits, dividends,

investments, and cash flow separately. Frank and Goyal (2003) measure deficit as dividend plus

investment and cashflow. Meanwhile, investment measured as capital expenditure and working

capital to capture a firm‟s demand for funds due to its real investments. Bulan and Yan (2009)

measure deficit as the financing deficit in period t scaled by total assets at the beginning of period

t, financing deficit as net equity plus net debt issues, and capital expenditures as capital

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expenditures divided by total assets. Frank and Goyal (2007) measure the deficit as cash dividends

plus investments plus change in working capital minus internal cash flow.

Sogorb-Mira and López-Gracia measured the financing deficit would be as ∆ Fixed

current investment as the sum of capital expenditures, increase in investments, acquisitions, and

other use of funds, less sale of plant, property, and equpment and sale of investment. Cash flow

defined as cash flow after interest and taxes net of dividends, respectively.

Financing Deficit = DIV + CAPEX + LTD payment + Δ WC – CF

in which DIV is dividend payments, CAPEX is capital expenditures, ΔWC is the net change in

working capital, and CF is operating cash flow (after interest and taxes), long-term debt payment.

All variables are scaled by total assets, as in Frank and Goyal (2003).

B. Net Debt Issue

Leary and Roberts (2005) measure debt issuances as a change in total debt (long term

plus short term) divided by total assets in an excess of 5%. Frank and Goyal (2007) net debt issued

as long-term debt issuance minus long-term debt redemption. Bulan and Yan (2009) measure net

debt issued scaled by total assets, or net debt [long-term debt issuance minus long-term debt

reduction divided by total assets.

Net debt issue = (dTA/TA) - (Net equity issue) – (dRE/TA)

Where TA is total asset, dTA is change in total asset, and dRE is change in retained earning.

C. Net Equity Issue

Leary and Roberts (2005) measured equity issuances for year t as sale of common and

preferred stock net of purchase of common and preferred stock. Frank and Goyal (2007) measured

net equity issued as the issue of stock minus the repurchase of stock. Bulan and Yan (2009)

measured net equity as sale of common and preferred stock minus purchase of common and

preferred stock divided by total assets..

Net equity issue = (dEq/TA) - (dRE/TA), and

NRE = dRE/TA

Where TA is total asset, dEq is change in book equity, NRE is newly retained earning, and dRE is

change in retained earning.

5.6. Hypotheses Testing

We have tested hypotheses 1-4 using regression. We used this statistical technique as we

explored linear relationships between the predictor and criterion variables. The criterion variable

and the predictor variable we used for making a prediction should be measured on a continuous

scale (ratio scale). We also tested H2 by using an augmented model.

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5.6.1. Hypothesis 1

The objective of testing hypothesis 1 is to examine the influence of growth opportunity,

profitability, risk, size, and asset tangibility on short-term leverage, long-term leverage, total

leverage, and market leverage.

The regression equation for hypotheses 1a, 1b, 1c, 1d, 1e, 1f, is as follows:

Y = a + β1 * X1 + β2 * X2 + β3 * X3 + β4 * X4 + β5 * X5 + e

Where:

Y = is the value of the dependent variable, Debt ratio

a = is the intercept of the regression line on the Y axis when X= 0

β = is the slope of the regression line

X1 = Growth opportunity

X2 = Profitability

X3 = Risk

X4 = Size

X5 = Asset tangibility

5.6.2. Hypothesis 2

The objective of testing hypothesis 2 is to examine how firms in the manufacturing sector

raise capital for investments externally (with debt, equity, or debt to repurchase equity).

Hypothesis 2 was analysed by using the mixed method approach.

A. Quantitative Analysis

For testing hypothesis 2, the independent variable was financing deficit, and net debt

issue, net equity issue, and net debt issue to repurchase equity were the dependent variables.

Therefore, the steps to analyse the relationship between variables are as follows:

A.1. Measuring the Financing Deficit/Surplus

The financing deficit would be approximated as:

FINANCING DEFICIT = DIV + CAPEX + ΔWC + LTD payment − CF

In which DIV is dividend payments, CAPEX is capital expenditures, ΔWC is the net change in

working capital, and CF is operating cash flow (after interest and taxes), LTD payment is long-

term debt payment. All variables are scaled by total assets, as in Frank and Goyal (2003).

A positive value of financing deficit indicates a financing deficit and a negative one

indicates financing surplus. The financing deficit/surplus in equation is equivalent to the one used

in previous studies.

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A.2. Testing the Pecking Order Theory

In Bulan and Yan (2007), the pecking order theory of Myers and Majluf (1984) and

Myers (1984)) and its extensions (Lucas and McDonald (1990)) is based on the idea of

asymmetric information between managers and investors. Managers know more about the true

value of the firm and the firm‟s riskiness than less informed outside investors. If the information

asymmetry causes the underpricing of the firm‟s equity and the firm is required to finance a new

project by issuing equity, the underpricing may be so severe that new investors accept the largest

part of the net present value of the project, resulting in a net loss to existing shareholders. Thus,

managers who work in the greatest interest of the current shareholders will reject the project. To

avoid the underinvestment problem, managers will search for financing the new project using a

security that is not undervalued in the market, such as internal funds.

Consequently, this affects the choice between internal and external financing. The

pecking order theory is capable to explain why firms tend to depend on internal sources of funds

and prefer debt to equity if external financing is required. Thus, a firm‟s leverage is simply the

cumulative results of the firm‟s attempts to mitigate information asymmetry. Due to the valuation

discount that less-informed investors apply to newly issued securities, so firms choose internal

funds first, then debt and equity last to satisfy their financing needs (Bulan and Yan, 2007).

In this section, we implement a test of the pecking order theory proposed by Shyam-

Sunder and Myers (1999) given by the following:

Net Debt Issue = a + b1 * Deficit + ε

Where net debt issued and financing deficit, i.e. uses of funds minus internal sources of funds,

(both scaled by total assets). This deficit is financed with debt and/or equity. If firms are consistent

with the pecking order, changes in debt should track changes in the deficit one-for-one. Hence, the

expected coefficient on the deficit is 1. Frank and Goyal (2003) showed that this test performed

poorly for small firms and performed best for large firms. However, since small firms were

thought to suffer most from asymmetric information problems, hence they should be the ones

following the pecking order.

A.3. Testing the Pecking Order and Debt Capacity with an Augmented Model

In Laarni Bulan Zhipeng Yan (2007, as an alternative means of accounting for a firm‟s

debt capacity, Lemmon and Zender (2007) and Agca and Mozumdar (2004) augmented equation

with the deficit-squared:

Net Debt Issue = a + b1 * Deficit + b2 * Deficit2 + ε

To estimate equation, we follow Bulan and Yan (2009). Firms that in accordance with the

pecking order more strongly should have a debt-deficit sensitivity that is closer to one. The

quadratic specification was used to account for requiring debt capacity constraints. This deficit is

financed with debt and/or equity. If firms follow the pecking order, variations in debt should

follow changes in the deficit one-for-one (Shyam-Sunder and Myers, 1999). If firms are financing

their deficit with debt first and issue equity only when they achieve their debt capacities, then net

debt issued is a concave function of the deficit (Chirinko and Singha, 2000) and the coefficient on

the squared deficit term would be negative. The larger the deficit, the more probably it is for a firm

to attain its debt capacity. In these instances, the debt-deficit sensitivity should be lower. A

negative coefficient on the squared deficit term implies that firms are limited by their debt capacity

inadequacy and they have to choice to issuing equity. A squared deficit coefficient that is large in

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absolute value describes a greater reliance on equity finance for larger values of the financing

deficit. If firms are issuing equity first and debt is the residual source of financing, then this

relationship should be convex and the coefficient on the squared deficit term would be positive. If

debt and equity are issued in static proportions, the deficit would have no influence on net debt

issued.

B. Qualitative Analysis

To make sure that our regression result is robust, we also analyse the results qualitatively

by using graphics and table analysis.

5.6.3. Hypothesis 3

The objective of testing H3 is to test the effect of issuing net debt, issuing net equity, and

issuing net debt to repurchase equity, on the firm‟s stock price. The regression equation for

hypothesis 3a, 3b, and 3c are as follow:

Y1 = a + β1 * X1 + e

Y2 = a + β2 * X2 + e

Y3 = a + β3 * X3 + e

Where:

Y = stock price

X1 = net debt issue

X2 = net equity issue

X3 = debt issue to repurchase equity

a = is the intercept of the regression line on the Y axis when X=0

β = is the slope of the regression line

5.6.4. Hypothesis 4

The objective of testing hypothesis 4 is to examine the firm‟s capital structure over the

life cycle of the firm to solve the problem of firm financing deficit. In testing the hypothesis, we

first classified firms into two cohorts according to their life cycle stage, namely, firms in their

growth stage and firms in their mature stage. Then we classified firms into small firms and large

firms, and additionally young firms and old firms.

Since we would like to examine how growth-mature firms and small-large firms finance

their deficit, hence, it is important to make sub distinctions in the theoretical framework between

growth-mature firms and small-large firms. Maturity and size can be regarded as a proxy for

information asymmetry between firm insiders and the capital markets. Mature [large] firms are

more closely observed by analysts and should therefore be more capable of issuing more equity,

and have lower debt (e.g., their reputation in debt markets or the extent their assets are diversified).

Growth [small] firms are on the other hand. Therefore, it is important to make sub distinctions in

the theoretical framework between growth-mature firms and small-large firms.

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In the context of a firm‟s life cycle, we expected that asymmetric information problems

were more severe among growth [small] firms compared to firms that have reached maturity.

Hence, the theory predicts that fast-growing [smaller] firms should be following the pecking order

more closely.

Life Cycle Definition

Bulan and Yan (2009) defined the growth stage as the first six-year period after the year

of the firm‟s initial public offering (IPO). This definition may not necessarily apply to some firms

from a mechanical point of view. However, the IPO itself is an important financing decision that a

firm has to make. Here, Bulan and Yan (2009) treated the IPO as the starting point of the growth

stage (or the “new growth” stage).

DeAngelo, DeAngelo and Stulz (2006), among others, found that a firm‟s propensity to

pay dividends was a function of the stage where the firm is in its life cycle. In particular, Bulan,

Subramanian and Tanlu (2007) found that dividend initiators were mature firms. Based on this

body of work, they identified firms in their mature stage by their dividend initiation history. First,

they used the entire compustat industrial annual database to find consecutive six-year periods for

which a firm has positive dividends. They required that such a period should immediately follow

at least one year with zero or missing dividends. They considered these 6-year dividend payment

periods as the mature stage of a firm‟s life cycle.

1. Growth Firms and Mature Firms

We took Grullon, Michaely and Swaminathan (2000), DeAngelo, DeAngelo and Stulz

(2005) and Bulan, Subramanian and Tanlu as the references (2007) who found that firms initiated

dividends were mature firms. Thus, we identified firms in their mature stage by their dividend

history. Halov and Heider (2005), Leary and Roberts (2006) and Byoun (2007) showed that firm

financing choice was complex and was driven by many factors which included both pecking order

and trade-off theory considerations.

We constructed two samples of firms according to their life cycle stage: firms in their

growth stage and firms in their mature stage. Bulan and Yan (2007) set the length of each stage to

be 6 years. Evans (1987) defined six years old or younger as young firms and seven years or older

as old firms. We followed Bulan and Yan (2007) to set the length of each stage to be 6 years.

Growth Stage

Our sample was constructed from the manufacturing sector of the LQ45 index over the

1994- 2007 period. Some previous research defined the growth stage to be the first six-year period

after the year of the firm‟s IPO, however we defined the growth stage to be the firms that paid

dividend less then 5 years sequencialy.

Mature Stage

Bulan, Subramanian and Tanlu (2007) found that firms initiated dividends are mature

firms. Thus Bulan and Yan (2007) identified firms in their mature stage by their dividend history.

We took Bulan and Yan (2007) as a reference to construct the sample as follows: we included the

former 6-year period in our sample. This 10-year requirement was to ensure that whatever reason

for the dividend omission, the firm had fully recovered and re-emerged as a regular dividend

payer. We consider these 6-year dividends payment periods as the mature stage of a firm‟s life

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cycle. We found that 10 firms had one 6-year dividend payment period; while 16 firms had less

than one 6-year dividend payment periods among the 26 firms.

Table 5.2. Growth Firms

No. Firm Life Cycle

1 ADMG Growth

2 BRPT Growth

3 BUDI Growth

4 CPIN Growth

5 DNKS Growth

6 FASW Growth

7 GJTL Growth

8 INDR Growth

9 INKP Growth

10 INAF Growth

11 INTP Growth

12 KOMI Growth

13 SMCB Growth

14 TKIM Growth

15 TSPC Growth

16 SULI Growth

Table 5.3. Mature Firms

No. Firm Life Cycle

1 ASII Mature

2 AUTO Mature

3 GGRM Mature

4 HMSP Mature

5 INDF Mature

6 KAEF Mature

7 KLBF Mature

8 RMBA Mature

9 SMGR Mature

10 UNVR Mature

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2. Small Firms and Large Firms

In Hufft, JR. study defined small as firms with less than 500 employees, total assets of

less than $150 million, and annual sales of less than $20 million. Then we adopted it to define

large firms that have total asset of more than $150 millions (equals to IDR 1,083,952.65 million).

Table 5.4. Small Firms

Firms USD 150 million

(equals to IDR

1,083,952.65 million)

Total

Asset<$150

millions

BUDI 496,726.67 Small

DNKS 377,072.67 Small

INAF 549,373.33 Small

KOMI 323,486.67 Small

KAEF 914,511.60 Small

RMBA 946,449.00 Small

TSPC 1,056,275.78 Small

Table 5.5 Large Firms

Firms USD 150 million

(equals to IDR

1,083,952.65 million)

Total Asset >

USD 150

millions

ASII 30,934,935.64 Large

ADMG 6,191,532.33 Large

AUTO 1,347,317.50 Large

BRPT 4,107,897.43 Large

CPIN 1,995,001.00 Large

FASW 1,811,608.00 Large

GGRM 10,846,690.67 Large

GJTL 9,353,014.00 Large

HMSP 5,418,818.33 Large

INDF 11,630,675.64 Large

INDR 3,472,316.56 Large

INKP 38,541,160.07 Large

INTP 6,510,362.43 Large

KLBF 2,564,165.14 Large

SMCB 6,335,029.07 Large

SMGR 5,729,074.22 Large

SULI 1,401,294.80 Large

TKIM 14,313,941.33 Large

UNVR 2,996,968.27 Large

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3. Young Firms and Old Firms

Evans (1987) defined six years old firms or younger as young firms and seven years firms

or older as old firms. We followed this study and set the length of each stage to be 7 years

(however, this restriction is not true for some firms from a manufacturing point of view). To take

an example, KAEF was founded in 1969 and went public also in 2001. This firm is “old” enough

and is mature in many respects. However, the IPO itself is an important financing decision that a

firm has to make, and in many cases, indicates a significant change in the firm‟s development over

its life cycle. Here, we treated the IPO as an important turning point in a firm‟s history and as the

starting point of the old/young stage. Table showed that INAF and KAEF are 6 years from listing

date to 2007 as our sampling period from 1994 to 2007.

Table 5.6. Young and Old Firms

Established Listed How Old Listed in IDX (no.

of years from

listed to 2007)

ASII 20 Feb 1957 04 Apr 1990 50 17

AUTO 04 Apr 1979 01 Okt 1993 28 14

ADMG 25-Apr-1986 20-Oct-1993 21 14

BRPT 04-Apr-1979 01-Oct-1993 28 14

BUDI 15-Jan-1979 08-May-1995 28 12

CPIN 07 Jan 1972 18 Mar 1991 35 16

DNKS 25-Mar-1974 13-11-1989 33 18

FASW 13-Jun 87 19-Dec 1994 20 13

GGRM 26-Jun 1958 27-Aug 1990 49 17

GJTL 24-Aug 1951 08-May 1990 56 17

HMSP 27-Mar-1905 15-Aug-1990 102 17

INDF 14-Aug-1990 14-Jul-1994 17 13

INDR 03-Apr-1974 03-Aug-1990 33 17

INKP 07-Dec-1976 16-Jul-1990 31 17

INAF 02-Jan-1996 17-Apr-2001 11 6

INTP 16 Jan 1985 05 Des 1989 22 18

KLBF 10-Sep-1966 30-Jul-1991 41 16

KOMI 13-Dec-1982 31-Oct-1995 25 12

KAEF 23-Jan-1969 04-Jul-2001 38 6

RMBA 19-Jan-1979 05-Mar-1990 28 17

SMCB 15-Jun-1971 10-Aug-1977 36 30

SMGR 25-Mar-1953 08-Jul-1991 54 16

TKIM 02-Oct-1972 03-Apr-1990 35 17

TSPC 20-May-1970 17-Jun-1994 37 13

UNVR 05-Dec-1933 11-Jan-1982 74 25

SULI 14-Apr-1980 21-Mar-1994 27 13

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As the equations applied in hypothesis 2, we also tested hypothesis 4 by following a test

of the pecking order theory proposed by Shyam-Sunder and Myers (1999) over the life cycle of the

firm.

5.7. Regression Analysis

Our regression analysis consists of the un-standardised Beta coefficients, the standardised

Beta coefficients, analysis of variance (ANOVA), coefficients of determination (R2), descriptive

statistics, and regression assumptions of hypotheses 1-4.

A. The Un-standardised Beta Coefficients

The Un-standardised Beta Coefficients (B) is the value for the regression equation for

predicting the dependent variable from the independent variable. These are called un-standardised

coefficients because they are measured in their natural units. As such, the coefficients cannot be

compared with one another to determine which one is more influential in the model, because they

can be measured on different scales.

B. The Standardised Beta Coefficients

The Standardised Beta coefficients give a measure of the contribution of each variable to

the model. A large value indicates that a unit change in this predictor variable has a large effect on

the criterion variable. The t and sig (p) values give a rough indication of the impact of each

predictor variable a big absolute t value and small p value suggests that a predictor variable is

having a large impact on the criterion variable.

When we have only one predictor variable in our model, then beta is equivalent to the

correlation coefficient between the predictor and the criterion variable. This equivalence makes

sense, as this situation is a correlation between two variables.

C. Analysis of Variance (ANOVA)

Analysis of variance enables an extrapolation of the t test results of two groups to three or

more groups. The F-statistic will be calculated for analysis of variance (ANOVA) to test whether

group population means are all equal or not. When the F-statistic is found significant, we may

conclude that at least one of the population means of the groups differs from the others, but

ANOVA does not tell us which groups are different from which. If this is the case, a multiple-

comparison analysis by pairwise group comparison will be an appropriate answer to this question

(Bekiro, 2001).

The statistical significance as depicted in the ANOVA analysis of the models for firms

reach statistical significance at significance value of p<0.05 (Coakes and Steed, 2003; and Pallant,

2005).

D. The Coefficient of Determination (R2)

The multiple correlation coefficients (R) are the linear correlation between the model-

predicted and the observed values of the dependent variable. The coefficient of determination, or

simply R-squared, has its value always between 0 and 1, and is interpreted as the percentage of

variation of the response variables explained by the regression line. If there is no linear relation

between the dependent and independent variable, R2

is 0 or very small. If all the observations fall

on the regression line, R2

is 1. This measure of the goodness of fit of a linear model is also called

the coefficient of determination. The sample estimate of R2

tends to be an optimistic estimate of

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the population value. Adjusted R Square is designed to more closely reflect how well the model

fits the population and is usually of interest for models with more than one predictor.

A high value of R2, suggesting that the regression model explains the variation in the

dependent variable well, is obviously important if one wishes to use the model for predictive or

forecasting purposes. To be sure, a large unexplained variation in the dependent variable will

increase the standard error of the coefficients in the model (which are a function of the estimated

variance of the noise term), and hence regressions with low values of R2 will often (but by no

means always) yield parameter estimates with small t-statistics for any null hypothesis. Because

this consequence of a low R2 will be reflected in the t-statistics, however, it does not afford any

reason to be concerned about a low R2 per se. R Square (R

2) is the square of the measure of

correlation and indicates the proportion of the variance in the criterion variable which is accounted

for by our model.

E. Descriptive Statistics

Descriptive statistics describe the value of each variable including mean, minimum, and

maximum values.

F. Regression Assumptions of Hypothesis 1-4

Before analyzing regression coefficients of variables, we must first make several

assumptions about the population of the research. They represent an idealisation of reality, and as

such, they are never likely to be entirely satisfied for the population in any real study (Van Horne,

1998). A good regression model should not have the following assumptions:

1. Multicollinearity

Multicollinearity implies that for some set of explanatory variables, there is an exact

linear relationship in the population between the means of the response variable and the values of

the explanatory variables (Van Horne, 1998). The goal of the multicollinearity test is to analyse

whether there is correlation between independent variables.

Multicollinearity in the regression model can be detected such as by testing the R2 value

and/or analysing the correlation matrix (Ghozali, 2002). The other ways to detect the problem of

multicollinearity are the tolerance values and VIF (Hair et al., 1998).

Correlations between Variables

For correlations between variables, we do not want strong correlations between the

criterion and the predictor variables.

The Tolerance and VIF

The tolerance values are a measure of the correlation between the predictor variables and

can vary between 0 and 1. The closer to zero the tolerance value is for a variable, the stronger the

relationship between this and the other predictor variables. We should worry about variables that

have a very low tolerance (Van Horne, 1998). SPSS will not include a predictor variable in a

model if it has a tolerance of less that 0.0001. However, we may want to set your own criteria

rather higher – perhaps excluding any variable that has a tolerance level of less than 0.01.

Meanwhile, VIF is an alternative measure of collinearity (in fact it is the reciprocal of

tolerance) in which a large value indicates a strong relationship between predictor variables.

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2. Autocorrelation

Autocorrelation requires probabilistic independence of the errors. This assumption means

that information on some of the errors provides no information on other errors. For time series data

this assumption is often violated. This is because of a property called autocorrelation (Van Horne,

1998).

Test of autocorrelation aims to examine whether in a linear regression model has

correlation between trouble errors in the period t with an error in the period t-1 (before). One of

the methods that can be used to detect autocorrelation is the Durbin Watson (DW). DW value

shows that there is no autocorrelation in regression model.

Durbin Watson (DW) Test Statistic

In Field (2008), Durbin Watson test statistic, a test for correlation between errors.

Specifically, it tests whether adjacent residuals are correlated. In short, this option is important for

testing whether the assumption of independent errors is tenable. The test statistic can vary between

0 and 4 with a value of 2 meaning that the residuals are uncorrelated. A value greater than 2

indicates a negative correlation between adjacent residuals whereas a value below 2 indicates a

positive correlation. The size of the DW statistic depends upon the number of predictors in the

model, and the number of observations. As a conservative rule of thumb, Field (2009) suggested

that, values less than 1 or greater than 3 gave definitely cause for concern, however values closer

to 2 may still be problematic depending on the sample and model.

3. Heteroscedasticity

This assumption concerns variation around the population regression line. Specifically, it

states that the variation of the Y‟s about the regression line is the same, regardless of the value of

the X‟s (Van Horne, 1998).

Test of heteroscedasticity aims to interpret whether the regression model has the

differences residual variance from one observation to another observation (Ghozali, 2002). If the

residual variance from one observation to another observation is the same, it is called

homoscedasticity.

The graphic of scatterplot (in appendix) shows that the dots have not established a

specific pattern. Some of the dots located adjacent but some other dots spread above and below the

numbers of 0 at the axis Y. Thus, the data in the graphics exhibits homoscedasticity.

4. Normally Distributed

The assumption states that the errors are normally distributed. We can check this by

forming a histogram of the residuals. If the assumption holds, then the histogram should be

approximately symmetric and bell-shaped. But if there is an obvious skewness, too many residual

more than, say, two standard deviations from the mean, or some other non-normal property, then

this indicates a violation of the assumption (Van Horne, 1998).

From the graphics of histogram and normal P-P plot (in appendix), we concluded that the

histogram gave the normal pattern of distribution. Meanwhile, the graphic of normal P-P plot

shows that the dots spread around the diagonal line, and the spreading follows the diagonal line.

Both graphics show that the data meets reasonable assumption of normality.

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Based on the results of assumptions of population described above, the regression model

does not have the assumptions of heteroscedasticity, multicollinearity, autocorrelation, and the

data are normally distributed. Thus, our regression model is appropriate to use for testing the

hypothesis 1- 4.

5.8. The Credibility of Research Findings

Underpinning the above discussion on multi-method usage has been the issue of the

credibility of research findings. This is neatly expressed by Raimond (1993) and Rogers (1961,

cited by Raimond, 1993). Reducing the probability of getting the answer wrong means that

concentration has to paid to two particular emphases on research design, namely, reliability and

validity.

5.8.1 Reliability

Reliability examines whether the measurement can be repeated; that is, whether we are

measuring something that can be replicated over time instead of a random effect. Reliability can be

evaluated by using the following three questions (Easterby-Smith et al., 2002):

1. Will the measures yield the same results on other occasions?

2. Will similar observations be reached by other observers?

3. Is there transparency in how sense was made from the raw data?

To ensure that this research has answered these three questions, we have reviewed the previous

research findings concluded by other researchers from many research setting and time period.

5.8.2 Validity

Validity is concerned with whether the findings are really about what they illustrate to be

about. Is the relationship between two variables a causal relationship? We minimised the potential

lack of validity in the conclusions by analysing the results obtained quantitatively and

qualitatively. Even though analysing results obtained quantitatively and qualitatively does not

minimise the potential lack of validity, the results obtained should be consistent with each other.

5.8.3 Generalisability

Generalisability is sometimes referred to as external validity. A concern we may have in

the design of our research is the extent to which your research results are generalisable: that is,

whether our findings may be equally applicable to other research settings, such as other

organisations.

In this research, the purpose of our research will not be to produce a theory that is

generalisable to all populations. Our objective will be simply to try to explain what is happened in

Indonesia Capital Market. Therefore, our results can not be generalised.

5.9. The Limitations of Research Design

There is no research project without limitations; and there is no research as a perfectly

designed study. It is in line with Patton (1990), who noted that “there are no perfect research

designs and there are always trade-offs”. Yin (2003) also noted that limitations derived from the

conceptual framework and the study‟s design. Furthermore, each method, tool or technique has its

unique strengths and weaknesses (Smith, 1975).

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Since all different methods will have different effects, it makes sense if we use different

methods to avoid the „method effect‟. It will lead us to greater confidence being placed in our

conclusions. Therefore, it is quite usual for a single study to combine quantitative and qualitative

methods and/or to use primary and secondary data. There are two major advantages to employ

multi-methods in the same study. First, different methods can be used for different purpose in a

study. The second advantage of applying multi-methods is that it enables triangulation to take

place. Triangulation refers to the use of different data collections methods within one study in

order to ensure that the data are telling us what we believe they are describing us.

In our research, we had two limitations as follow, the first is regarding to the limitation of

data, as sometimes the data are not complete. The second is regarding to the data analysis.

Therefore, we used regression and augmented equations and also qualitative analysis to explain the

finding of hypotheses.

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6. PRESENTATION OF DATA AND ANALYSIS OF RESULTS

6.1 Research Question 1, Hypotheses, Hypotheses Testing, and Result Analysis

Chapter 6 described the hypothesis testing for research questions one and two, three, and

four, and discussed the results obtained in detail. The chapter discussed the results for each

research question.

In chapter 1, four research questions were introduced. A total of 4 major hypotheses were

constructed to assist in answering the research questions. Chapters 6 now discuss the findings from

this inquiry. It presented and discussed the results of testing hypotheses 1, 2, 3, and 4 that

belonged to research question one, two, three, and four respectively. The remaining research

questions, the associated hypotheses and the results are presented below.

6.1.1. Research Question 1

In this research, our minor research questions are as follow:

What are the determinants of capital structure of the firms in the manufacturing sector in

Indonesia?

a. As implied by the trade-off theory and the pecking order theory, do growth

opportunities have a positive relationship with debt ratio?

b. As the pecking order hypothesis, does a firm‟s profitability have a negative

relationship with level of debt? And as implied by the trade-off theory, does a firm‟s

profitability have a positive relationship with the debt ratio?

c. In accordance with the pecking order theory and trade-off theory, is there a negative

relationship between risk (earnings volatility) and debt ratio?

d. As suggested by the trade-off theory, does size has a positive relationship with debt

ratio? And as suggested by the pecking order theory of the capital structure, is there a

negative relationship between level of debt and size of the firm?

e. In accordance with the trade-off theory, is there a positive relationship between asset

tangibility and level of debt?

6.1.2. Hypothesis One (H1)

In this research, our minor hypotheses one (H1) are as follow:

H1.a: As implied by the trade-off theory and the pecking order theory, we hypothesise that growth

opportunity is positively related to debt ratios.

H1.b: As the pecking order hypothesis, we hypothesise that profitability has a negative

relationship with debt ratios and based on the trade-off theory we hypothesise that profitability has

a positive relationship with debt ratio.

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H1.c: In accordance with the pecking order theory and trade-off theory, we hypothesise a negative

relationship between risk (earnings volatility) and debt ratio.

H1.d: As suggested by the trade-off theory, we hypothesise that size has a positive relationship

with debt ratio, and as suggested by the pecking order theory of the capital structure there is a

negative relationship between debt ratio and size.

H1.e: In accordance with the trade-off theory, we hypothesise a positive relationship between asset

tangibility and debt ratio.

6.1.3. Testing the Hypothesis 1

As described in chapter 5, multiple regression analysis was selected to test hypothesis 1.

Variables used at hypothesis 1 are growth; asset tangibility; risk; size; and profitability as the

independent variables and short-term leverage; long-term leverage; total leverage; market leverage

as the dependent variables.

The objective of regression analysis are to examine the linear relationships between the

predictor and criterion variables, to examine the influence of growth opportunity; profitability;

risk; size; and asset tangibility on short-term leverage; long-term leverage; total leverage; market

leverage.

6.1.4. Analysis of Results

Analysis of results is consistent of result analysis of each variables and its consistency to

theory and previous research, and also the Indonesian capital market condition regarding variables

relationship and LQ45 Index.

6.1.4.1 Analysis of the Result and Its Consistency to Theory and Previous Research

The following is the regression result of the effect of independent variable on dependent

variable. 0.000 level of significant is the highest significant level which implies that dependent

variable is significantly influenced by independent variable.

Table 6.1a. Regression Results of Hypothesis Testing 1

Model Unstandar

dised Co-

efficients B

Standar

dised Co-

efficients

Beta

t Sig. Collineari

ty

Statistics

Tolerance

Collinea

rity

Statistics

VIF

STL (Consta

nt)

.138 .843 .400

PRFT -.443 -.277 -3.761 .000 .648 1.543

TANG -.230 -.196 -2.687 .008 .658 1.519

SIZE .012 .071 1.019 .310 .724 1.381

RISK 1.218 .346 5.081 .000 .758 1.319

GROW .092 .136 2.117 .036 .848 1.179

F=18.878 (0.000); R-squared=0.332; Adjusted R-squared=0.314; N=196

LTL (Consta

nt)

.141 .962 .337

PRFT -.296 -.213 -2.794 .006 .648 1.543

TANG .372 .364 4.822 .000 .658 1.519

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SIZE -.004 -.029 -.398 .691 .724 1.381

RISK -.712 -.232 -3.301 .001 .758 1.319

GROW .138 .234 3.522 .001 .848 1.179

F=15.362 (0.000); R-squared=0.288 ; Adjusted R-squared= 0.269; N=196

Table 6.1b. Regression Results of Hypothesis Testing 1

Model Unstandar

dised Co-

efficients B

Standar

dised Co-

efficients

Beta

t Sig. Collineari

ty

Statistics

Tolerance

Collinea

rity

Statistics

VIF

TLV (Consta

nt)

.207 1.851 .066

PRFT -.765 -.502 -9.481 .000 .648 1.543

TANG .104 .093 1.765 .079 .658 1.519

SIZE .014 .090 1.789 .075 .724 1.381

RISK .506 .151 3.085 .002 .758 1.319

GROW .229 .356 7.681 .000 .848 1.179

F=72.059; R-squared=0.655 ; Adjusted R-squared=0.646 ; N=196

MRL (Consta

nt)

1.283 12.829 .000

PRFT -.683 -.513 -9.464 .000 .648 1.543

TANG .106 .109 2.019 .045 .658 1.519

SIZE -.011 -.080 -1.556 .121 .724 1.381

RISK .142 .049 .968 .334 .758 1.319

GROW -.375 -.666 -14.070 .000 .848 1.179

F=67.082 (0.000) ; R-squared=0.638 ; Adjusted R-squared=0.629 ; N=196

A.Growth on Leverage

From table 6.1, we can analyse the influence of growth on short-term leverage, long-term leverage,

total leverage, and market leverage.

Growth and Short-term Leverage

Growth has a positive significant regression coefficient on short-term leverage, with 0.036 level of

significance and 2.117 t-values. This suggests that high growth firms are more likely to use short-

term leverage for financing their investments than low growth firms.

Growth and Long-term Leverage

Growth has a positive significant regression coefficient on long-term leverage, with 0.001 level of

significance and 3.522 t-values. This suggests that high growth firms are more likely to use long-

term leverage for financing their investments than low growth firms.

Growth and Total Leverage

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Growth has a positive significant regression coefficient on total leverage, with 0.000 level of

significance and 7.681 t-values. This suggests that high growth firms are more likely to use total

leverage for financing their investments than low growth firms.

Growth and Market Leverage

Growth has a negative significant regression coefficient on market leverage, with 0.000 level of

significance and -14.070 t-values. This suggests that high growth firms are less likely to use

market leverage for financing their investments than low growth firms.

Our results showed that growth was positively related with short-term leverage, long-term

leverage, and total leverage. It was consistent with the pecking order theory. According to the

pecking order theory hypothesis, a firm will first use internally generated funds which may not be

sufficient for a growth firm. And the next option for the growth firms is to use debt financing

which implies that a growth firm will have a high leverage (Drobetz and Fix 2003).

Applying pecking order arguments, growth firms place a greater demand on the internally

generated funds of the firm. Consequentially, firms with relatively high growth will tend to issue

securities less subject to information asymmetries, i.e. short-term debt. This should lead firms with

relatively higher growth to having more leverage.

Our results were consistent with what Sogorb-Mira and Lopez-Gracia (2003) said that

there was a positive relation between growth and short-term leverage, long-term leverage, and

total leverage. Sogorb-Mira and López-Gracia (2003) tested leverage predictions of the trade-off

and pecking order models. They used panel data Spanish SMEs. Their result showed a positive

and statistically significant impact between growth opportunities and firm leverage. This result is

consistent with the Michaelas et al. (1999) argument, based on the idea that in SMEs the trade off

between independence and financing availability is more pronounced and the major part of debt

financing is short term.

Pandey (2001) examined the determinants of capital structure of Malaysian companies

and showed that growth variable had a positive significant influence on all types of book and

market value debt ratios. This finding supported both trade-off and pecking order theories. On the

other hand, according to Çağlayan and Şak (2010), market to book was found to have positive

effect on book leverage. Positive sign of market to book was also along the lines of pecking order

theory.

Our results were in line with what agency costs / trade-off theory that the growth was

negatively related with market leverage. Agency costs for growth firms are expected to be higher

as these firms have more flexibility with regard to future investments. The reason is that

bondholders fear that such firms may go for risky projects in future as they have more choice of

selection between risky and safe investment opportunities. Deeming their investments at risk in

future, bondholders will impose higher costs at lending to growth firms. Growth firms that are

facing higher cost of debt will use less debt and more equity. Congruent with this, Titman and

Wessels (1988), Barclay et al. (1995) and Rajan and Zingales (1995), all found a negative

relationship between growth opportunities and leverage.

Following the trade-off theory, for companies with growth opportunities, the use of debt

is limited as in the case of bankruptcy, the value of growth opportunities will be close to zero,

growth opportunities are particular case of intangible assets (Myers, 1984; Williamson, 1988 and

Harris and Raviv, 1990). Firms with less growth prospects should use debt because it has a

disciplinary role (Jensen, 1986; Stulz, 1990). Firms with growth opportunities may invest sub-

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optimally, and therefore creditors will be more reluctant to lend for long horizons. This problem

can be solved by short-term financing (Titman and Wessels, 1988) or by convertible bonds (Jensen

and Meckling, 1976; Smith and Warner, 1979).

According to agency costs, on the other hand, Myers (1977) argued that due to agency

problems, firms invested in assets that might generate high growth opportunities in the future

faced difficulties in borrowing against such assets. For this reason, we might now instead expect a

negative relationship between growth and leverage.

Some research found the negative result, such as Huang and Song (2002), concluded that

the static trade-off model seemed better than the pecking order hypothesis in explaining the

features of capital structure for Chinese listed companies. They used sales growth rate to measure

the past growth experience and Tobin‟s Q to measure a firm‟s growth opportunity in the future.

Their finding showed that firms with a high growth rate in the past tended to have higher leverage,

while firms that had a good growth opportunity in the future (a higher Tobin‟s Q) tended to have

lower leverage.

Sbeiti (2010) found a negative relation between growth opportunities and leverage, it was

consistent with the predictions of the agency theory that high growth firms used less debt, since

they did not wish to be exposed to possible restrictions by lenders. However, variables such as

market to book ratio reflected the capital market valuation of the firm, which in turn was affected

by the conditions of the capital market.

In the Shah and Khan (2007) study, growth variable was significant and was negatively

related to leverage. As expected, this negative coefficient showed that growth firms did not use

debt financing. Their results were in conformity with the result of Titman and Wessels (1988);

Barclay, et al. (1995) and Rajan and Zingales (1995). The usual explanation was that growing

firms had more options of choosing between safe and risky firms.

In Gaud, Jani, Hoesli, and Bender (2003), the negative sign of growth confirmed the

hypothesis that firms with growth opportunities were less levered. To analyse further this

relationship, they observed a negative relationship between growth and leverage when market

values were used, and a positive relation when leverage was measured with book values.

B. Profitability on Leverage

We can see from table 6.1 to imply the influence of profitability on short-term leverage,

long-term leverage, total leverage, and market leverage.

Profitability and Short-term Leverage

Profitability has a negative significant regression coefficient on short-term leverage, with

0.000 level of significance and -3.761 t-values. This suggests that high profitability firms are less

likely to use short-term leverage for financing their investments than firms with low profitability.

High profitability firms in the manufacturing sector of the LQ45 Index are less likely to use short-

term leverage for financing their investments than low profitability firms.

Profitability and Long-term Leverage

Profitability has a negative significant regression coefficient on long-term leverage, with


likely to use long-term leverage for financing their investments than firms with low profitability.

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Profitability and Total Leverage

Profitability has a negative significant regression coefficient on total leverage, with 0.000

level of significance and -9.481 t-values. This suggests that high profitability firms are less likely

to use total leverage for financing their investments than firms with low profitability.

Profitability and Market Leverage

Profitability has a negative significant regression coefficient on market leverage, with


likely to use market leverage for financing their investments than firms with low profitability.

Profitability has negative correlation with short-term leverage, long-term leverage, total

leverage, and market leverage. Comparing the results with the theory, all of our results are

negative and they are in line with the pecking order theory but contradicting with the trade-off

theory.

The pecking order theory, based on works by Myers and Majluf (1984) suggests that

firms have a pecking-order in the choice of financing their activities. Roughly, this theory states

that firms prefer internal funds rather than external funds. If external finance is required, the first

choice is to issue debt, then possibly with hybrid securities such as convertible bonds, then

eventually equity as a last resort (Brealey and Myers, 1991). This behaviour may be due to the

costs of issuing new equity, as a result of asymmetric information or transaction costs.

All things being equal, the more profitable the firms are, the more internal financing they

will have, and therefore we should expect a negative relationship between leverage and

profitability. This relationship is one of the most systematic findings in the empirical literature

(Harris and Raviv, 1991; Rajan and Zingales, 1995; Booth et al., 2001). There are conflicting

theoretical predictions on the effects of profitability on leverage (Rajan and Zingales, 1995); while

Myers and Majluf (1984) predicted a negative relationship according to the pecking order theory,

Jensen (1986) predicted a positive relationship. Following the pecking order theory, profitable

firms, which have access to retained profits, can use these for firm financing rather than accessing

outside sources.

However, in a trade-off theory framework, an opposite conclusion is expected. When

firms are profitable, they should prefer debt to benefit from the tax shield. In addition, if past

profitability is a good proxy for future profitability, profitable firms can borrow more as the

likelihood of paying back the loans is greater. From the trade-off theory point of view more

profitable firms are exposed to lower risks of bankruptcy and have greater incentive to employ

debt to exploit interest tax shields. Hence, high profitability firms in the manufacturing sector of

the LQ45 Index do not want to take benefit from the tax shield.

Meanwhile, based on agency theory, there are two possible explanations. Jensen (1986)

predicted a positive relationship between profitability and financial leverage, if the market for

corporate control was effective, such relation occurred because debt reduced the free cash flow

generated by profitability. However, if it was ineffective, Jensen (1986) predicted a negative

relationship between profitability and leverage.

Comparing the results with previous studies, they were consistent. Drobetz and Fix

(2003) tested leverage predictions of the trade-off and pecking order models using Swiss data.

Their results were in conformity with the pecking order model but contrary to the trade-off model,

more profitable firms used less leverage. They found that profitability was negatively correlated

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with leverage, both for book and market leverage. This result reliably supported the predictions of

the pecking order theory.

The Huang and Song (2002) study results were consistent with the predictions of

theoretical studies and the results of previous empirical studies. Profitability was strongly

negatively related with total liabilities ratios. The Pandey (2001) results showed that profitability

had a significant inverse relation with all types of book and market value debt ratios. He showed

that the results confirmed findings of earlier studies and were consistent with pecking order theory

(Myers, 1984) that postulated a negative relationship between profitability and debt ratio.

Cole (2008) showed a consistent negative relation between profitability with the loan-to-

asset ratio. The coefficients for return on asset were significant. These later findings were strongly

supportive to the pecking order theory which predicted that profitable firms used less debt because

they could fund projects with retained earnings. It was inconsistent with trade-off theory that

predicted profitable firms used more debt to take advantage of the debt tax shield The other reason

was they had lower probability of financial distress.

Sbeiti (2010) found that firm profitability seemed to have a statistically negative and

significant relationship with both the book and market leverage in the three countries. It was

consistent with Booth et al. (2001), who reported the same results for the profitability variable and

argued that the importance of profitability was related to the significant agency and informational

asymmetry problems in developing countries. The results were also consistent with Titman and

Wessels (1988), Rajan and Zingales (1995), Cornelli et al. (1996), Bevan and Danbolt (2002) in

developed countries, Pandey (2001), Um (2001), Wiwattanakantang (1999), Chen (2004),

Deesomsak, Paudyal and Pescetto (2004) and Antoniou et al. (2007).

The Shah and Khan (2007) study found the negative sign and statistical significance.

Frydenberg (2001b) described retained earning as the most important source of financing. Good

profitability thus reduced the need for external debt. In Çağlayan and Şak (2010) study,

profitability was found to have negative effect on the book leverage. A negative relationship

between profitability and leverage was observed in the majority of empirical studies. This study

provided similar results confirming the pecking order theory rather than static trade-off theory.

In the Han-Suck Song (2005) study, profitability was negatively correlated with all three

leverage measures, which was in line with the pecking-order theory. Firms preferred using surplus

generated by profits to finance investments. This result might also indicate that firms in general

always preferred internal funds rather than external funds, irrespective of the characteristic of an

asset that should be financed (e.g. tangible or nontangible asset). Gaud, Jani, Hoesli and Bender

(2003), reported in several other studies that the profitability variable was negative and significant

in all cases (Rajan and Zingales, 1995; Booth et al., 2001; Frank and Goyal, 2002). This finding

provides support for the pecking order theory.

In Indonesia, previous empirical testing showed a significant negative relationship

between profitability and leverage. This phenomenon indicates that the lower the profitability, the

higher the leverage or vice versa. If the indication happens, it leads to a state that firm‟s debt to

help increasing liquidity but it is not supported by the firm‟s performance. This indicates the

occurrence of agency problems. If the opposite happens then the relationship is consistent with the

pot which states that profitability is negatively related to leverage. In this case the firm is the low

use of debt with high profitability. According to pecking order theory, high profitability firms

borrow less because such firms have more internal financing, while firms with lower profitability

require external funding and the consequence is debt accumulation (Sugiarto, 2009).

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C. Risk on Leverage

The following result is the analysis of the effect of risk on short-term leverage, long-term

leverage, total leverage, and market leverage (table 6.1).

Risk and Short-term Leverage

Risk has a positive significant regression coefficient on short-term leverage, with 0.000

level of significance and 5.081 t-values. This suggests that high risk firms are more likely to use

short-term leverage for financing their investments than low risk firms.

Risk and Long-term Leverage

Risk has a negative significant regression coefficient on long-term leverage, with 0.001

level of significance and -3.301 t-values. This suggests that high risk firms are less likely to use

long-term leverage for financing their investments than low risk firms.

Risk and Total Leverage

Risk, has a positive significant regression coefficient on total leverage, with 0.002 level

of significance and 3.085 t-values. This suggests that high risk firms are more likely to use total

leverage for financing their investments than low risk firms.

Risk and Market Leverage

Risk has a positive but not significant regression coefficient on market leverage, with

0.334 level of significance and 0.968 t-values. This suggests that high risk firms are more likely to

use market leverage for financing their investments than low risk firms.

Our result showed that risk has positive influence on short-term leverage, total leverage,

and market leverage, while it has negative effect on long-term leverage. The negative result

supported both the trade-off theory that the more volatile cash flows the higher the probability of

default and the pecking order theory that issuing equity is more costly for firms with high volatile

cash flows. Our positive result supported the agency theory that the problem of underinvestment

decreased when the volatility of the firms returns increased, hence, firms use more leverage.

Bradley et al., (1984); Kester, (1986); Titman and Wessels (1988) found that since higher

variability in earnings indicates that the probability of bankruptcy increases, they expect that firms

with higher income variability have lower leverage. Firms that have high operating risk can lower

the volatility of the net profit by reducing the level of debt. A negative relation between operating

risk and leverage is also expected from a pecking order theory perspective: firms with high

volatility of results try to accumulate cash during good years, to avoid under-investment issues in

the future. Drobetz and Fix (2003) found as expected, the leverage was negatively related to the

volatility. They also showed that their finding supported both the trade-off theory (more volatile

cash flows increase the probability of default) and the pecking order theory (issuing equity is more

costly for firms with volatile cash flows).

Pandey (2001) found that there was a negative relation of earnings volatility with book

and market value long-term debt ratio, which was consistent with the trade-off theory. It also

revealed a positive relation between risk and short-term debt ratios.

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We found that risk was positively related with the short-term leverage, and risk was also

positively related with the total leverage and market leverage. Those were in line with the agency

theory that Cools (1993) said it suggested positive relationship between earning volatility and

leverage. He said that the problem of underinvestment decreased when the volatility of the firms

return increased.

The Huang and Song (2002) results showed that there was a positive relation between

total liabilities ratio and volatility. It was consistent with Hsia‟s (1981) view that firms with a

higher leverage level tended to make riskier investment. They found that the companies with high

leverage in China tended to make riskier investments.

D. Size on Leverage

Table 6.1 indicates the regression result of the effect of size on short-term leverage, long-

term leverage, total leverage, and market leverage. Our analysis is as follows:

Size and Short-term Leverage

Size has a positive but not significant regression coefficient on short-term leverage, with

0.310 level of significance and 1.019 t-values. This suggests that larger firms are more likely to

use short-term leverage for financing their investments than small size firms.

Size and Long-term Leverage

Size has a negative but not significant regression coefficient on long-term leverage, with

0.691 level of significance and -0.398 t-values. This suggests that larger size firms are less likely

to use long-term leverage for financing their investments than small size firms.

Size and Total Leverage

Size has a positive but not significant regression coefficient on total leverage, with 0.075

level of significance and 1.789 t-values. This suggests that larger size firms are more likely to use

total leverage for financing their investments than small size firms.

Size and Market Leverage

Size has a negative significant regression coefficient on market leverage, with 0.121 level

of significance and -1.556 t-values. This suggests that larger size firms are less likely to use market

leverage for financing their investments than small size firms.

Our results which describe that the size was positively related with total leverage and

short-term leverage were consistent with trade-off theory, meanwhile our results which show that

the size was negatively related with market leverage and long-term leverage were consistent with

pecking order theory. Rajan and Zingales (1995) argued that there was less asymmetrical

information about the larger firms. This reduced the chances of undervaluation of the new equity

issue and thus encouraged the large firms to use equity financing.

Static trade-off theory is generally interpreted as predicting that large firms will have

more debt since larger firms are more diversified and have lower default risk. Larger firms are also

typically more mature firms. These firms have a reputation in debt markets and consequently face

lower agency costs of debt. Hence, the trade-off theory predicts that leverage and firm size should

be positively related. The pecking order theory is usually interpreted as predicting an inverse

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relation between leverage and firm size. The argument is that large firms have been around longer

and are better known. Thus, large firms face lower adverse selection and can more easily issue

equity compared to small firms where adverse selection problems are severe. Large firms also

have more assets and thus the adverse selection may be more important if it impinges on a larger

base.

There are several theoretical reasons why firm size is related to the capital structure.

Smaller firms may find it relatively more costly to resolve informational asymmetries with lenders

and financiers, which discourages the use of outside financing (Chung, 1993; Grinblatt and

Titman, 1998) and should increase the preference of smaller firms for equity relative to debt

(Rajan and Zingales, 1995). However, this problem may be mitigated with the use of short term

debt (Titman and Wessels, 1988). Relative bankruptcy costs and probability of bankruptcy (larger

firms are more diversified and fail less often) are an inverse function of firm size (Warner, 1977;

Ang et al. , 1982; Pettit and Singer, 1985; Titman and Wessels, 1988). A further reason for smaller

firms to have lower leverage ratios is that smaller firms are more likely to be liquidated when they

are in financial distress (Ozkan, 1996).

Some previous studies conclude positive relationship, for example Drobetz and Fix

(2003) found that size was positively related to leverage, indicating that size was a proxy for a low

probability of default. This is in contrast to the results in Rajan and Zingales (1995), where firms

in Germany tend to be liquidated more easily than in the Anglo-Saxon countries. Large firms have

substantially less debt than of small firms. Therefore, Drobetz and Fix (2003), concluded that this

result supported the trade-off theory, suggesting that large firms showed lower probability of

default.

Sogorb-Mira and López-Gracia (2003) found that firm size and leverage were positively

related. They explained that this relationship could come from the fact that SMEs had to face

higher bankruptcy costs, greater agency costs and bigger costs to resolve the higher informational

asymmetries. Even within this firm category, SMEs of greater size could access a higher leverage.

Their result was also the same as that obtained by a considerable number of previous studies

(Ocaña et al., 1994; Hutchinson, 1995; Chittenden et al., 1996; Berger and Udell, 1998; Michaelas

et al., 1999; and Romano et al., 2000).

Pandey (2001) showed that the positive correlation between size and debt ratios

confirmed the hypothesis, that larger firms tended to be more diversified and less prone to

bankruptcy and the direct cost of issuing debt or equity was smaller. This is consistent with the

trade-off theory.

Sbeiti (2010) investigated the determinants of capital structure in the context of three

GCC countries and the impact of their stock markets' development on the financing choices of

firms operating in these markets. He found that the coefficient values of the size variable remained

positive and were statistically significant in relation to both book and market leverage ratios across

the three countries. The result was in line with results reported by Rajan and Zingales (1995),

Wiwattanakantang (1999), Booth et al. (2001), Pandey (2001), Prasad et al. (2001), Deesomsak,

Paudyal and Pescetto (2004), and Antoniou et al. (2007), the size coefficient was positive and

statistically significant in the case of all three countries and for both measures of leverage.

In Shah and Khan (2007) study, size had a positive coefficient but was insignificant. The

coefficient value was 0.0002. However, the t-value of 0.07 was very small and the p-value was

0.940. This showed that size variable was not a proper explanatory variable of debt ratio. This

finding did not confirm our second hypothesis. Our second hypothesis was based on the Rajan and

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Zingales‟ (1995) argument that there was less asymmetric information about the larger firms

which reduced the chance of undervaluation of new equity. Our finding did not confirm the

Titman and Wessels‟ (1988) argument as well that larger firms were more diversified and had

lesser chances of bankruptcy that should motivate the use of debt financing. Why did our finding

on size of a firm with relation to the leverage ratio not confirm the established theories? Trade off

theory suggested that firm size should matter in deciding an optimal capital structure because

bankruptcy costs constituted a small percentage of the total firm value for larger firms and greater

percentage of the total firm value for smaller firms. As debt increased the chances of bankruptcy,

hence smaller firms should have lower debt ratio.

Çağlayan and Şak (2010) showed size was found to have positive relationships with the

leverage of banks in this study. The findings of the relationship with the size were in line with the

static trade-off and agency cost theory.

In the Han-Suck Song (2005) study, the results revealed that size was a significant

determinant of leverage. But while size was positively related to both total debt and short-term

debt ratio, it was negatively correlated with long-term debt ratio, although the economic

significance was rather small for the latter case. Even if the data did not allow us to further

decompose short-term debt, we might still find the results of Bevon and Danbolt (2000)

interesting. They found that while size was positively correlated with both trade credit and

equivalent and short-term securitized debt, it was negatively correlated with short-term bank

borrowing. This may indicate that small firms were supply constrained, in that they did not have

sufficient credit ranking to allow them to long-term borrowing.

Gaud, Jani, Hoesli and Bender (2003) analysed the determinants of the capital structure

Swiss companies listed in the Swiss stock exchange. They found the positive impact of size on

leverage was consistent with the results of many empirical studies (Rajan and Zingales, 1995;

Booth et al., 2001; Frank and Goyal, 2002). It led them to reject the hypothesis that size acted as

an inverse proxy for informational asymmetries, but could suggest that size acted as an inverse

proxy for the probability of bankruptcy.

Some previous studies which had negative result for this relationship were as follows.

Huang and Song (2002) concluded that, on the relationship between size and leverage, if size is

interpreted as a reversed proxy for bankruptcy cost, it should have less or no effect on Chinese

firms‟ leverage because the state kept around 40% of the stocks of these firms and, because of soft

budget constraint, state-controlled firms should have much less chance to go bankrupt. Cole

(2008), stated that firm size, as measured by the natural logarithm of total assets, was inversely

related to firm leverage, and this relation was significant better than the 0.001 level in each survey.

In other words, larger firms used significantly less debt in their capital structure.

In Indonesia, firm size has positive regression coefficient on short-term and long-term

liabilities. It indicates that larger firms tend to have more debt. Firm size is a proxy for information

asymmetry between the firm and market. According to the pecking order theory, there will be a

negative relationship between leverage and firm size. Because, the bigger the firm the greater the

access to capital markets, so that firms will reduce their leverage and prefer to issue equity.

Previous empirical finding in Indonesia showed that there was a negative relationship existing

between firm size and leverage (in Sugiharto, 2009).

E. Tangibility on Leverage

Finally, table 6.1 implies the regression result of the influence of tangibility on short-term

leverage, long-term leverage, total leverage, and market leverage. Our analysis is as follows:

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Tangibility and Short-term Leverage

Tangibility has a negative significant regression coefficient on short-term leverage, with

0.008 level of significance and -2.687 t-values. This suggests that high tangibility firms are less

likely to use short-term leverage for financing their investments than firms with low tangibility.

Tangibility and Long-term Leverage

Tangibility has a positive significant regression coefficient on long-term leverage, with

0.000 level of significance and 4.822 t-values. This suggests that high tangibility firms are more

likely to use long-term leverage for financing their investments than firms with low tangibility.

Tangibility and Total Leverage

Tangibility has a positive but not significant regression coefficient on total leverage, with

0.079 level of significance and 1.765 t-values. This suggests that high tangibility firms are more

likely to use total leverage for financing their investments than firms with low tangibility.

Tangibility and Market Leverage

Tangibility has a positive significant regression coefficient on total leverage, with 0.045

level of significance and 2.019 t-values. This suggests that high tangibility firms are more likely to

use market leverage for financing their investments than firms with low tangibility.

Our results show that high tangibility firms in the manufacturing sector of the LQ45

Index use more long-term leverage, total leverage, and market leverage. However, high tangibility

firms use less short-term leverage, it implies that short-term leverage needs less tangibility of

assets. If we compare our results to the theory, that the tangibility is negatively related with short-

term leverage, it is in line with the agency cost theory. Based on the agency problems between

managers and shareholders, Harris and Raviv (1990) suggested that firms with more tangible

assets should take more debt. This is due to the behaviour of managers who refuse to liquidate the

firm even when the liquidation value is higher than the value of the firm as a going concern.

Indeed, by increasing the leverage, the probability of default will increase for the benefit of the

shareholders. In an agency theory framework, debt can have another disciplinary role: by

increasing the debt level, the free cash flow will decrease (Grossman and Hart, 1982; Jensen,

1986; Stulz, 1990). As opposed to the former, this disciplinary role of debt should mainly occur in

firms with few tangible assets, because in such a case it is very difficult to monitor the excessive

expenses of managers.

Previous studies with negative correlation between variables are as follows. Huang and

Song (2002) found that, in contrast to theoretical predictions, tangibility was negatively related

with total liability. They explained that the reason for that might be the non-debt part of total

liability did not need collaterals. Long-term debt ratio is positively correlated with tangibility.

Pandey‟s results (2001) indicated a significant negative relation of tangibility with book

and market value short-term debt ratios. The relation of tangibility with the market value long-

term debt ratio was also significantly negative whilst with book value long-term ratio was not

statistically significant. These results contradicted with the trade-off theory that postulated a

positive correlation between long-term debt ratio and tangibility since fixed assets acted as

collateral in debt issues.

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Sbeiti (2010) found that the stylised fact that the tangibility variable was positively

related to the availability of collateral and leverage was not consistent with the findings in the

paper, where tangibility was negative and statistically significant in relation to both book and

market value of leverage in the three countries. In general, this negative association between

leverage and tangibility can be explained by the fact that those firms that maintain a large

proportion of fixed assets in their total assets tend to use less debt than those which do not. This is

due to the fact that a firm with an increasing level of tangible assets may have already found a

stable source of income, which provides it with more internally generated funds and avoid using

external financing. Another explanation for this relationship could be the view that firms with

higher operating leverage (high fixed assets) would employ lower financial leverage. Overall the

results are consistent with Cornelli et al. (1996), Hussain and Nivorozhkin (1997), Booth et al.

(2001), Nivorozhkin (2002) who also suggested a negative relation between tangibility and debt

ratio. Finally, the relatively larger coefficient value of tangibility for the Saudi firms may indicate

that firms in this country have an effective guarantee against bankruptcy.

Çağlayan and Şak (2010) found that the relationship between tangibility and book

leverage was also found to be negative in this study. This significant negative relationship between

tangibility and leverage provided further support for the agency cost theory and the existence of

conflict between debt holders and shareholders. These results also confirmed with results of

empirical studies for developing countries whereas studies for developed countries showed a

positive relationship.

Our results show that high tangibility firms use more long-term leverage, more total

leverage, and more market leverage. These are in line with the pecking order theory and trade-off

theory. According to the pecking order theory and the trade-off theory, a firm with a large amount

of fixed asset can borrow at relatively lower rate of interest by providing the security of these

assets to creditors. Having the incentive of getting debt at lower interest rate, a firm with a higher

percentage of fixed asset is expected to borrow more than a firm which cost of borrowing is higher

because of having less fixed assets. Thus, there is a positive relationship between tangibility of

assets and leverage.

From a pecking order theory perspective, firms with few tangible assets are more

sensitive to informational asymmetries. These firms will thus issue debt rather than equity when

they need external financing (Harris and Raviv, 1991), leading to an expected negative relation

between the importance of intangible assets and leverage. Most empirical studies concluded to a

positive relation between collaterals and the level of debt (Rajan and Zingales, 1995; Kremp et al.,

1999; Frank and Goyal, 2002). Inconclusive results were reported for instance by Titman and

Wessels (1988).

Some previous studies which conclude positive relationship are as follows: Drobetz and

Fix (2003), found that tangibility was almost always positively correlated with leverage. They

showed that this supported the prediction of the trade-off theory that the debt-capacity increased

with the proportion of tangible assets on the balance sheet.

Rebel A. Cole (2008) found tangibility was positive across each of the four surveys and

was statistically significant at better than the 0.05 level for each survey except for the year 2003.

According to Frank and Goyal (2006), the relation between tangibility and leverage was reliably

positive in cross-sectional studies of publicly traded firms. Shah and Khan (2007) found that

tangibility, with coefficient of 0.1304 was significantly related to debt. Thus their hypothesis was

confirmed by the statistically significant positive relationship between tangibility and leverage.

This finding was in contrast to the earlier finding by Shah and Hijazi (2004). They found that

tangibility was not significantly related to leverage ratio.

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In the Han-Suck Song (2005) study, as can be seen, the coefficients of tangibility were

highly statistically significant for all three debt measures. But while the results showed that

tangibility had a positive relationship with the total debt ratio and the long-term debt ratio, as

expected according to the theoretical discussion above, tangibility was negatively related to the

short-term debt ratio. This finding was consistent with the results of Bevan and Danbolt (2000),

Huchinson et al. (1999), Chittenden et al. (1996) and Van der Wijst and Thurik (1993) report

(Michaleas et al., 1999). Indeed, this result supported the maturity matching principle: long-term

debt forms were used to finance fixed (tangible) assets, while non-fixed assets were financed by

short-term debt (Bevan and Danbolt, 2000).

Gaud, Jani, Hoesli and Bender (2003) showed that the coefficient of the tangibility

variable was positive and significant for the panel data estimations, and this result was similar to

those reported in previous research (Rajan and Zingales, 1995; Kremp et al., 1999; Frank and

Goyal, 2002). This result suggested that firms used tangible assets as collateral when negotiating

borrowing, especially long term borrowing. The observed sign of the relationship did not confirm

the sign that would be expected when using the pecking order theory framework. In such a

framework, firms with less tangible assets are more subject to informational asymmetries, and are

more likely to use debt principally short term debt when they need external financing.

Relationship between tangibility, risk, and leverage in the context of Indonesia are as

follows: Result showed that asset tangibility had negative regression coefficient on short-term

liability while it had positive regression coefficient on long-term liability. It indicated that firms

with higher tangible asset tended to have less short-term debt but had more long-term debt. This

result was consistent with the finding which showed that firm‟s risk had positive regression

coefficient on short-term liability while it had negative regression coefficient on long-term

liability. It indicated that firms with higher risk of bankruptcy and low tangible asset tended to

have more short-term debt but had less long-term debt. Chen and Hammes (2003) found that

tangible assets positively related to leverage. Previous empirical findings in Indonesia found that

the negative coefficient of tangible assets to leverage. This indicated the possibility that the larger

proportion of tangible assets, the lower the leverage, or the lower the tangible asset the higher the

leverage. The significant negative coefficient of tangible assets indicated giving debt to the firm

without considering the firm tangible assets. Therefore, firms that have higher proportion of

tangible assets can borrow more (Rajan and Zingales, 2005).

6.1.4.2 Analysis of the Indonesian Condition

Our findings are implied that high growth firms in the manufacturing sector of the LQ45

Index are more likely to use short-term leverage, long-term leverage, and total leverage for

financing their investments than low growth firms. However, firms with relatively high growth use

less market leverage. Market leverage and firm size have negative correlation and growth and firm

size has positive correlation which shows that high growth firms use less market leverage as they

are large firms. 16/26 of our samples are growth firms. Firms with relatively high growth will tend

to issue securities less subject to information asymmetries, i.e. shot-term debt. Firms in the

manufacturing sector of the LQ45 Index with relatively high growth are also use more long-term

and total leverage as when they use long-term leverage and total leverage for financing their

investments, they have asset tangibility to secure their long-term debt. It is shown by positive

correlation between long-term leverage and total leverage and tangibility.

Even though high growth firms will face more information asymmetries, the Indonesian

capital market has already had the regulation to minimise information asymmetries, such as

regulation of capital market supervisory agency – financial institution, regarding disclosure of

110

information that must be announced to the public, and decision of the board of directors of

Indonesia Stock Exchange, concerning the obligation to deliver information.

For firms in the manufacturing sector of the LQ45 Index, financing constraints will be

more easily solved, as they have more access to banking. Banks will be more recognised and

trusted the companies. It is not excessive considering each moment banks can determine the

condition of the company's financial through various disclosure of information which announced

by the company in the Stock exchange. With this condition, not only the process of granting new

loans will be easier, but also rate of interest charged may also be lower considering that the credit

risk of public companies is relatively smaller. Firms also have easier access to the company to

enter into money markets through the issuance of debt, both short and long term. Generally the

buyer of a letter of debt would certainly prefer if the company which issues a letter of Debt has

become a public company especially firms from LQ45 Index.

High profitability firms in the manufacturing sector of the LQ45 Index are less likely to

use short-term leverage, long-term leverage, total leverage, and market leverage for financing their

investments than low profitability firms. Even though profitability has negative correlation with

risk, which implies that high profitability firms in the manufacturing sector has low risk, firms

prefer use more internal funds rather than more external funds. Comparing the results with the

theory, all of our results are negative and they are in line with the pecking order theory, but

contradicting the trade-off theory. Hence, high profitability firms in the manufacturing sector of

the LQ45 Index use their retained earning and do not want to take benefit from the tax shield.

Result showed that high risk firms in the manufacturing sector of the LQ45 Index have

lower long-term leverage than low risk firms, and it was in line with the pecking order theory and

trade-off theory. As long-term leverage needs more collateral to secure this leverage, the firms

with high risk should have lower long-term leverage. The correlation table indicates that high risk

firms have low profitability, low tangibility, and low size; hence, they use less long-term leverage.

Earning volatility is proxy for the probability of financial distress and the firm will have to pay

risk premium to outside fund providers. To reduce the cost of capital, a firm will first use

internally generated funds and then outsider funds. This suggests that earning volatility is

negatively related with leverage, especially long-term leverage.

However, our results showed that high risk firms in the manufacturing sector use more

short-term leverage, total leverage, and market leverage than low risk firms. In Indonesia, for firms

in the manufacturing sector of the LQ45 Index, financing constraints will be more easily solved,

and rate of interest charged may also be lower, considering that the credit risk of public companies

is relatively smaller, and generally the buyer of a letter of debt would certainly prefer if the

company is from the LQ45 Index.

Our results showed a positive relation between firm size in the manufacturing sector of

the LQ45 Index and short-term leverage, and between size and total leverage. These are consistent

with the following theories: As trade-off theory states, first, large firms did not consider the direct

bankruptcy costs as an active variable in deciding the level of leverage as these costs were fixed by

constitution and constituted a smaller proportion of the total firm‟s value. And also, larger firms

were more diversified and had lesser chances of bankruptcy. Meanwhile, small firms often suffer

the problems associated with asymmetric information, such as adverse selection, and they have to

face higher bankruptcy costs, greater agency costs and bigger costs to resolve the higher

informational asymmetries. That is why there is a positive relationship between size and short-

term leverage and total leverage of our manufacturing firm.

111

Our results show that the size was negatively related to market leverage and long-term

leverage and they were consistent with the pecking order theory. As Rajan and Zingales (1995)

argued there was less asymmetrical information about the larger firms. This reduced the chances

of undervaluation of the new equity issue and thus encouraged the large firms to use equity

financing. Hence, larger firms in the manufacturing sector of the LQ45 Index have less long-term

leverage and market leverage. Meanwhile, size positively related to total leverage and short-term

leverage was consistent with trade-off theory. It implies that larger firms would take the tax shield

benefit.


Index use more long-term leverage, total leverage, and market leverage. According to the pecking

order theory and trade-off theory, a firm with a large amount of fixed asset can borrow at a

relatively lower rate of interest by providing the security of these assets to creditors. Having the

incentive of getting debt at lower interest rate, a firm with a higher percentage of fixed assets is

expected to borrow more as compared to a firm whose cost of borrowing is higher because of

having less fixed assets. However, high tangibility firms in the manufacturing sector of the LQ45

Index use less short-term leverage; it implies that short-term leverage needs less tangibility of

assets.

6.2. Research Question 2, Hypothesis 2, Hypothesis Testing, and Result Analysis

In this sub-section, we will analyse hypothesis 2 with quantitative and qualitative

analysis. The research question two, hypothesis two, hypothesis testing, and the result of the

analysis are as follow:


In this research, our research question two is as follows: How do firms in the

manufacturing sector in Indonesia raise capital for investments, internally or externally (with debt,

equity, or debt to repurchase equity)?

6.2.2. Hypothesis 2

Based on research question two, our hypothesis two (H2) is as follows: Firms in the

manufacturing sector in Indonesia raise capital for investments externally (with debt, equity, or

debt to repurchase equity).


As described in the chapter on research methodology, for testing hypothesis 2, with the

independent variable as financing deficit, and net debt issue, net equity issue, and issue debt to

repurchase equity as the dependent variables, we apply multiple regression analysis and

augmented analysis to test hypothesis 2.

The objective of regression analysis is to examine which firm is following the pecking

order theory more, growth firms or mature firms. If the firms follow the pecking order, the deficit

is financed with internal financing, if they use the external financing, the financing deficit is

financed with debt first, then equity. The firms which follow the pecking order have the changes in

debt with track changes in the deficit one-for-one. Hence, the expected coefficient on the deficit is

1.

The objective of augmented analysis is to examine how growth and mature firms finance

the deficit, with debt first or equity first. If the firms follow the pecking order, changes in debt

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should track changes in the deficit one-for-one (Shyam-Sunder and Myers, 1999). If firms are

financing their deficit with debt first and issue equity only when they reach their debt capacities,

then net debt issued is a concave function of the deficit. (Chirinko and Singha, 2000) and the

coefficient on the squared deficit term would be negative. If firms are issuing equity first and debt

is the next source of financing, then this relationship should be convex and the coefficient on the

squared deficit term would be positive.

6.2.4. Analysis of Quantitative Results of Hypothesis 2

Analysis of results for hypothesis 2 is consists of quantitative and qualitative analysis.

Our quantitative analysis is about variables relationship and its consistency to theory and previous

research, and also about the Indonesia capital market condition.

6.2.4.1 Analysis of Results and Its Consistency to the Theory and Previous Research

The results of hypothesis testing 2 of the influence of financing deficit on net debt issue

and net equity issue are as follow. It includes analysis of regression and augmented model result.

Table 6.2 Regression Results of Hypothesis Testing 2 (Net Debt and Net Equity Issue)

Coefficients

Model Unstandar

dised Co-

efficients

Standardi

sed Co-

efficients

t Sig. Collinearity

Statistics

B

Std.

Error

Beta

Tolera

nce

VIF

NDEBT (Cons

tant)

.001 .021 .052 .958

FD .281 .032 .775 8.753 .000 1.000 1.000

F=76.620 (0.000) ; R-squared=0.600; N=53

NEQUITY (Cons

tant)

-.015 .030 -.506 .615

FD .169 .045 .464 3.738 .000 1.000 1.000

F=13.971 (0.000) ; R-squared=0.215 ; N=53

NRE (Cons

tant)

.086 .014 6.150 .000

FD -.037 .021 -.236 -

1.735

.089 1.000 1.000

F=3.010 (0.089) ; R-squared=0.056 ; N=53

Table 6.3 Augmented Model Results of Hypothesis Testing 2

Coefficients

Model Unstandar

dised Co-

efficients

Standar

dised Co-

efficients

t Sig. Collinearity

Statistics

B Std.

Error

Beta Tolera

nce

VIF

NDEBT (Consta .014 .030 .475 .637

113

nt)

FD .239 .075 .659 3.182 .003 .185 5.410

FDSQR .023 .037 .128 .618 .539 .185 5.410

Independent Variable: FD


A. Regression Model Result

Y is net debt issued and deficit is the financing deficit. This deficit is financed with debt

and/or equity. If firms follow the pecking order, changes in debt should track changes in the deficit

one-for-one. Therefore, the expected coefficient on the deficit is 1.

Net Debt Issued

From the tables we can conclude that the financing deficit has positive significant effects

on net debt issue with t-value of 8.753 and significance value of 0.000. This result suggests that

high deficit firms would tend to issue more net debt. However, the coefficient on the deficit is

0.281 and constant value is 0.001.

Net Equity Issued

The financing deficit has positive significant effects on net equity issue with t-value of

3.738 and significance value of 0.000. This result suggests that high deficit firms would tend to

issue more net equity. The coefficient on the deficit is 0.169 and constant value is -0.015.

Newly Retained Earning

The financing deficit has negative but not significant effects on newly retained earning with t-

value of -1.735 and significance value of 0.089. This result suggests that high deficit firms would

not tend to use newly retained earning. The coefficient on the deficit is -0.037 and constant value

is 0.086.

B. Augmented Model Result

The augmented model is an alternative means of accounting for a firm‟s debt capacity. If

firms are issuing equity first and debt is the next source of financing, then this relationship should

be convex and the coefficient on the squared deficit term would be positive.

For the augmented model, our result shows a positive coefficient on the financial deficit

and on the squared deficit term. However, for the squared deficit term, the coefficient was not

significant. A squared deficit coefficient that is not large in absolute value implies a less reliance

on equity finance for values of the financing deficit.

Table 6.4. Regression Results of Hypothesis Testing 2 (Issue Debt to Repurchase Equity)

Coefficients

Model Unstandar

dised Co-ef

ficients

Standardi

sed Co-

efficients

t Sig. Collinearity

Statistics

B Std.

Error

Beta Tolera

nce

VIF

114

Issue

Debt

(Constant) .025 .051 .502 .620

FD .179 .062 .508 2.890 .008 1.000 1.00

0

F=8.354 (0.008) ; R-squared=0.258 ; N=26

Repo

Equity

(Constant)

-.021

.020

-

1.054

.302

FD .000 .024 -.002 -.009 .993 1.000 1.00

0


F=0.000 (0.993) ; R-squared=3.18E-6 ; N=26

Issue Debt


on the net debt issue with t-value of 2.890 and significance value of 0.008. This result suggests

that high deficit firms would tend to issue more net debt. However, the coefficient on the deficit is


Repurchase Equity

The financing deficit has negative but not significant effects on repurchase equity with t-


not tend to repurchase equity. The coefficient on the deficit is 0.000 and constant value is -0.021.

From table 6.2-6.4, we concluded about firms preferring external or internal financing

and prefer debt or equity.

Prefer External or Internal Financing?

The coefficient of financing deficit on newly retained earning of the firms in the sample

is insignificantly negative. The coefficient of financing deficit on net debt and on net equity issue

is significantly positive. The coefficient of financing deficit on repurchase equity is insignificantly

negative. Therefore, we can conclude that our firms of sample prefer external to internal financing.

In addition, the firms would not repurchase equity to finance the deficit.

Prefer Debt or Equity?

The results of the firms that adopted the pecking order were consistent. The coefficient on

the deficit is significantly positive but the coefficient on the deficit-squared is insignificantly

positive. It indicates that firms issue debt at the first place, and debt is also the residual source of

financing once they have reached their debt capacities. Our evidence seems to suggest firms to rely

more heavily on debt financing rather than equity financing and it follows the pecking order

theory.

The pecking order theory states that changes in debt have played an important role in


according to this theory. Shyam-Sunder and Myers (1999) examined how debt responded to short-

term variation in investment and earnings. The theory predicts that when investments exceed

earnings, debt grows, and when earnings exceed investments, debt falls. Tests of the pecking order

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theory define financing deficit as investments plus change in working capital plus dividends less

internal cash flow. The theory predicts that in a regression of net debt issues on the financing

deficit, the estimated slope coefficient should be one. The slope coefficient indicates the extent to

which new debt issues are explained by financing deficits.

Meanwhile, according to Myers (1984) a firm is said to follow a pecking order if it

prefers internal to external financing and debt to equity if external financing is used. In the Frank

and Goyal (2008) study, the definition of “prefer” internal financing can be interpreted in two

different views. The meaning could be that the firm uses all existing sources of internal finance

before issuing any debt or equity or “other things equal”, that the firm mostly uses internal

financing before using external financing. Meanwhile, they imply the strict interpretation of

“preference of debt over equity” which suggested that after the IPO, equity should never be issued

unless debt had for some reason become insufficient. This leads to the view of a “debt capacity”

which serves to limit the amount of debt and to allow for the use of equity within the pecking

order.

Pecking order models can be derived based on adverse selection considerations, agency

considerations, or other factors. There seem to be a couple of common features that inspire

pecking order theories. The first element is the linearity of the firm‟s objective function, which

means that costs tend to drive the results to corner solutions. The second common element of

pecking order models is the relative simplicity of the model (Frank and Goyal, 2008).

If we compared the previous research to our result, there were some research findings that

were not consistent with our results, for instance, previous research finding from Indonesia, (Ari

Christianti, 2008), concluded that: (1) The results of this study did not fully support the pecking

order theory in explaining the behaviour of firm financing in the IDX especially the manufacturing

sector. This could be explained from the results of the estimation that showed a negative and

significant coefficient of pecking order. (2) It might be explained from the results of this study that

the Indonesian capital market conditions were different from capital markets in developed

countries studied by Shyam-Sunder and Myers (1999), Frank and Goyal (2003) and Jong,

Verbeek, and Verwijmeren (2005). In addition, the impact of the economic crisis in 1997 still

affected the economic condition of Indonesia until 2005.

Leary and Roberts (2005) empirically examined the pecking order theory of capital

structure using a new empirical model that was motivated by the pecking order's decision rule and

implied financing hierarchy. They found that 62% (29%) of the firms in the sample were following

the pecking order in their decision between internal and external (debt and equity) financing and

that most of the equity issuing violations were not due to debt capacity concerns, as suggested by

the modified version of the pecking order. They showed empirically that the pecking order did not

seem to be an implication of information asymmetry.

The Cotei and Farhat (2008) study concluded that for the pecking order model, the test

results rejected the symmetric behaviour assumption at the industry level as well as across all

industries. Under the pecking order model, firms in financing deficit used debt to finance their new

investment whereas firms in financing surplus ended up retiring debt rather than repurchasing

equity. The results showed that firms had the tendency to reduce debt by a significantly higher

proportion when they had financing surplus compared to the proportion of debt issued when they

had financing deficit.

However, there were some research findings which were consistent to our results, for

example Sogorb-Mira and López-Gracia (2003) who explored pecking order theory and trade-off

116

theory that explained financial policy in Spanish small and medium enterprises (SMEs). The

results suggested that both theoretical approaches contributed to explain capital structure in SMEs.

Joher, Ahmed, and Hisham (2009) drew on studies from finance and accounting literature to

revisit pecking order and static trade-off-hypothesis in the context of the Malaysia capital market.

The evidence from the pecking order model suggested that the internal fund deficiency was the

most important determinant that possibly explained the issuance of new debt. Hence, pecking

order hypothesis is well explained in the Malaysian capital market despite the lower predicting

power.

Bharath, Pasquariello, Wu (2008) tested whether information asymmetry was an

important determinant of capital structure decisions, as suggested by the pecking order theory.

They found that information asymmetry did affect the capital structure decisions of U.S. firms.

Medeirosa and Daherb tested two models of the static tradeoff theory and the pecking order theory

for the capital structure of Brazilian firms. The sample consists of firms listed in the Sao Paulo

(Brazil) stock exchange. The result showed that the pecking order theory established that the

financial deficit was covered by debt, permitting the issue of new shares in exceptional cases only.

Shyam-Sunder and Myers (1994, 1999) tested the traditional capital structure models


model predicts external debt financing driven by the internal financial deficit. Their main

conclusion regarding pot is that the pecking order is an effective first-order descriptor of corporate

financing behaviour. Shyam-Sunder and Myers (1999) summarised that the pecking order was an

excellent first-order descriptor of corporate financing behaviour, at least for the sample of mature

corporations. Their results suggested that firms planned to finance anticipated deficits with debt.

The plausible explanation is that the features of the Indonesian economy, with very high

real interest rates and reduced long-term credit supply, makes Indonesian firms to avoid long term

debt when internally generated resources are available. These resources are usually used to repay

debt, which is exactly what the pecking order theory foresees. It should be mentioned that the

Indonesian economy and market conditions differ from those under which the tested theories were

developed and consequently there are some aspects that need to be pointed out. First, the

Indonesian capital market has a secondary role in the capitalisation of Indonesian firms, both in

terms of stock or debt issues. Second, Indonesian interest rates, both short and long-term, are very

high in real terms. This, together with credit restrictions and the incentive given to banks to invest

in government bonds, there is a short supply of private credits. Long-term lending is virtually

supplied by the BNDES (the state-owned development bank) only with subsidized interest rates,

which is a situation extremely favourable to the pecking order theory.


From our results, we imply that manufacturing firms of the LQ45 Index prefer external to

internal financing and debt to equity if external financing is used. It follows the pecking order

theory.

How do we get our results? Our firm of sample prefers external to internal financing. The

plausible explanation is that:

1. Out of 26 firms in our sample, 24 firms are old ones. Older and more mature firms are

more closely followed by analysts and are better known to investors and, hence, should

suffer less from problems of information asymmetry. The theory‟s prediction that firms

with the greatest information asymmetry problems (specifically young and growth firms)

117

are precisely those that should be making financing choices according to the pecking

order.

However, in Indonesia all listed firms, including older-mature-large and young-growth-

small firms have less problems of information asymmetry as the government of Indonesia

has issued the regulations in order to make all listed firms announcing all information

about firms.

2. Firms in the manufacturing sector of the LQ45 Index firms have a good reputation to

mitigate the adverse selection problem between borrowers and lenders. In Indonesia, by

listing on the Indonesia Stock Exchange, banks will be more recognised and trusted than

companies. It is not excessive considering each moment banks can determine the condition

of the company's financials through various disclosure of information announced by the

company in the stock exchange. Rate of interest charged may also be lower considering

that the credit risk of public companies is relatively smaller.

3. Furthermore, older firms, more stable and highly profitable firms with few growth

opportunities and good credit histories are more suited to use external fund, both debt and

equity. Firms also have easier access to the company to enter into money markets through

the issuance of debt, both short and long term. Generally, the buyer of a letter of debt

would certainly prefer if the company issuing letters of debt has become a public company,

especially firms from the LQ45 Index.

4. However, some empirical evidence for the pecking order theory is inconsistent from our

results. The plausible explanation is that the Indonesian economy and market conditions

differ from those under which the previous research was developed.

If firms managers of manufacturing sector of the LQ45 issue equity, the most common

motivation based on the pecking order could be adverse selection developed by Myers and Majluf

(1984) and Myers (1984). The key idea is that the owner-manager of the firm knows the true value

of the firm‟s assets and growth opportunities. Outside investors can only guess these values. If the

manager offers to sell equity, then the outside investor must ask why the manager is willing to do

so. In many cases the manager of an overvalued firm will be happy to sell equity, while the

manager of an undervalued firm will not.

In the Indonesian capital market, by issuing equity, many benefits can be obtained by the

company including: obtaining large amounts of funds with costs of fund that are relatively smaller

than the funds obtained through banks, the various constraints and problems faced by the company

to survive and to develop are becoming the problems of stock holders by participating to think of

the best solutions so that the company can continue to grow, any increase in operational

performance and financial performance would have an impact on stock prices, which will

ultimately increase the value of the company.

6.2.5 Qualitative Analysis of Hypothesis 2

The following tables 6.5, 6.6a, 6.6b, 6.6c, 6.6d, are our research sample that consists of

26 firms and its classification, namely: growth (16 firms) and mature (10 firms), small (7 firms)

and large (19 firms), young (2 firms, INAF and KAEF) and old firms (24 firms).

118

Table 6.5. Research Sample

No. Firm No. Firm

1 ASII 14 INKP

2 AUTO 15 INAF

3 ADMG 16 INTP

4 BRPT 17 KLBF

5 BUDI 18 KOMI

6 CPIN 19 KAEF

7 DNKS 20 RMBA

8 FASW 21 SMCB

9 GGRM 22 SMGR

10 GJTL 23 TKIM

11 HMSP 24 TSPC

12 INDF 25 UNVR

13 INDR 26 SULI

Table 6.6a. Firm Classification

over Firm Life Cycle (Growth Firms)

No. Firm Life Cycle

1 ADMG Growth

2 BRPT Growth

3 BUDI Growth

4 CPIN Growth

5 DNKS Growth

6 FASW Growth

7 GJTL Growth

8 INDR Growth

9 INKP Growth

10 INAF Growth

11 INTP Growth

12 KOMI Growth

13 SMCB Growth

14 TKIM Growth

15 TSPC Growth

16 SULI Growth

Table 6.6b. Firm Classification

over Firm Life Cycle (Mature Firms)

No. Firm Life Cycle

1 ASII Mature

119

2 AUTO Mature

3 GGRM Mature

4 HMSP Mature

5 INDF Mature

6 KAEF Mature

7 KLBF Mature

8 RMBA Mature

9 SMGR Mature

10 UNVR Mature

Table 6.6c. Firm Classification over

Firm Life Cycle (Small Firms)

No. Firm Size

1 BUDI Small

2 DNKS Small

3 INAF Small

4 KOMI Small

5 KAEF Small

6 RMBA Small

7 TSPC Small

Table 6.6d. Firm Classification over

Firm Life Cycle (Large Firms)

No. Firm Size

1 ASII Large

2 ADMG Large

3 BRPT Large

4 CPIN Large

5 FASW Large

6 GGRM Large

7 GJTL Large

8 HMSP Large

9 INDF Large

10 INDR Large

11 INKP Large

12 INTP Large

13 KLBF Large

14 SMCB Large

15 SMGR Large

16 TKIM Large

120

17 UNVR Large

18 SULI Large

19 AUTO Large

Financing Deficit

Figure 6.1 implies the financing deficit of each firm, and its dependent variables which illustrated

by the following figures. Figure 6.2 shows net debt issue, figure 6.3 explains net equity issue, and

figure 6.4 describes newly retained earnings of each firm.

Figure 6.1. Financing Deficit of Each Firm

The firm that has the highest financing deficit is RMBA (mature-small-old firm), and

followed by SMCB (growth-large-old firm), FASW (growth-large-old firm), SULI (growth-large-

old firm), INKP (growth-large-old firm), and TKIM (growth-large-old firm). The firm that has the

lowest financing deficit is BRPT (growth-large-old firm), and followed by UNVR (mature-large-

old firm), TSPC (growth-small-old firm), GGRM (mature-large-old firm), KLBF (mature-large-

old firm), and DNKS (growth-small-old firm).

Financing Deficit of Each Firm

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

firm

%

Note: 1=ASII, 2=AUTO, 3=ADMG, 4=BRPT, 5=BUDI, 6=CPIN, 7=DNKS, 8=FASW, 9=GGRM,

10=GJTL, 11=HMSP, 12=INDF, 13=INDR, 14=INKP, 15=INAF, 16=INTP, 17=KLBF, 18=KOMI,

19=KAEF, 20=RMBA, 21=SMCB, 22=SMGR, 23=TKIM, 24=TSPC, 25=UNVR, 26=SULI

121

Figure 6.2. Net Debt Issue

The firm that has the highest net debt issue is RMBA, followed by FASW, BUDI, CPIN, HMSP,

and INDF. The firm that has the lowest net debt issue is ADMG, followed by SULI, BRPT,

KAEF, INTP, and GJTL.

Figure 6.3 Net Equity Issue

Net Debt Issue

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

firm

%

Net Equity Issue

-0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

firm

%







122

The firm that has the highest net equity issue is INAF, followed by INAF, BUDI, KOMI, RMBA,

and SMCB. The firm that has the lowest net equity issue is KAEF, followed by GGRM, INDF,

KLBF, ASII, and HMSP.

Figure 6.4. Newly Retained Earning

The firm that has the highest NRE is KAEF, followed by GGRM, KOMI, UNVR, TSPC, ADMG,

and HMSP. The firm that has the lowest NRE is SMCB, followed by FASW, BRPT, SULI, INTP,

INKP, and TKIM.

Capital Structure

Figure 6.5 implies firms capital structure which consists of newly retained earning, net

equity issue, and net debt issue overall of 26 firms. Meanwhile, figure 6.6 shows aggregates of

financial deficit, these are long-term leverage, fixed asset, dividend, change in working capital,

and net income, where all aggregates are divided by total asset.

Figure 6.5. Firms Capital Structure

Note:1=newly retained earning, 2=net equity issue, 3=net debt issue


-0.1

-0.05

0

0.05

0.1

0.15

0.2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

firm

%

0

0.1

0.2

%

1 2 3

Capital Structure




123

The capital structure which has the highest composition to overcome financing deficit is

the net debt, followed by the net equity. It is supported also by the results of regression tests which

concluded that the net debt issues, instead of the net equity issues, are more influenced by the

financial deficit of the company.

Figure 6.6. Aggregate of Financial Deficit

The highest aggregate of financial deficit is fixed asset to total asset while the lowest is

long-term liability to total asset. The firms which have issued more net debt than net equity are

ASII, AUTO, BUDI, CPIN, DNKS, FASW, GGRM, HMSP, INDF, INDR, INKP, KLBF, RMBA,

SMGR, TKIM, TSPC, and UNVR. The firms consist of growth firms (8 firms including BUDI,

CPIN, DNKS, FASW, INDR, INKP, TKIM, and TSPC), and mature firms (9 firms including

ASII, AUTO, GGRM, HMSP, INDF, KLBF, RMBA, SMGR, and UNVR). Hence, the 17

mentioned firms follow pecking order theory. The firms which have issued more net equity than

the net debt are ADMG, BRPT, GJTL, INAF, INTP, KOMI, KAEF, SMCB, and SULI. All of

these firms are growth firms except KAEF.

Newly Retained Earning, Net Debt Issue, Net Equity Issue, and Financing Deficit

Table 6.7 describes a firm‟s capital structure. Within the research period of 1994-2007, firms that

had a negative average of newly retained earning were BRPT, FASW, INKP, INTP, SMCB, and

SULI. All of these 6 firms were growth firms, while five out of six firms were large firms except

SULI. However, two out of six firms, INKP and SMCB have ever had the negative net equity

while they had positive net debt.

Within the research period of 14 years, the firm that had a negative average of net equity

issue was KAEF as a mature-small-young firm. Meanwhile, firms that had a negative average of

net debt issue were ADMG, BRPT, INTP, KAEF, and SULI. Four out of five firms were growth

firms except for KAEF, and three out of five firms were large firms except for KAEF and SULI.

Negative average of net debt issue indicated that firms paid their debt.

Table 6.7. The Firm’s Capital Structure

Firms FD NDEBT NEQUITY NRE

ASII 0.246289 0.061464 0.009313 0.042845

AUTO 0.325671 0.05574 0.042183 0.040869

Agregate of Financial Deficit

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

1 2 3 4 5

1=LTL/TA 2=FA/TA 3=DIV/TA 4=change in WC/TA 5=NI/TA

%

124

ADMG 0.484154 -0.25791 0.060644 0.069033

BRPT 0.069466 -0.09401 0.052395 -0.02841

BUDI 0.698215 0.209711 0.201106 0.035606

CPIN 0.514908 0.136836 0.051208 0.016976

DNKS 0.233196 0.117662 0.022378 0.026532

FASW 0.894229 0.247511 0.093016 -0.03372

GGRM 0.194664 0.047786 0.002338 0.096525

GJTL 0.52135 0.009762 0.017907 0.008259

HMSP 0.317986 0.13326 0.014735 0.07053

INDF 0.531629 0.129649 0.00349 0.027858

INDR 0.649509 0.086615 0.040038 0.022252

INKP 0.754023 0.081828 0.0496 -0.00104

INAF 0.318097 0.125295 0.266026 0.062635

INTP 0.491431 -0.00348 0.028975 -0.00244

KLBF 0.218019 0.036872 0.00764 0.057149

KOMI 0.271851 0.017977 0.201452 0.095146

KAEF 0.284279 -0.04839 -0.0334 0.178833

RMBA 1.157571 0.265378 0.191678 0.015732

SMCB 0.951745 0.045728 0.14223 -0.0775

SMGR 0.661141 0.090224 0.086827 0.022076

TKIM 0.736859 0.104332 0.040307 0.002906

TSPC 0.184621 0.066072 0.035862 0.078918

UNVR 0.13224 0.056314 0.034405 0.085179

SULI 0.79252 -0.12123 0.215961 -0.02187

Interestingly, KAEF had the negative average of net debt and net equity issue, it indicated

that the firm paid the debt and repurchased the equity within the research period. However, within

the research period of 14 years, all firms have experienced the financing deficit which was

indicated by the positive sign of financing deficit.

BRPT, INTP, and SULI, had the negative sign of net debt issue and newly retained

earning; it implied that even though they had negative newly retained earning, they decided to pay

their debt. All of the firms were growth firms which did not pay dividend regularly in six years.


Table 6.8 shows the value of newly retained earning, net debt issue, net equity issue, and financing

deficit.

ASII had a negative value of net equity and a positive value of net debt in the years 2000,

2005, and 2007. ADMG had a negative net equity and a positive net debt in 2001. CPIN had a

negative net equity and a positive net debt in 1998. DNKS had a negative value of net equity and a

positive net debt in the year 2000 and 2001.

GGRM had a negative value of net equity and positive net debt in 2000, 2001, and 2003.

GJTL had a negative value of net equity and positive net debt in the year 1998, 2000, and 2001.

HMSP had a negative net equity and positive net debt in 2001.

125

INDF had a negative net equity and a positive net debt in 2002. INKP had a negative net

equity and a positive net debt in the years 1999 and 2003. INAF had a negative net equity and a

positive net debt in 2002. KLBF had a negative value of net equity and a positive net debt in the

years 1997, 2001, and 2007. SMCB had a negative net equity and a positive net debt in 1999-

2000. SMGR had a negative net equity and a positive net debt in 1998-2000. UNVR had a

negative value of net equity and positive net debt in 2001.

Table 6.8. The Value of Newly Retained Earning, Net Debt Issue, Net Equity Issue, and

Financing Deficit

Firms Year Newly

Retained

Earning

Repurchase

Equity

Issue Debt Financing

Deficit

ASII

(large-mature

firm)

2000 0.083505247 -0.094685929 0.2015054 0.8550144

2005 0.084511677 -0.000673288 0.083037532 0.37542806

2007 0.073955065 -0.00174384 0.015797959 0.343649514

ADMG

(large-growth

firm)

2001 -0.230395473 -0.001471987 0.179544341 1.300327061

CPIN

(large-growth

firm)

1998 -0.005971524 -0.022074149 0.213562172 0.47214413

DNKS

(small-growth

firm)

2000 0.093221007 -0.004470623 0.076558907 0.630562377

2001 0.072408449 -0.000459094 0.080552531 0.581196283

GGRM

(large-mature

firm)

2000 0.029431086 -0.000144607 0.225830025 0.205849014

2001 0.155215776 -2.05977E-05 0.038506858 0.181018483

2003 0.07275237 -1.5918E-05 0.036047618 0.227989418

GJTL

(large-growth

firm)

1998 0.002980209 -0.001386912 0.191662927 1.155574689

2000 -0.102718813 -0.005802868 0.285555047 1.286702285

2001 -0.183565324 -0.001698518 0.200972425 0.898055012

HMSP

(large-mature

firm)

2001 0.04810845 -0.012238795 0.063990015 0.514639991

INDF

(large-mature

firm)

2002 0.038009008 -0.031379045 0.142365979 0.876375743

INKP

(large-growth

firm)

1999 -0.022459613 -0.045864651 0.003060718 0.95402555

2003 -0.049895818 -0.021819506 0.001376357 0.710517226

126

INAF

(small-growth

firm)

2002 0.01890907 -9.50205E-06 0.017088241 0.139112319

KLBF

(large-mature

firm)

1997 -0.052110644 -0.007027881 0.465615684 0.243849179

2001 0.017400374 -0.000205613 0.046446629 0.839836767

2007 0.092558286 -0.023619687 0.080026337 0.313407658

SMCB

(large-growth

firm)

1999 0.001814411 -0.264012967 0.209955396 0.615446007

2000 -1.019071741 -0.004922722 0.699748236 1.87377103

SMGR

(large-mature

firm)

1998 -0.004681704 -1.4107E-07 0.258969715 1.205612352

2000 0.032699452 -0.000624729 0.017395451 1.112214508

UNVR

(large-mature

firm)

2001 0.118556144 -0.005690993 0.046673976 0.077622313

Table 6.8 shows the value of newly retained earning, net debt issue, net equity issue, and

financing deficit. It shows that firms that have negative net equity issue are ASII, ADMG, CPIN,

DNKS, GGRM, GJTL, HMSP, INDF, INKP, INAF, KLBF, SMCB, SMGR, and UNVR. It

implies that the firms have repurchased their equity. However, based on regression result,

coefficient of correlation obtained from variable net debt issue and repurchase equity is negative

but not significant. It explains that firm‟s has not issued debt to repurchase their equity.

Coefficient of correlation obtained from variable net debt and financing deficit is positive

significant. It explains that firm‟s issued debt to solve their financing deficit.

Half of the firms which repurchase equity are mature firms and the rest are growth firms.

However, 12 out of 14 firms repurchase equity of large firms while the rest repurchase equity of

small firms. It means that repurchase equity is mostly done by large firms which have large

amount of total asset.

ASII repurchased its equity in 2000, 2005, and 2007 with the percentages of 9.45%,

0.067%, and 0.174%. ADMG repurchased its equity in 2001 with the amount of 0.147%. CPIN

repurchased its equity in 1998 with the amount of 2.2%. DNKS repurchased its equity in 2000-

2001 with the amount of 0.45% and 0.045%. GGRM repurchased its equity in 2000, 2001, 2003

with the amount of 0.014%, 2.05977E-03%, and 1.5918E-03%. GJTL repurchased its equity in

1998, 2000, 2001, with the amount of 0.138%, 0.58%, 0.169%. HMSP repurchased its equity in

2001 with the amount of 1.22%, INDF repurchased its equity in 2002 with the amount of 3.14%,

and INKP repurchased its equity in 1999 and 2003 with the amount of 4.58%, 2.18%. INAF

repurchased its equity in 2002 with the amount of 9.50205E-04%, KLBF repurchased its equity in

1997, 2001 and 2007 with the amount of 0.702%, 0.02%, 2.36%. SMCB repurchased its equity in

1999 and 2000 with the amount of 26.4%, 0.49%. SMGR repurchased its equity in 1998 and 2000

with the amount of 1.4107E-05%, 0.0624729%. UNVR repurchased its equity in 1998 and 2000

with the amount of 0.5690993%. These indicate that in these mentioned years, the firms have more

excess funds.

127

The positive sign of net debt issue, the negative sign of net equity issue, the positive sign

of newly retained earning and financing deficit at the same time, indicated that the 14 firms have

issued debt, decreased their equity composition, at the time they had newly retained earning and

they were experiencing financing deficit. It indicates that mature firms issue debt, pay dividend,

and repurchase their equity, when they have newly retained earning.

However, the 3 remaining mature firms AUTO, KAEF, and RMBA have not repurchased

equity and it meant that the firms preferred to pay dividend to repurchase equity when they had

excess funds.

Surplus

Table 6.9 implies the firm‟s surplus. Based on annual average data of financing deficit,

firms which experienced a surplus during the period of study were ASII, ADMG, BRPT, GJTL,

INAF, KLBF, KAEF, RMBA, TSPC, and UNVR. ASII had a surplus of 7.2% in 2003. ADMG

had a surplus of 28% in 2004. BRPT experienced a surplus of 76% and 26% in 2005-2006. GJTL

had 65% surplus in 2004. INAF had a surplus of 13% and 1.65% in 1999-2000. KLBF had

surplusses of 3.5%, 15.86%, and 9.06% in 1997, 2002, and 2006. KAEF had a surplus of 14.05%

in 1999. RMBA had a 4.83% surplus in 1997. TSPC had a 9.83% and a 55.81% surplus in the

years 1996 and 1999. UNVR had surplusses in the year 2000, 2003, 2004, and 2006 of 2.38%,

10.7%, 1.71%, and 17.37%.

Table 6.9. Firm’s Surplus

Firm Year % Surplus

ASII 2003 -0.072029157 Surplus

ADMG 2004 -0.280020082 Surplus

BRPT 2005 -0.759917408 Surplus

2006 -0.260283818 Surplus

GJTL 2004 -0.648184854 Surplus

INAF 1999 -0.129034314 Surplus

2000 -0.016552298 Surplus

KLBF 1997 -0.035090159 Surplus

2002 -0.15868839 Surplus

2006 -0.09065936 Surplus

KAEF 1999 -0.140533486 Surplus

RMBA 1997 -0.048368201 Surplus

TSPC 1996 -0.09829475 Surplus

1999 -0.558142346 Surplus

UNVR 2000 -0.023845269 Surplus

2003 -0.107000213 Surplus

2004 -0.017121084 Surplus

2006 -0.173690878 Surplus

ASII, KLBF, KAEF, RMBA, and UNVR are mature firms, while the rest are growth

firms. Although all five firms are mature firms that indicate they are the dividend payer, those

firms still have a surplus because their cashflows exceed the dividend, capital expenditure, current

128

assets, and the LTD payment. ASII, ADMG, BRPT, GJTL, KLBF, and UNVR are large firms,

while the rest are small firms. UNVR is the most frequently experienced the surplus (4 years),

KLBF got 3 years of surplus, BRPT, INAF, and TSPC got 2 years of surplus, ASII, ADMG,

GJTL, KAEF, and RMBA got 1 year of surplus.

Correlations Analysis

Table 6.10 implies that profitability and newly retained earning have positive significant

correlations, which means that the firm which has higher profitability can issue more newly

retained earning. Tangibility and newly retained earning have negative significant correlations. It

means that the firms which issue more newly retained earning have lower tangibility. Size and

newly retained earning have negative but not significant correlation, growth and newly retained

earning have negative but not significant correlation. It means that the firm which issue more

newly retained earning has smaller size, and lower growth, but not significant. Risk and newly

retained earning have negative significant correlation, it means that the firm which issue more

newly retained earning has lower risk, and significance.

Table 6.10a. Correlations between Variables

NRE NEQUITY NDEBT FD

NRE Pearson Correlation 1 -.095 -.370**

-.277**

Sig. (2-tailed) .203 .000 .000

NEQUITY Pearson Correlation -.095 1 -.267**

.225**

Sig. (2-tailed) .203 .000 .002

NDEBT Pearson Correlation -.370**

-.267**

1 .347**

Sig. (2-tailed) .000 .000 .000

FD Pearson Correlation -.277**

.225**

.347**

1

Sig. (2-tailed) .000 .002 .000

Table 6.10b. Correlations between Variables

PRFT TANG SIZE RISK GROWTH

NRE Pearson Correlation .654**

-.227**

-.088 -.444**

-.072

Sig. (2-tailed) .000 .001 .189 .000 .280

NEQUIT

Y

Pearson Correlation -.123 .126 -.186* .085 -.106

Sig. (2-tailed) .100 .092 .012 .321 .157

NDEBT Pearson Correlation -.110 -.046 -.097 .134 -.053

Sig. (2-tailed) .102 .498 .146 .081 .433

FD Pearson Correlation -.461**

.551**

.150* .111 -.058

Sig. (2-tailed) .000 .000 .022 .140 .379

Growth and net equity issue have negative but not significant correlation, profitability

and net equity issue have negative but not significant correlation; it means that the firm which has

higher profitability and higher growth issues less net equity, but not significantly. Tangibility and

129

net equity issue have positive but not significant correlation, risk and net equity issue have positive

but not significant correlation, it means that the firm which has higher tangibility and higher risk

issues more net equity, but not significantly. Size and net equity issue have negative significant

correlation, it means that the firm which has larger size and issue less net equity, and significantly.

Profitability and net debt issue have negative but not significant correlation, tangibility

and net debt issue have negative but not significant correlation, size and net debt issue have

negative but not significant correlation, growth and net debt issue have negative but not significant

correlation; it means that the firm which has larger size, higher profitability, tangibility, and

growth, issues less net equity, but not significantly. Risk and net debt issue have positive but not

significant correlation. It means that the firm which has higher risk, issues more net debt, but not

significantly.

Profitability and financing deficit have negative significant correlation; it means that the

firm which has higher financing deficit has lower profitability, and the correlation is significant.

Growth and financing deficit have negative but not significant correlation; it means that the firm

which has higher financing deficit has lower growth, but it is not significant. Tangibility and

financing deficit have positive significant correlation, Size and financing deficit have positive

significant correlation, it means that the firm which has higher financing deficit, is a larger firm,

and has higher asset tangibility, and the correlation is significant. Risk and financing deficit have

positive but not significant correlation, it means that the firm which has higher financing deficit, is

high risk firm, but not significant.

6.3. Research Question 3, Hypothesis, Hypothesis Testing, and Result Analysis

As applied in hypothesis 1, we also use quantitative strategy in testing hypothesis 3. The following

sub-sections are explaining research questions three, hypotheses three, hypotheses testing three,

and results analysis.

6.3.1. Research Question Three

Based on the asymmetric information and signalling theory, our major and minor

research questions are as follow:

Does debt policy matter?

(a) If firms issue new debt, what will happen to the firm‟s stock price?

(b) If firms issue new equity, what will happen to the firm‟s stock price?

(c) If firms issue debt to repurchase equity, what will happen to the firm‟s stock price?

6.3.2. Hypothesis 3

By formulating research question three, therefore, our major and minor hypotheses three

are as follow:

Debt does policy matter.

(a) If firms issue new debt, then the firms‟s stock price will be higher.

(b) If firms issue new equity, then the firms‟s stock price will be lower.

(c) If firms issue debt to repurchase equity, then the firms‟s stock price will be higher.

130


As described in chapter 5, multiple regression analysis is selected to test hypothesis 3. For

testing hypothesis 3, the independent variables are the net debt issue, the net equity issue, and the

debt issued to repurchase equity, whereas the dependent variable is stock price. The objective of

regression analysis is to examine to what extent the influence of those independent variables.


The following sub-chapters are consisting of analysis of result and consistency of result

with the theory and previous research, and also how its condition in Indonesia capital market.

6.3.4.1 Analysis of Result and Its Consistency with the Theory and Previous Research

Table 6.11-6.13 shows the regression results of hypothesis testing 3a, 3b, and 3c, which

tested net debt issue, net equity issue, and net debt issue to repurchase equity, on monthly and

yearly stock price.

A. Regression Results of Hypothesis 3a

Table 6.11 explains the influence of net debt issue on stock price from January to

December and the impact of net debt issue on the yearly stock price.

Table 6.11. Regression Results of Hypothesis Testing 3a

Coefficientsa

Model Unstanda

rdised

Coefficients

Standardi

sed Co-

efficients

t Sig. Collinearity

Statistics

B Beta Tolera

nce

VIF

Jan

(Constant) 2669.888 5.806 .000

NDEBT 3343.817 .189 1.789 .077 1.000 1.000

Feb

(Constant) 2641.563 5.812 .000

NDEBT 3189.333 .183 1.727 .088 1.000 1.000

Mar

(Constant) 2542.863 5.960 .000

NDEBT 3032.894 .185 1.749 .084 1.000 1.000

Apr

(Constant) 2529.530 6.288 .000

NDEBT 2714.026 .176 1.673 .098 1.000 1.000

May

(Constant) 2590.855 6.400 .000

NDEBT 2812.141 .181 1.722 .089 1.000 1.000

Jun

(Constant) 2588.295 6.510 .000

NDEBT 2780.620 .182 1.734 .086 1.000 1.000

Jul

(Constant) 2674.262 6.287 .000

NDEBT 2848.535 .172 1.652 .102 1.000 1.000

Aug

(Constant) 2520.000 6.340 .000

NDEBT 2641.924 .171 1.639 .105 1.000 1.000

Sep

(Constant) 2524.109 6.508 .000

NDEBT 2521.638 .164 1.590 .115 1.000 1.000

Oct

(Constant) 2491.144 6.227 .000

NDEBT 2638.081 .164 1.604 .112 1.000 1.000

(Constant) 2544.752 6.289 .000

131

Nov NDEBT 2669.798 .161 1.589 .115 1.000 1.000

Dec

(Constant) 2642.126 6.410 .000

NDEBT 2786.625 .159 1.598 .113 1.000 1.000

yearly (Constant) 3557.769 5.286 .000

NDEBT 945.164 .027 .383 .702 1.000 1.000

Dependent Variable: P_yearly F=0.146 (0.702) ; R-squared=0.001 ; N=196

The t-values of net debt on January to June were 1.789; 1.727; 1.749; 1.673; 1.722; and

1.734. But these t-values did not have the significance value of 0.077; 0.088; 0.084; 0.098; 0.089;

and 0.086 consecutively. Actually, stock price from January to June got almost the positive

significant impact, but it needed more data and a longer period of sampling to make the result

significant.

The t-values of net debt on stock price from July to December were 1.652; 1.639; 1.590;

1.604; 1.589; and 1.598. Those, t-value did not have the significance value of 0.102; 0.105; 0.115;

0.112; 0.115; and 0.133 consecutively from January to December. It indicated that net debt got no

significant impact on the stock price of January to December.

The t-value of net debt on yearly stock price was 0.383 and got positive but not

significance value of 0.702. This indicated that net debt got no significant impact on the yearly

stock price.

Here is the explanation of our result. When we compared the results to the theory of

predictions, we first analysed the theory of predictions for debt issues and equity issues. When a

firm issued, repurchased or exchanged one security for another, it changed its capital structure.

What were the valuation effects of these changes? There were several theories that explained the

relationship between capital structure and stock price.

Along with the increased level of leverage accompanied by higher risk of bankruptcy, the

increased level of debt indicates the confidence level of the management in the future. Hence, it

carries greater conviction than a mere announcement of undervaluation of the firm, by the

management. On the other hand, an issue of equity is a signal that the firm is overvalued. The

market concludes that the management has decided to offer equity because it is valued higher than

it has been valued intrinsically by the market. The markets normally react favourably to moderate

increases in leverage and negatively to fresh issue of equity.

Under the trade-off theory, the market reaction to both equity and debt securities will be

the following: (1) Debt issues. The market response to a leverage change confounds information:

necessitating financing and the effect of the financing on security valuations. The information

contained in security issuance decisions could be either good news or bad news. It would be good

news if the firm is issuing securities to take advantage of a promising new opportunity that was not

previously anticipated. It might be bad news if the firm is issuing securities because the firm

actually needs more resources than anticipated to conduct operations. (2) Equity issues. Jung et al.

(1996) suggested an agency perspective and argued that equity issues by firms with poor growth

prospects reflected agency problems between managers and shareholders. If this is the case, then

stock prices would react negatively to news of equity issues.


adverse selection (equity) will result in more negative market reaction. Securities with less adverse

132

selection (debt) will result in less negative or no market reaction. This of course, still rest on some

assumptions about market anticipations.

Conclusion: Our result is positive. Therefore, our result is consistent with signalling

through capital structure, as the increased level of leverage is accompanied by higher risk of

bankruptcy, the increased level of debt indicates the confidence of the management in the future.

Hence it carries greater conviction than a mere announcement of undervaluation of the firm by the

management.

Our result is also consistent with the pecking order theory, as securities with less adverse

selection (debt) will result in less negative or no market reaction. Finally, our result is in line with

trade off theory. If the firm issued securities to take advantage of a promising new opportunity, so

it would be good news to the market.

If compared to previous empirical evidence, our result is consistent with the following

findings, for example, announcements of ordinary debt issues generate zero market reaction on

average (Eckbo (1986) and Antweiler and Frank (2006)). The zero market reaction to corporate

debt issues is robust to various attempts to control for partial anticipation. Meanwhile, exchange of

common for debt/preferred stock generates positive stock price reactions while exchange of

debt/preferred for common stock generates negative reactions (Masulis, 1980a). Eckbo and

Masulis (1995) concluded that announcements of security issues typically generated a non-positive

stock price reaction.


portion of debt in their capital structure. Ross (1977) assumed firms that were less well

performancing would not use debt in large portion as it would be followed by the high chance of


higher debt, while firms that are less good performance will use more of equity. Ross (1977)


company's capital structure and they will give a higher value on the company with larger debt

portion. It indicates that the result do not support the stated of signalling theory. The result

indicates that the greater the leverage, the greater the possibility of financial distress leading to

bankruptcy. When the company went bankrupt, shareholders would lose money they invest in the

company (Peirson et al, 2002).

However, our result is inconsistent with the following empirical evidence. In Indonesia,

the regression coefficient between leverage and stock price is significantly negative. The use of

high leverage will be responded by the market with a fall in stock prices. These results are

consistent with the findings of a negative relationship between leverage and stock price as

proposed by Frank and Goyal (2003). Relationship between the two variables will be positive at


Announcements of convertible debt issues resulted in mildly negative stock price

reactions (such as Dann and Mikkelson, 1984; Mikkelson and Partch, 1986). The valuation effects

are the most negative for common stock issues, slightly less negative for convertible debt issues

and least negative (zero) for straight debt issues. The effects are more negative the larger the issue.

The reason of why firms issue debt could be the intention to take advantage, eventhough

there would be the disadvantages of debt. Indonesia companies face the challenge of determining

whether to issue debt or equity for financing needs.

133

B. Regression Result of Hypothesis 3b

The following table 6.12 explains the influence of net equity issue on stock price from

January to December and the impact of net equity issue on the yearly stock price based on

regression results.

Table 6.12. Regression Result of Hypothesis Testing 3b

Coefficients

Model Unstandar

dised Coef

ficients

Standar

dised Co-

efficients

t Sig. Collinearity

Statistics

B Beta Tolera

nce

VIF

Jan

(Constant) 2576.337 5.485 .000

NEQUITY -1994.990 -.067 -.627 .533 1.000 1.000

Feb

(Constant) 2553.475 5.507 .000

NEQUITY -1938.788 -.066 -.617 .539 1.000 1.000

Mar

(Constant) 2458.847 5.647 .000

NEQUITY -1835.863 -.067 -.622 .536 1.000 1.000

Apr

(Constant) 2476.652 6.006 .000

NEQUITY -1778.643 -.067 -.632 .529 1.000 1.000

May

(Constant) 2533.633 6.099 .000

NEQUITY -1767.766 -.066 -.623 .535 1.000 1.000

Jun

(Constant) 2532.026 6.205 .000

NEQUITY -1757.588 -.067 -.631 .530 1.000 1.000

Jul

(Constant) 2637.624 6.031 .000

NEQUITY -2176.638 -.080 -.753 .454 1.000 1.000

Aug

(Constant) 2485.666 6.083 .000

NEQUITY -2009.016 -.079 -.743 .459 1.000 1.000

Sep

(Constant) 2504.131 6.281 .000

NEQUITY -2098.576 -.082 -.787 .433 1.000 1.000

Oct

(Constant) 2456.075 5.983 .000

NEQUITY -2035.811 -.076 -.734 .465 1.000 1.000

Nov

(Constant) 2507.174 6.047 .000

NEQUITY -2027.171 -.073 -.716 .475 1.000 1.000

Dec

(Constant) 2590.482 6.146 .000

NEQUITY -1882.064 -.064 -.642 .523 1.000 1.000

Yearly

(Constant) 3823.851 5.580 .000

NEQUITY -4402.595 -.067 -.934 .352 1.000 1.000

Dependent Variable: P_yearly, F=0.872 (0.352) ; R-squared=0.004 ; N=196

The t-value of net equity on stock price of January to December were -0.627; -0.617; -

0.622; -0.632; -0.623; -0.631; -0.753; -0.743; -0.787; -0.734; -0.716; and -0.642. It did not have

the significance-value of 0.533; 0.539; 0.536; 0.529; 0.535; 0.530; 0.454; 0.459; 0.433; 0.465;

0.475; and 0.523 consecutively from January to December.

134

The t-value of net equity on yearly stock price was -0.934 and got negative but not

significance value of 0.352. This indicated that net equity had no significant impact on the yearly

stock price. This result suggests that firms that issue more net equity would tend to have

decreasing stock price. Thus, we fail to reject the hypothesis that if firms issue new equity, then

the firm‟s stock price will be lower.

Here is the explanation of our result. When we compared the results to the theory of

predictions, our results were consistent with the theory of signalling through capital structure,

pecking order theory, and Jung et al. (1996). Jung et al. (1996) suggested an agency perspective

and argued that equity issues by firms with poor growth prospects reflected agency problems

between managers and shareholders. If this is the case, then stock prices would react negatively to

news of equity issues. Our results were consistent with the following empirical evidence, for

instance, announcements of equity issues resulted in significant negative stock price reactions

(Asquith and Mullins Jr., 1986; Masulis and Korwar, 1986; and Antweiler and Frank, 2006).

The negative market reaction to equity issues and zero market reaction to debt issues are

consistent with adverse selection arguments. Indeed, there is other interpretation. Jung et al. (1996)

showed that firms without valuable investment opportunities experienced a more negative stock

price reaction to equity issues than did firms with better investment opportunities. Thus, agency

cost arguments could also explain the existing evidence on security issues. Further support for the

agency view came from the finding that firms without valuable investment opportunities issuing

equity invested more than similar firms issuing debt and that firms with low managerial ownership

had worse stock price reaction to new equity issue announcements than did firms with high

managerial ownership.

Meanwhile, our results were inconsistent with the following empirical evidence. The

impact of equity issues appears to differ between countries. Several studies found positive market

reaction to equity issues around the world (Eckbo et al., 2007). To understand this evidence,

Eckbo and Masulis (1992) and more recently Eckbo and Norli (2004) examined stock price

reactions to equity issues conditional on a firm‟s choice of flotation method. Firms can issue

equity using uninsured rights, standby rights, firm commitment underwriting and private

placements. The stock price reactions to equity issues depend on the floatation method. For U.S.

firms Eckbo and Masulis (1992) it was found that the average announcement-period abnormal

returns were insignificant for uninsured rights offerings and they were significantly negative for

firm-commitment underwritten offerings. Eckbo and Norli (2004) studied equity issuances on the

Oslo Stock Exchange. They found that uninsured rights offerings and private placements resulted

in positive stock price reactions while standby rights offerings generated negative market

reactions. These papers interpreted the effect of the flotation method as reflecting different degrees

of adverse selection problems.

C. Regression Result of Hypothesis 3c

Table 6.13 shows the influence of net debt issue and repurchase equity on stock price

from January to December and the impact of net debt issue and repurchase equity on the yearly

stock price.

Table 6.13. Regression Result of Hypothesis Testing 3c (Firms which Repurchased Stock)

Model Unstandar

dised Co-

efficients

Standar

Dised Co-

efficients

t Sig. Collinearity

Statistics

135

B Beta Tolera

nce

VIF

Jan (Constant) 9549.399 1.877 .110

NDEBT -4686.999 -.033 -.051 .961 .319 3.135

NEQUITY 115471.516 .379 .575 .586 .319 3.135

Feb (Constant) 9629.505 1.875 .110

NDEBT -9220.515 -.065 -.099 .924 .319 3.135

NEQUITY 106415.517 .347 .525 .619 .319 3.135

Mar (Constant) 8666.033 1.925 .103

NDEBT 5385.242 .043 .066 .950 .319 3.135

NEQUITY 130984.979 .478 .737 .489 .319 3.135

Apr (Constant) 7950.787 2.004 .092

NDEBT 16417.744 .145 .228 .827 .319 3.135

NEQUITY 145659.672 .590 .930 .388 .319 3.135

May (Constant) 7985.908 1.974 .096

NDEBT 7134.492 .063 .097 .926 .319 3.135

NEQUITY 121320.210 .492 .759 .476 .319 3.135

Jun (Constant) 7418.618 1.961 .098

NDEBT 9641.753 .091 .140 .893 .319 3.135

NEQUITY 116524.608 .508 .780 .465 .319 3.135

Jul (Constant) 8249.616 1.903 .106

NDEBT 1422.511 .012 .018 .986 .319 3.135

NEQUITY 107359.119 .415 .627 .554 .319 3.135

Aug (Constant) 7620.698 1.894 .107

NDEBT 10576.200 .095 .145 .890 .319 3.135

NEQUITY 119145.242 .493 .750 .482 .319 3.135

Sep (Constant) 6850.980 1.922 .103

NDEBT 22634.964 .224 .350 .739 .319 3.135

NEQUITY 138803.313 .631 .986 .362 .319 3.135

Oct (Constant) 4985.450 1.752 .130

NDEBT 41427.820 .492 .802 .453 .319 3.135

NEQUITY 156236.835 .854 1.391 .214 .319 3.135

Nov (Constant) 5350.611 1.680 .144

NDEBT 50616.898 .535 .875 .415 .319 3.135

NEQUITY 181312.980 .881 1.442 .200 .319 3.135

Dec (Constant) 6117.175 2.361 .050

NDEBT 34238.389 .373 .895 .401 .562 1.779

NEQUITY 148939.412 .733 1.755 .123 .562 1.779

Yearly (Constant) 6510.521 3.710 .001

NDEBT -10125.124 -.284 -1.358 .190 .998 1.002

NEQUITY 21513.638 .211 1.010 .324 .998 1.002

Dependent Variable: P_yearly, F=1.491 (249) ; R-squared=0.130 ; Adjusted R-

squared=0.043 ; N=23

The t-value of repurchasing equity on stock price of January to December was positive

but the result was not significant. The t-value of repurchasing equity on yearly stock price was

positive but neither was it significant.

136

This result suggested that the firms that repurchased equity would tend to have increasing

stock price. Thus, we failed to reject the hypothesis that if firms repurchased equity, then the

firm‟s stock price would be higher.

Negative sign means that the more the debt is issued, the lower the price goes, whereas a

positive sign means the more the firms repurchase equity, the higher the price goes. Based on

undervaluation hypothesis: Repurchases and investment policy: repurchasing stock offered

flexibility not only for the option taken on distributing the excess of funds but also when to

distribute these funds. This flexibility in timing is beneficial because firms can wait to repurchase

until the stock price is undervalued. The undervaluation hypothesis is based on the premise that

information asymmetry between insiders and shareholders may cause a firm to be misvalued. If

insiders believe that the stock is undervalued, the firm may repurchase stock as a signal to the

market to invest in its own stock and acquire mispriced shares. According to this hypothesis, the

market interpreted the action as an indication that the stock was undervalued (in Amy K. Dittmar

(1999). Because of the asymmetric information between managers and shareholders, share

repurchase announcements are considered to reveal private information that managers have about

the value of the company.

The signalling hypothesis has three immediate implications: repurchase announcements

should be accompanied by positive price changes; repurchase announcements should be followed

(though not necessarily immediately) by positive news about profitability or cash flows; and

repurchase announcements should immediately be followed by positive changes in the market‟s

expectation about future profitability (in Gustavo Grullon and Roni Michaely, 2002).

When we compared the results to the previous research, many studies showed that

repurchases were associated with a positive stock price reaction, for example Vermaelen (1981),

Dann (1981), and Comment and Jarell (1991) found that the positive stock price reaction at the

announcement of a stock repurchase program should correct the misevaluation.

Ikenberry, Lakonishok and Vermaelen (1995) showed that this increase might not be

sufficient to correct the price since the repurchasing firms, particularly low market to book firms,

have earned a positive abnormal return during the four years subsequent to the announcement. The

amount of information available and the accuracy of the valuation of firms by the market can

affect firms‟ repurchase decisions. Ikenberry et al. (1995) have studied abnormal returns following

share repurchase announcements. They found out that the average instant (two days before through

two days after) reaction to the announcement was 3.54 percent. The average long-term (four-year

buy-and-hold) abnormal return was 12.1 percent.

According to Jensen (1986), firms repurchased stock to distribute the excess of cash flow.


between repurchases and the levels of cash flow. Stephens and Weisbach also showed that

repurchase activity was negatively correlated with prior stock returns, indicating that firms

repurchased stock when their stock prices were perceived as undervalued. This result agrees with

Vermaelen‟s (1981) findings that firms repurchase stock to signal undervaluation. Thus, firms

repurchase stock when they are undervalued and have the excess of cash to distribute.

Masulis (1980b), Dann (1981), and Antweiler and Frank, (2006) also found that the

announcement effects were positive when common stock was repurchased. Brav et al. (2005.b.)

discovered on their survey that only 22.5 percent of executives believed that reducing repurchases

had negative consequences. On the other hand, almost 90 percent thought that reducing dividends

had negative consequences.

137

Overall, our result is in line with the signalling hypothesis that has immediate

implications: repurchase announcements should be accompanied by positive price changes. It is

also consistent with many empirical evidences.


In Indonesia, why can the stock price go up and down? Stock price movements are

determined by supply and demand for these shares. Demand increases, the stock price increases

and vice versa. Factors that affect stock price movements include the movements in interest rates,

inflation, exchange rate of the Rupiah, performance of the company such as sales and profit

increases, for dividends and so on. Non-economic factors including social and political conditions

also influenced the firm‟s stock price.

Stock price movements are determined by the issue of equity and debt. Firms in the

manufacturing sector of the LQ45 Index take the advantages of debt compared to equity. Issuance

of debt has a tax benefit because of the debt tax shield. A company with a higher tax rate thus has

a higher tax benefit from debt issuance. Some assert that debt adds discipline to management

because interest expenses cause lower cash flows, which makes management more likely to be

efficient. Hence, debt issue has positive effect on stock price.

Meanwhile, firms in the manufacturing sector of the LQ45 Index face the problems

because of increasing using leverage, even though the firms have high asset tangibility to secure

their debt. The firms have a risk of bankruptcy. Additionally, unlike equity, debt must at some

point be repaid. High interest costs during difficult financial periods can increase the risk of

insolvency, and companies that are too highly leveraged (that have large amounts of debt as

compared to equity) often find it difficult to grow because of the high cost of servicing the debt.

Therefore, our result shows that the positive influence of debt issue on stock price is not

significant.

6.4. Research Question 4, Hypothesis, Hypothesis Testing, and Result Analysis

In this study, the research question, hypothesis, hypothesis testing, and result analysis four are

explained in the follow sub-sections:


Our research question 4 is, in the context of firm‟s life cycle, can we expect that growth [and

small] firms follow the pecking order theory more closely than mature [and large] firms?

6.4.2. Hypothesis 4

As our hypothesis 4 state that, in the context of a firm‟s life cycle, we expect that growth [and

small] firms follow the pecking order theory more closely than mature [and large] firms, hence, we

test this hypothesis applying multiple regression and augmented analysis as in hypothesis 2, but in

the context of firm‟s life cycle.

6.4.3. Testing Hypothesis 4

As described in chapter 5, multiple regression analysis and augmented analysis were

selected to test hypothesis 4. For testing hypothesis 4, the independent variable was financing

deficit and net debt issue and net equity issue, were the dependent variables.

The objective of regression analysis is to examine which firm is more following the

pecking order theory, growth firms or mature firms. If firms followed the pecking order theory, the

138

deficit is financed with internal financing, for external financing, the financing deficit is financed

with debt first then equity. The firms adopted the pecking order have the changes in debt with

track changes in the deficit one-for-one. Hence, the expected coefficient on the deficit is 1.

The objective of augmented analysis is to examine how growth and mature firms finance

the deficit, with debt first or equity first. If firms followed the pecking order, changes in debt

should track changes in the deficit one-for-one (Shyam-Sunder and Myers, 1999). If firms

financed their deficit with debt first and issued equity only when they reached their debt capacities,

then net debt issued was a concave function of the deficit (Chirinko and Singha, 2000) and the

coefficient on the squared deficit term would be negative. If firms issued equity first and debt was

the residual source of financing, then this relationship should be convex and the coefficient on the

squared deficit term would be positive.

6.4.4 Sample Description

Table 6.14 is divided into the following tables 6.14a, 6.14b, 6.14c, 6.14d, and 6.14e,

which are our samples of mature/growth, large/small, and young/old firms. From the tables, we

can see that all mature firms are large firms except the KAEF and RMBA, all mature firms are old

firms except KAEF, all small firms are growth firms except KAEF and RMBA, all firms are old

firms except INAF and KAEF.

Table 6.14a. Firm Classifications: Growth Firms

Growth Firms

ADMG BRPT BUDI CPIN

INKP INAF INTP KOMI

DNKS FASW GJTL INDR

SMCB TKIM TSPC SULI

Table 6.14b. Firm Classifications: Mature Firms

Mature

Firms

ASII AUTO GGRM HMSP INDF

KAEF KLBF RMBA SMGR UNVR

Table 6.14c. Firm Classifications: Small Firms

Small

Firms

BUDI DNKS INAF KOMI KAEF RMBA TSPC

Table 6.14d. Firm Classifications: Large Firms

Large

Firms

ASII AUTO ADMG BRPT CPIN FASW GGRM

GJTL HMSP INDF INDR INKP INTP SULI

KLBF SMCB SMGR TKIM UNVR

139

Table 6.14e. Firm Classifications: Old and Young Firms

Old Firms ASII AUTO ADMG BRPT CPIN FASW GGRM GJTL

BUDI DNKS INDR INKP INTP KLBF KOMI RMBA

TKIM TSPC UNVR SULI SMCB SMGR HMSP INDF

Young

Firms

INAF KAEF

Bulan, Subramanian, and Tanlu (2007) found that firms that initiated dividends were

mature firms. Thus, Bulan and Yan (2007) identified firms in their mature stage by their dividend

history. By following Bulan and Yan (2007) to construct the growth and mature sample firms, to

deem the 6-year dividends payment periods as the mature stage of a firm‟s life cycle, we found 10

firms which have one 6-year dividend payment period; while 16 firms have less than one 6-year

dividend payment periods.

Meanwhile, 8 of our 10 mature firms are large firms, except KAEF, RMBA. KAEF went

public on July 4, 2001. Its amount of total assets made KAEF was categorised as a small firm

(Hufft, JR category). It was established on January 23, 1969 and the firm was a dividend payer.

Based on those facts, KAEF is a small young mature firm that is liquid enough to pay dividend to

the shareholder. RMBA is a small mature old firm that pays dividend for 6 years consecutively

(Bulan and Yan, 2007).

We have 7 small firms and 19 large firms based on the Hufft category with total assets of

less than $150 million, or equal to IDR 1,081,028.68 - 1,086,876.61 million. Our sample of firms

which were categorised into young firms, based on Evans (1987) who defined firms of six years

old or younger as young firms and firms of seven years or older as old firms, were INAF and

KAEF. INAF was established on January 2, 1996 and went public on April 17, 2001. INAF is also

a small-growth firm. KAEF was established on January 23, 1969 and went public on July 4, 2001.

INAF is a small-mature firm.


The following sub-sections are analysis of results for growth and mature firms and its consistency

with the theory and previous research, and also with the Indonesian capital market condition.

6.4.5.1 Analysis of Results and Its Consistency to the Theory and Previous Research

(Growth and Mature Firms)

As shown by table 6.15-6.16, the regression result for mature and growth firms are as follow: The

financing deficit is financed with debt and/or equity. If firms follow the pecking order, changes in

debt should track changes in the deficit one-for-one. Therefore, the expected coefficient on the

deficit is 1.

Table 6.15. Regression and Augmented Results for Mature Firms

Coefficientsa

Model Unstandardised

Coefficients

Standar

dised Co-

efficients

t Sig. Collinearity

Statistics

B Std.

Error

Beta Tolera

nce

VIF

140

NDebt_

M

(Cons

tant)

.026 .021 1.201 .233

FD_M .151 .038 .383 3.932 .000 1.000 1.000

F=15.463 (0.000) ; R-squared=0.147 ; N=92

NEquity

_M

(Cons

tant)

-.005 .013 -.414 .680

FD_M .058 .023 .254 2.489 .015 1.000 1.000

F=6.196 (0.015) ; R-squared=0.064 ; N=92

NRE_M (Cons

tant)

.074 .014 5.368 .000

FD_M -.042 .025 -.177 -1.709 .091 1.000 1.000

F=2.921 (0.091) ; R-squared=0.031 ; N=92

NDebt_

M

(Cons

tant)

-.025 .025 -1.012 .314

FD_M .404 .082 1.025 4.895 .000 .193 5.174

FDSQR_

M

-.129 .038 -.715 -3.415 .001 .193 5.174



Table 6.16. Regression and Augmented Results for Growth Firms

Coefficients


Coefficients

Standar

Dised Co-

efficients

t Sig. Collinearity

Statistics

B Std.

Error

Beta Tolera

nce

VIF

NDebt

_G

(Constant) -.106 .041 -2.617 .010

FD_G .284 .060 .385 4.749 .000 1.000 1.000

F=22.556 (0.000) ; R-squared=0.148 ; N=132

NEquit

y_G

(Constant) .021 .024 .895 .373

FD_G .073 .035 .177 2.054 .042 1.000 1.000

F=4.219 (0.042) ; R-squared=0.031 ; N=132

NRE_

G

(Constant) .057 .018 3.135 .002

FD_G -.091 .027 -.285 -3.391 .001 1.000 1.000

F=11.501 (0.001) ; R-squared=0.081 ; N=132

NDebt

_G

(Constant) -.146 .038 -3.830 .000

FD_G .666 .095 .901 7.042 .000 .339 2.952

FDSQR_G -.365 .074 -.635 -4.965 .000 .339 2.952



A. Growth Firms

Our regression model results of financing deficit on net debt issue, net equity issue, and

newly retained earning for growth firms are as follow:

141

Regression Model Result

In the regression model, Y is net debt issued and deficit is the financing deficit. This

deficit is financed with debt and/or equity. If firms follow the pecking order, changes in debt

should track changes in the deficit one-for-one. Therefore, the expected coefficient on the deficit

is 1.

Net Debt Issue


on net debt issue with t-value of 4.749 (it higher than mature firms) and significance value of

0.000. This result suggests that high deficit firms would tend to issue more net debt. However, the

coefficient on the deficit is 0.284 and constant value is -0.106.

Net Equity Issue

The financing deficit has positive but not significant effects on net equity issue with t-

value of 2.054 (it lower than mature firms) and significance value of 0.042. This result suggests

that high deficit firms would tend to issue more net equity. However, the coefficient on the deficit

is 0.073 and the constant value is 0.021.


The financing deficit has negative significant effects on newly retained earning with t-

value of -3.391 (it more negative than mature firms) and significance value of 0.001. This result

suggests that high deficit firms would not tend to use newly retained earning. However, the

coefficient on the deficit is -0.091 and constant value is 0.057.

Augmented Model Result

If firms are issuing equity first and debt is the residual source of financing, then this

relationship should be convex and the coefficient on the squared deficit term would be positive.

However, our result shows a negative coefficient on the squared deficit term, it implies that firms

are limited by their debt capacity constraints and they have to resort to issuing equity. A squared

deficit coefficient that is large in absolute value implies a greater reliance on equity finance for

larger values of the financing deficit.

From these results, we can conclude that our sample of growth firm in the manufacturing

sector of the LQ45 Index prefers external to internal financing and debt to equity if external

financing is used. This is consistent with the theory‟s prediction that firms with the greatest

information asymmetry problems (specifically young, growth firms) are precisely those that

should be making financing choices according to the pecking order. Growth firms in the

manufacturing sector of the LQ45 Index should face more asymmetric information in capital

markets and be less watched by the analysts.

B. Mature Firms


newly retained earning for mature firms are as follow.

Regression Model Result

142

As for growth firms, the regression model of mature firms, Y is net debt issued and

deficit is the financing deficit. This deficit is financed with debt and/or equity. If firms follow the

pecking order, changes in debt should track changes in the deficit one-for-one. Therefore, the

expected coefficient on the deficit is 1.

Net Debt Issue



high deficit firms would tend to issue more net debt. However, the coefficient on the deficit is


Net Equity Issue


2.489 and significance value of 0.015. This result suggests that high deficit firms would tend to

issue more net equity. However, the coefficient on the deficit is 0.058 and the constant value is -

0.005.




not tend to use newly retained earning. However, the coefficient on the deficit is -0.042 and

constant value is 0.074.

Augmented Model Result

If firms are issuing equity first and debt is the residual source of financing, then this

relationship should be convex and the coefficient on the squared deficit term would be positive.

However, our result shows a negative coefficient on the squared deficit term, it implies that firms

are limited by their debt capacity constraints and they have to resort to issuing equity. A squared

deficit coefficient that is large in absolute value implies a greater reliance on equity finance for

larger values of the financing deficit.

Prefer External or Internal Financing?

The coefficient of financing deficit on newly retained earning is negative for growth and

mature firms. The coefficient of financing deficit on net debt and net equity issue are positive

significant for growth and mature firms. For both firms, the significance value of net debt issue is

more significant than net equity issue. The evidence seems to suggest mature and growth firms

rely more heavily on external financing.

Prefer Debt or Equity?

Growth firms have the same 0.000 significantly value with mature firms while growth

firms have higher standardised coefficients (0.385) of deficit on net debt issue than mature firms

(0.383), however mature firms have higher standardised coefficients (0.254) of deficit on net

equity issue than of growth firms (0.177). These results imply that deficit of mature firms is solved

more by net equity issue while deficit of growth firms is solved more by net debt issue.

143

From augmented model result, the findings are consistent with the firms following the

pecking order: the coefficient on the deficit is positive and the coefficient on the deficit-square is

negative. Both growth and mature firms are issuing debt first, while equity is the residual source of

financing once they reach their debt capacities. Our evidence seems to suggest mature and growth

firms rely more heavily on debt financing rather than equity financing.

For growth firms, Adjusted R Square (0.273) and R Square (0.285) are stronger than

mature firms (0.229) and (0.245). R-squared of financing deficit on net debt issue of growth firms

are higher than mature firms, while R-squared of financing deficit on net equity issue of mature

firms are higher than of growth firms. Therefore, overall, we find that the pecking order theory

describes the financing patterns of growth firms better than mature firms.

Adjusted R-squared of predictors of financing deficit and the financing deficit square on

net debt issue of growth firms (0.273) are higher than mature firms (0.229). It implies that

financing deficit and financing deficit square on net debt issue of growth firms rely more on net

debt issue. Therefore, the pecking order theory describes the financing patterns of growth firms

better than mature firms, as mature firms are more closely followed by analysts and are better

known to investors, and hence, should suffer less from problems of information asymmetry.

The results are consistent with firms following the pecking order: the coefficient on the

deficit is positive and the coefficient on the deficit square is negative. Both growth and mature

firms are issuing debt first, while equity is the residual source of financing once they reach their

debt capacities. Comparing across life cycle stages however, we found that growth firms have

significantly higher debt-deficit sensitivities indicating that growth firms follow the pecking order

more closely. This is consistent to conventional wisdom since they would expect growth firms to

suffer more from information asymmetry problems. This result is not in line with the finding

research of Bulan and Yan (2009).

Older and more mature firms are more closely followed by analysts and are better known

to investors, and hence, should suffer less from problems of information asymmetry. For example,

a good reputation (such as a long credit history) mitigates the adverse selection problem between

borrowers and lenders. Thus, mature firms are able to obtain better loan rates compared to their

younger firm counterparts (Diamond, 1989). Furthermore, mature firms generally have more

internal funds due to higher profitability and lower growth opportunities. Older, more stable and

highly profitable firms with few growth opportunities and good credit histories are more suited to

use internal funds first, and then debt before equity for their financing needs.

As explained by the pecking order theory, firms with the greatest information asymmetry

problems (specifically young growth firms) are precisely those that should be making financing

choices based on the pecking order. Thus, they are more suited to use internal funds first, and then

debt before equity for their financing needs.

From descriptive statistics and correlation matrix, we conclude that: growth firms have

lower newly retained earning and lower profitability. It is indicated by profitability and newly

retained earning which have positive significant correlation (0.654; 0.000). It suggests that the

larger the firm‟s profitability, the higher the firm‟s newly retained earning.

Small-growth firms issue more net debt to solve financing deficit than equity as they have

higher asset tangibility to secure net debt issue. It was shown by tangibility and financing deficit

have positive significant correlation (0.551; 0.000) which implies that firm that has higher

144

financing deficit has larger asset tangibility to secure debt issue. Tang and newly retained earning

are negative significant correlated (-0.227; 0.001), it implies that the lower the firm‟s newly

retained earning the larger the firm‟s tangibility.

However, growth-small firms have higher profitability than mature-large firms. It shown

by profitability and asset tangibility which have negative significant correlation and size and asset

tangibility have positive significant correlation, profitability and risk have negative significant

correlation. Hence, small-growth firms have low risk (earning volatility).

Even though growth firms have higher financing deficit, financing deficit square, net

equity issued, but they finance their financing deficit with more net debt issue than net equity

issue, while mature firms have higher net debt issued, but they manage their financing deficit with

more net equity issue than net debt issue.

For growth firms, long-term leverage and capital expenditure have higher composition in

forming financing deficit, while for mature firms dividend and working capital have higher

composition in forming financing deficit as mature firms have higher newly retained earning.

Mature firms have higher dividend, working capital, cashflow, newly retained earning,

net debt issued, while growth firms have higher long-term leverage, fixed asset, financing deficit,

financing deficit1square, net equity issued (descriptive statistics). Growth firms have lower

profitability, higher tangibility, higher risk, while mature firms have higher profitability, lower

tangibility, lower risk (correlation matrix).

It can be shown that there exists positive significant correlation (0.654; 0,000) between

profitability and newly retained earning. The larger the firm‟s profitability, the higher the firm‟s

newly retained earning.

Profitability and financing deficit are significantly negative (-0.461; 0.000). It suggests

that the larger the firm‟s profitability, the smaller the firm‟s financing deficit. Tangibilty and

financing deficit are significantly positive (0.551; 0.000). It implies that the larger the firm‟s

tangibility, the higher the firm‟s financing deficit. Tangibility and newly retained earning are

significantly negative (-0.227; 0.001). It implies that the larger the firm‟s tangibility, the lower the

firm‟s financing deficit. Risk and newly retained earning are significantly negative (-0.444; 0.000).

It implies that the larger the firm‟s risk, the lower the firm‟s newly retained earning.

According to Myers (1984), a firm is said to follow a pecking order if it prefers internal to

the external financing and debt to equity if external financing is used. Therefore, overall, we found

that the pecking order theory described the financing patterns of growth firms better than mature

firms, as mature firms were more closely followed by analysts and were better known to investors,

and hence, should suffer less from problems of information asymmetry. Our result is consistent

from the theory, and also consistent from the previous research findings of Shyam-Sunder and

Myers (1999). They proposed a direct test of the pecking order and found strong support for the

theory among a sample of large firms.

However, some empirical evidence for the pecking order theory over firms life cycles

which are inconsistent with our results are as follow: The plausible explanation is that the

Indonesian economy and market conditions differ from those under which the previous research

was developed, such as the USA.

More recent work by Lemmon and Zender (2004) and Agca and Mozumdar (2004) have

shown that the Shyam-Sunder and Myers test did not account for a firm‟s debt capacity; a

145

constraint that was particularly binding for small firms. Thus, it was not surprising that this test

failed to find support for the pecking order among small firms. To address this shortcoming,

Lemmon and Zender and Agca and Mozumdar used sub-samples of firms that were the least debt-

constrained and they found support for the pecking order. In addition, once debt capacity

constraints were accounted for, they found that the pecking order performed well even for small

firms.

Frank and Goyal (2003), found that large firms fitted the pecking order theory better than

of small firms, contrary to the predictions of the theory. Frank and Goyal (2003) examined the

broad applicability of the pecking order theory. Their evidence was based on a large cross-section

of US publicly traded firms over long time periods. It showed that external financing was heavily

used by some firms. On average net equity issued track the financing deficit more closely than did

net debt issues. These facts did not match the claims of the pecking order theory. The greatest

support for pecking order was found among large firms, which might be expected to face the least

severe adverse selection problem since they received much better coverage by equity analysts.

Even here, the support for pecking order was declining over time and the support for pecking order

among large firms was weaker in the 1990s. They concluded that the pecking order theory did not

explain broad patterns in the data.

Overall, Bulan and Yan (2007) found that the pecking order theory described the

financing patterns of mature firms better than of growth firms. This is contrary to the theory‟s

prediction that firms with the greatest information asymmetry problems (specifically young,

growth firms) are precisely those that should be making financing choices according to the

pecking order. These results are robust under alternative empirical models for testing the pecking

order theory.

Bulan and Yan (2007) further saw that growth firms had larger financing deficits, as

expected. The financing deficit is defined as the uses of funds minus internal sources of funds,

which, by an accounting identity, is also the sum of net debt issued and net equity issued. There

seems to be no difference in net debt issued between the two cohorts, while net equity issued is

larger for the growth firms. From this simple comparison, the evidence seems to suggest growth

firms rely more heavily on equity financing rather than debt. This finding is consistent with Agca

and Mozumdar (2004) and Lemmon and Zender (2004).





adverse selection costs followed the pecking order more closely.

Financing deficit of growth firms are higher than financing deficit of mature firms, as

growth firms have lower cashflow than mature firms. Additionally, the findings that growth firms

had greater financing deficits but smaller debt capacities are implying that growth firms would

reach their debt capacities more often than mature firms.

The results are consistent with firms following the pecking order: the coefficient on the

deficit is positive and the coefficient on the deficit square is negative. Both growth and mature

firms issued debt first, while equity is the residual source of financing once they reach their debt

capacities. Comparing across life cycle stages however, they found that mature firms had

significantly higher debt-deficit sensitivities indicating that mature firms followed the pecking

order more closely. That was contrary to conventional wisdom, since they would expect growth

firms to suffer more from information asymmetry problems. Bulan and Yan (2009) documented

146

this result as a maturity effect in firm financing choice. Mature firms are older, more stable, and

highly profitable with few growth opportunities and good credit histories. Hence, they are able to

borrow more easily and at a lower cost. Therefore, by the very nature of their life cycle stage,

mature firms are pre-disposed to utilising debt financing first before equity.

Halov and Heider‟s (2003) main hypothesis was that firms issued more equity and less

debt in situations where risk was an important element of the adverse selection problem of outside

financing. They found robust empirical support for the hypothesis and documented a strong link

between asset risk and the decision to issue debt and equity in a large unbalanced panel of publicly

traded US firms from 1971 to 2001.

Frank and Goyal expected the pecking order to work best for young, small firms since

they argued that these firms should have the most severe asymmetric information problem. Halov

and Heider (2003) explained that the standard pecking order should not work at all for those firms.

Risk differences, i.e. differences in failure rates and upside potential, play an important role in the

adverse selection problem for young, small firms. Hence, they should issue equity and not debt, or

alternatively, rational investors demand equity and not debt from these firms. Therefore, our result

is what we expected.

6.4.5.2 Analysis of Results and Its Consistency to the Theory and Previous Research (Small

and Large Firms)

As shown by table 6.17, the regression result for large and small firms is as follows: The financing

deficit is financed with debt and/or equity. If firms follow the pecking order, changes in debt

should track changes in the deficit one-for-one. Therefore, the expected coefficient on the deficit is

1.

Table 6.17. Regression Results for Large and Small Firms

Coefficientsa


Coefficients

Standardised

Coefficients

T Sig. Collinearity

Statistics

B Std.

Error

Beta Toleran

ce

VIF

1 (Constant) .000 .055 -.011 .991

FD_L .091 .099 .217 .915 .373 1.000 1.00

0

a. Dependent Variable: NDEBT_L

F-value=0.837 (0.373) ; R-Squared=0.047 ; N=19

1 (Constant) -.011 .021 -.508 .618

FD_L .126 .038 .627 3.315 .004 1.000 1.00

0

a. Dependent Variable: NEQUITY_L


1 (Constant) .078 .017 4.659 .000

FD_L -.115 .030 -.681 -3.836 .001 1.000 1.00

0

a. Dependent Variable: NRE_L


1 (Constant) -.003 .045 -.056 .957

FD_S .245 .080 .806 3.049 .028 1.000 1.00

0

147

a. Dependent Variable: NDEBT_S


1 (Constant) .063 .073 .871 .424

FD_S .140 .131 .432 1.072 .333 1.000 1.00

0

a. Dependent Variable: NEQUITY_S


1 (Constant) .107 .033 3.199 .024

FD_S -.081 .060 -.517 -1.352 .234 1.000 1.00

0

a. Dependent Variable: NRE_S


A. Large Firms


newly retained earning for large firms are as follow:

Net Debt Issue

From the tables we can conclude that the financing deficit has positive insignificant

effects on net debt issue with t-value of 0.915 and significance value of 0.373. This result suggests

that large firm with high financing deficit would tend to issue more net debt. However, the

coefficient on the deficit is 0.091 and constant value is 0.000.

Net Equity Issue


3.315 and significance value of 0.004. This result suggests that large firm with high deficit

financing would tend to issue more net equity. However, the coefficient on the deficit is 0.126 and

constant value is -0.011.



value of -3.836 and significance value of 0.001. This result suggests that large firm with high

deficit financing would not tend to use newly retained earnings to finance the deficit. However, the

coefficient on the deficit is -0.115 and constant value is 0.078.

B. Small Firms


newly retained earning for small firms are as follow:

Net Debt Issue



small firm with high financing deficit would tend to issue more net debt. However, the coefficient

on the deficit is 0.245 and constant value is -0.003.

148

Net Equity Issue

The financing deficit has positive insignificant effects on net equity issue with t-value of

1.072 and significance value of 0.333. This result suggests that small firm with high financing

deficit would tend to issue more net equity. However, the coefficient on the deficit is 0.140 and

constant value is 0.063.


The financing deficit has negative insignificant effects on newly retained earning with t-

value of -1.352 and significance value of 0.234. This result suggests that small firms with high

deficit of financing would not tend to use newly retained earnings to finance the deficit. However,

the coefficient on the deficit is -0.081 and constant value is 0.107.

Our result implies that the deficit of large firms is solved more by net equity issue, while

the deficit of small firms is solved more by the net debt issue. It is consistent with the pecking

order theory which predicts an inverse relation between leverage and firm size. The argument is

that large firms have been around longer and are better known. Thus, large firms face lower

adverse selection and can more easily issue equity compared to small firms where adverse

selection problems are severe. Large firms also have more assets and thus the adverse selection

may be more important if it impinges on a larger base. Rajan and Zingales (1995) argued that there

was less asymmetrical information about the larger firms. This reduced the chances of

undervaluation of the new equity issue and thus encouraged the large firms to use equity financing.


From the results, we imply that our growth and mature firms in the manufacturing sector

of the LQ45 Index prefers external to internal financing and debt to equity if external financing is

used. Therefore, both kinds of firms are following the pecking order theory. Specifically, the

results imply that deficit of mature firms is solved more by net equity issue while deficit of growth

firms is solved more by net debt issue.

Following the pecking order theory, growth firms should face more asymmetric

information in capital markets. However, in the Indonesian capital market namely IDX,

information asymmetry both for growth and mature firms has rarely happened as the government

of Indonesia has stipulated the regulations regarding information asymmetry. The efforts of the

Government are as follow: (a) Develop protection scheme of investor. Investor confidence in

capital markets is in absolute terms that must be constantly guarded by the regulator. Investors will

utilise the capital markets industry as a means of investment and risk management if they feel

confident that their interests are protected. (b) Improving the quality of financial information

transparency in capital market industry. In the Indonesian capital markets industry, the

transparency of financial information is one form of implementation of the disclosure of

information. Investment decision made by investors will be strongly influenced by the information

obtained from financial statements.

6.4.6. Capital Structure over Firm’s Life Cycle

The following graphics 6.7-6.16 are to describe which firms‟ life cycles, namely mature/growth

firms, small/large firms, and old/young firms in the manufacturing sector raise relatively more (or

less) capital externally (or internally) than other firms‟ life cycles in the manufacturing sector.

149

Figure 6.7. Mature Firms Figure 6.8. Growth Firms

Figure 6.9. Large Firms Figure 6.10. Small Firms

0

0,1

0,2

0,3

0,4

0,5

FD1 NDEBT NEQUITY NRE

Mature Firms

0

0,1

0,2

0,3

0,4

0,5


Growth Firms

0

0,1

0,2

0,3

0,4

0,5


Large Firms

0

0,2

0,4

0,6


Small Firms

150

Figure 6.11. Young Firms Figure 6.12. Old Firms

Figure 6.13. Mature-Growth Firms Figure 6.14. Large-Small Firms

00,05

0,10,15

0,20,25

0,30,35

Young Firms

0

0,1

0,2

0,3

0,4

0,5

0,6


Old Firms

0

0,1

0,2

0,3

0,4

0,5

Mature

Growth

00,05

0,10,15

0,20,25

0,30,35

0,40,45

0,5

Large Firms

small firms

151

Figure 6.15. Young-Old Firms Figure 6.16. All Classification of Firms

Graphics show that large and small firms in the manufacturing sector raise relatively

more capital externally than internally, and they raise more equity than debt. Young firms in the

manufacturing sector raise relatively more NRE than equity and debt, however, they raise more

equity than debt. Old firms in the manufacturing sector raise relatively more capital externally than

internally, and they raise more debt than equity.

Mature and growth firms in the manufacturing sector raise relatively more capital

externally than internally, and they raise more debt than equity. However, from this result, we

have not concluded yet whether mature or growth firms that more rely on debt and equity.

Therefore, we test it through hypothesis 4 which gives a more specific result.

Young firms in the manufacturing sector raise a higher capital internally than the other

types of firms and are followed by small firms. Large firms in the manufacturing sector raise a

lower capital internally than the other types of firms and are followed by old firms. Small firms in

the manufacturing sector raise a higher net debt than the other types of firms and are followed by

old and mature firms.

Young firms in the manufacturing sector raise a lower net debt than the other types of

firms and are followed by large firms. Small firms in the manufacturing sector raise a higher net

equity than the other types of firms and are followed by young firms. Mature firms in the

manufacturing sector raise a lower net equity than the other types of firms and are followed by

large firms. Old firms in the manufacturing sector have a higher financing deficit in all other types

of firms and are followed by large firms. Young firms in the manufacturing sector have a lowest

financing deficit in all other types of firms and are followed by mature firms.

6.4.7 Frequency

Frequency tables consist of mean, median, mode, deviation, variance, skewness, standard error of

skewness, kurtosis, standard error of kurtosis, range, maximum, minimum, sum, percentiles 25,

50, and 75. These values describe the tendency of variables. The meaning of each value is as

follow.

0

0,1

0,2

0,3

0,4

0,5

0,6

Old FirmsYoung …

0

0,2

0,4

0,6

Old

Fir

ms

You

ng

Firm

s

Larg

e Fi

rms

Smal

l fir

ms

Mat

ure

Fir

ms

Gro

wth

Fir

ms

FD1

NDEBT

NEQUITY

NRE

152

The mode of a set of data values is the value (s) that occurs most often. The median of a

set of data values is the middle value of the data set when it has been arranged in ascending order.

That is, from the smallest value to the highest value. The mean (or average) of a set of data values

is the sum of all of the data values divided by the number of data values. Standard deviation is a

widely used measurement of variability or diversity used in statistics. It shows how much variation

or "dispersion" there is from the average (mean, or expected value). A low standard deviation

indicates that the data points tend to be very close to the mean, whereas high standard deviation

indicates that the data are spread out over a large range of values. In descriptive statistics, the

range is the length of the smallest interval which contains all the data. It is calculated by

subtracting the smallest observation (sample minimum) from the greatest (sample maximum) and

provides an indication of statistical dispersion. The variance is used as a measure of how far a set

of numbers are spread out from each other. It is one of several descriptors of a probability

distribution, describing how far the numbers lie from the mean (expected value). Skewness is a

measure of the asymmetry of the probability distribution of a real-valued random variable. The

skewness value can be positive or negative, or even undefined. Qualitatively, a negative skew

indicates that the tail on the left side of the probability density function is longer than the right side

and the bulk of the values (possibly including the median) lie to the right of the mean. A positive

skew indicates that the tail on the right side is longer than the left side and the bulk of the values

lie to the left of the mean. A zero value indicates that the values are relatively evenly distributed

on both sides of the mean, typically but not necessarily implying a symmetric distribution.

Kurtosis is a measure of the "peakedness" of the probability distribution of a real-valued random

variable, although some sources are insistent that heavy tails, and not peakedness, is what is really

being measured by kurtosis. Higher kurtosis means more of the variance is the result of infrequent

extreme deviations, as opposed to frequent modestly sized deviations. Sum is the amount of the

values. Minimum is the minimum value. Maximum is the maximum value.

Table 6.18, 6.19, 6.20, 6.21, 6.22, and 6.23 show the frequency of mature-growth, large-

small, and old-young firms which analyse the mean, median, mode, deviation, variance, skewness,

standard error of skewness, kurtosis, standard error of kurtosis, range, maximum, minimum, sum,

and percentiles 25, 50, and 75.

Table 6.18. Frequency of Mature Firms

FD_M NRE_M NEQUITY_M NDEBT_M

N Valid 75 71 71 71

Missing 43 47 47 47

Mean .362999 .063057 .012640 .086801

Median .227218 .058904 .000243 .043679

Mode -.1737a -.1641

a .0000 -.1788

a

Std. Deviation .4579992 .1047534 .0989413 .1716948

Variance .210 .011 .010 .029

Skewness 2.993 1.509 -1.819 1.023

Std. Error of

Skewness

.277 .285 .285 .285

Kurtosis 11.483 7.753 19.773 1.292

Std. Error of

Kurtosis

.548 .563 .563 .563

Range 2.8143 .7445 .9578 .8371

Minimum -.1737 -.1641 -.5690 -.1788

Maximum 2.6406 .5805 .3888 .6583

http://www.mathsteacher.com.au/year8/ch01_arithmetic/01_basic/oper.htm#add

http://en.wikipedia.org/wiki/Statistics

http://en.wikipedia.org/wiki/Statistical_dispersion

http://en.wikipedia.org/wiki/Mean


http://en.wikipedia.org/wiki/Descriptive_statistics

http://en.wikipedia.org/wiki/Sample_minimum

http://en.wikipedia.org/wiki/Sample_maximum

http://en.wikipedia.org/wiki/Statistical_dispersion

http://en.wikipedia.org/wiki/Probability_distribution




http://en.wikipedia.org/wiki/Real_number

http://en.wikipedia.org/wiki/Random_variable


http://en.wikipedia.org/wiki/Real_number



http://en.wikipedia.org/wiki/Variance

http://en.wikipedia.org/wiki/Deviation_%28statistics%29

153

Sum 27.2250 4.4770 .8974 6.1628

Percentiles 25 .161062 .017796 .000000 -.025757

50 .227218 .058904 .000243 .043679

75 .447046 .112630 .011334 .154644

Table 6.19. Frequency of Growth Firms

FD_G NRE_G NEQUITY_G NDEBT_G

N Valid 156 153 153 153

Missing 106 109 109 109

Mean .529736 .012489 .056144 .049616

Median .563386 .021865 .000646 .050031

Mode -.7599a -1.0191

a .0000 -1.5006

a

Std. Deviation .3954524 .1254664 .1589349 .2841207

Variance .156 .016 .025 .081

Skewness -.076 -3.635 2.804 -1.913

Std. Error of

Skewness

.194 .196 .196 .196

Kurtosis 1.400 29.604 18.855 8.207

Std. Error of

Kurtosis

.386 .390 .390 .390

Range 2.6032 1.3324 1.7730 2.2003

Minimum -.7599 -1.0191 -.6053 -1.5006

Maximum 1.8433 .3134 1.1677 .6997

Sum 82.6387 1.9109 8.5901 7.5912

Percentiles 25 .293100 -.028236 .000000 -.058374

50 .563386 .021865 .000646 .050031

75 .771394 .068037 .075072 .210872

For financing deficit variable of mature and growth firms, mean, median, sum, percentiles

25, 50, and 75 of mature firms are lower than of growth firms. For financing deficit variable of

mature and growth firms, mode, standard deviation, variance, skewness, standard error of

skewness, kurtosis, standard error of kurtosis, range, minimum, and, maximum of mature firms are

higher than of growth firms.

For net debt issue variable of mature and growth firms, mode, skewness, standard error of

skewness, standard error of kurtosis, minimum, percentiles 25 of mature firms are higher growth

firms. For net debt issue variable of mature and growth firms, mean, median, standard deviation,

variance, kurtosis, range, maximum, sum, percentiles 50, and 75 of mature firms are lower than of

growth firms.

For net equity issue variable of mature and growth firms, mode and percentiles 25 of

mature firms are exactly the same with growth firms. For net equity issue variable of mature and

growth firms, standard error of skewness, kurtosis, standard error of kurtosis, and minimum of

mature firms are higher than of growth firms. For net equity issue variable of mature and growth

firms, mean, median, standard deviation, variance, skewness, range, maximum, sum, percentiles

50, and 75 of mature firms are lower than of growth firms.

154

For newly retained earning variable of mature and growth firms, standard deviation,

variance, kurtosis, and range of mature firms are lower than of growth firms. For newly retained

earning variable of mature and growth firms, mean, median, mode, skewness, standard error of

skewness, standard error of kurtosis, minimum, maximum, sum, percentiles 25, 50, and 75 of

mature firms are higher than of growth firms.

Table 6.20. Frequency of Large Firms

Statistics

FD_L NDEBT_L NEQUITY_

L

NRE_L

N Valid 19 19 19 19

Missing 0 0 0 0

Mean .4994 .0446 .0523 .0209

Std. Error of Mean .06015 .02513 .01214 .01016

Median .5149 .0563 .0403 .0221

Mode .07a -.26

a .00

a -.08

a

Std. Deviation .26217 .10953 .05290 .04430

Variance .069 .012 .003 .002

Skewness .035 -1.142 1.970 -.249

Std. Error of Skewness .524 .524 .524 .524

Kurtosis -1.020 2.580 4.459 -.029

Std. Error of Kurtosis 1.014 1.014 1.014 1.014

Range .88 .51 .21 .17

Minimum .07 -.26 .00 -.08

Maximum .95 .25 .22 .10

Sum 9.49 .85 .99 .40

Percentiles 25 .2463 .0098 .0147 -.0024

50 .5149 .0563 .0403 .0221

75 .7369 .1043 .0606 .0571

a. Multiple modes exist. The smallest value is shown

Table 6.21. Frequency of Small Firms

Statistics

FD_S NDEBT_S NEQUITY_

S

NRE_S

N Valid 7 7 7 7

Missing 0 0 0 0

Mean .4497 .1077 .1264 .0705

Median .2843 .1177 .1917 .0626

Mode .18a -.05

a -.03

a .02

a

Std. Deviation .35503 .10788 .11513 .05572

Variance .126 .012 .013 .003

Skewness 1.715 .074 -.324 1.343

Std. Error of Skewness .794 .794 .794 .794

Kurtosis 2.357 -.617 -1.952 1.979

Std. Error of Kurtosis 1.587 1.587 1.587 1.587

155

Range .97 .31 .30 .16

Minimum .18 -.05 -.03 .02

Maximum 1.16 .27 .27 .18

Sum 3.15 .75 .89 .49

Percentiles 25 .2332 .0180 .0224 .0265

50 .2843 .1177 .1917 .0626

75 .6982 .2097 .2015 .0951


For financing deficit variable of large and small firms, mean, median, sum, percentiles

25, 50, and 75 of large firms are higher than of small firms. For financing deficit variable of large

and small firms, mode, standard deviation, variance, skewness, standard error of skewness,

kurtosis, standard error of kurtosis, range, minimum, and maximum of large firms are lower than

of small firms.

For net debt issue variable of large and small firms, variance of large firms is exactly the

same with small firms, while standard deviation, kurtosis, range, and sum of large firms are higher

than of small firms. For net debt issue variable of large and small firms, mean, median, mode,

skewness, standard error of skewness, standard error of kurtosis, minimum, maximum, and

percentiles 25, 50, and 75 of large firms are lower than of small firms.

For net equity issue variable of large and small firms, mode, skewness, kurtosis,

minimum, and sum of large firms are higher than of small firms. For net equity issue variable of

large and small firms, mean, median, standard deviation, variance, standard error of skewness,

standard error of kurtosis, range, maximum, percentiles 25, 50, and 75 of large firms are lower

than of small firms.

For newly retained earning variable of large and small firms, range of large firms are

higher than of small firms. For newly retained earning variable of large and small firms, mean,

median, mode, standards deviation, variance, skewness, standard error of skewness, kurtosis,

standard error of kurtosis, minimum, maximum. Sum, percentiles 25, 50, and 75 of large firms are

lower than of small firms.

Table 6.22. Frequency of Old Firms

Statistics

FD_O NDEBT_O NEQUITY_O NRE_O

N Valid 24 24 24 24

Missing 0 0 0 0

Mean .5014 .0635 .0686 .0271

Median .5032 .0638 .0412 .0244

Mode .07a -.26

a .00

a -.08

a

Std. Deviation .28980 .11226 .06884 .04352

Variance .084 .013 .005 .002

Skewness .447 -.842 1.244 -.316

Std. Error of

Skewness

.472 .472 .472 .472

Kurtosis -.550 2.194 .189 .046

Std. Error of .918 .918 .918 .918

156

Kurtosis

Range 1.09 .52 .21 .17

Minimum .07 -.26 .00 -.08

Maximum 1.16 .27 .22 .10

Sum 12.03 1.52 1.65 .65

Percentiles 25 .2365 .0227 .0190 .0000

50 .5032 .0638 .0412 .0244

75 .7272 .1267 .0915 .0661


Table 6.23. Frequency of Young Firms

Statistics

FD_Y NDEBT_Y NEQUITY_Y NRE_Y

N Valid 2 2 2 2

Missing 0 0 0 0

Mean .3012 .0385 .1163 .1207

Median .3012 .0385 .1163 .1207

Mode .28a -.05

a -.03

a .06

a

Std. Deviation .02391 .12281 .21173 .08216

Variance .001 .015 .045 .007

Range .03 .17 .30 .12

Minimum .28 -.05 -.03 .06

Maximum .32 .13 .27 .18

Sum .60 .08 .23 .24

Percentiles 25 .2843 -.0484 -.0334 .0626

50 .3012 .0385 .1163 .1207

75 .3181 .1253 .2660 .1788


For financing deficit variable of old and young firms, mean, median, standard deviation,

variance, skewness, standard error of skewness, kurtosis, standard error of kurtosis, range,

maximum, sum, percentiles 50 and 75 of old firms are higher than of young firms, while for

financing deficit variable of old and young firms, mode, minimum, and percentiles 25 of old firms

are lower than of young firms,

For net debt issue variable of old and young firms, mean, median, skewness (-), standard

error of skewness, kurtosis, standard error of kurtosis, range, maximum, sum, percentiles 25, 50,

and 75 of old firms are higher than of young firms. For net debt issue variable of old and young

firms, mode, standard deviation, variance, and minimum of old firms are lower than of young

firms.

For net equity issue variable of old and young firms, mean, median, standard deviation,

variance, range, maximum, percentiles 50, and 75 of old firms are lower than of young firms. For

net equity issue variable of old and young firms,mode, skewness, standard error of skewness ,

kurtosis , standard error of kurtosis, minimum , sum, and percentiles 25 of old firms are higher

than of young firms.

157

For newly retained earning variable of old and young firms, mean, median, mode,

standard deviation, variance, minimum, maximum, percentiles 25,50, and 75 of old firms are

lower than of young firms. For newly retained earning variable of old and young firms, skewness,

standard error of skewness, kurtosis, standard error of kurtosis, range, and sum of old firms are

higher than of young firms.

6.5. Statistical Power Analysis of Hypotheses 1, 2, 3, and 4

To examine to what extent the theory is implied in our sample, we also analyse the

predictive power of the result. It consists of analysing the un-standardised beta coefficients, the

standardised beta coefficients, analysis of variance (ANOVA), coefficient of determination (R-

squared), and descriptive statistics.

A. The Un-standardised Beta Coefficients

B is the value for the regression equation for predicting the dependent variable from the

independent variable. These are called un-standardised coefficients because they are measured in

their natural units. As such, the coefficients cannot be compared with one another to determine

which one is more influential in the model, because they can be measured on different scales.

For hypothesis 1, (Constant) value of profitability, growth, tangibility, risk, and size on

short-term leverage as dependent variable is 0.138, B Coefficients of profitability, growth,

tangibility, risk, and size are -0.443, -0.230, 0.012, 1.218, and 0.092. (Constant) value of

profitability, growth, tangibility, risk, and size on long-term leverage as dependent variable is

0.141, B Coefficients of profitability, growth, tangibility, risk, and size are -0.296, 0.372, -0.004, -

0.712, and 0.138. (Constant) value of profitability, growth, tangibility, risk, and size on total

leverage as dependent variable is 0.207, B Coefficients of profitability, growth, tangibility, risk,

and size are -0.765, 0.104, 0.014, 0.506, and 0.229. (Constant) value of profitability, growth,

tangibility, risk, and size on market leverage as dependent variable is 1.283, B Coefficients of

profitability, growth, tangibility, risk, and size are -0.683, 0.106, -0.011, 0.142, and -0.375. From

these results, we can conclude that the highest value is market leverage while the lowest is long-

term leverage.

For hypothesis 2, each model for firms sample have only one predictor variable, then beta

is equivalent to the correlation coefficient between the predictor and the criterion variable. The

following is the result of (constant) value and beta coefficients of financing deficit on net debt

issued, net equity issued, and newly retained earning for all of sample of firms. (Constant) value of

financing deficit on net debt issued is 0.001, B coefficients of financing deficit is 0.281. It

indicates that if there is no financing deficit, then net debt issued is 0.001. If value of financing

deficit 1 is 1, then net debt issued is 0.282. (Constant) value of financing deficit on net equity

issued is -0.015, B coefficients of financing deficit is 0.169. It indicates that if there is no financing

deficit, then net equity issued is -0.015. If value of financing deficit is 1, then net equity issued is

0.154. (Constant) value of financing deficit on newly retained earning is 0.086, B coefficients of

financing deficit is -0.037. It indicates that if there is no financing deficit, then newly retained

earning is 0.086. If value of financing deficit is 1, then newly retained earning is 0.049. (Constant)

value of financing deficit on repurchase equity is -0.021, B coefficients of financing deficit is

0.000. It indicates that if there is no financing deficit, then repurchase equity is -0.021. If value of

financing deficit is 1, then repurchase equity is -0.021. From these results, we can conclude that

the highest value is net debt issue. It indicates that if firms face financing deficit, they tend to issue

more debt.

158

For hypothesis 3, (constant) value of net debt issued on the monthly and the yearly stock

price is positive. From the table in the appendix, we can conclude that the highest value is the

January stock price while the lowest value is the September stock price. (Constant) value of net

equity issued on monthly and yearly stock price is positive while net equity issued is negative.

(Constant) value of repurchase equity on monthly and yearly stock price is positive but not

significant.

For hypothesis 4, the result of (constant) value and beta coefficients of financing deficit

on net debt issued, net equity issued, and newly retained earning for growth and mature firms is as

follows. For growth firms, (constant) value of financing deficit on net debt issue is -0.106, beta

coefficients of financing deficit are 0.284. It indicates that if there is no financing deficit, then net

debt issue is -0.106, if value of financing deficit is 1, then net debt issue is 0.178. (Constant) value

of financing deficit on net equity issue is 0.021, beta coefficients of financing deficit is 0.073, it

indicates that if there is no financing deficit, then net equity issue is 0.021, if value of financing

deficit is 1, then net equity issue is 0.094. (Constant) value of financing deficit on newly retained

earning is 0.057, beta coefficients of financing deficit are -0.091. It indicates that if there is no

financing deficit, then newly retained earning is 0.057, if value of financing deficit is 1, then newly

retained earning is -0.034.

For mature firms, (constant) value of financing deficit on net debt issue is 0.026, beta

coefficients of financing deficit is 0.151. It indicates that if there is no financing deficit, then net

debt issue is 0.026. If value of financing deficit is 1, then net debt issue is 0.177. (Constant) value

of financing deficit on net equity issue is -0.005, B coefficients of financing deficit1 is 0.058. It

indicates that if there is no financing deficit, then net equity issue is -0.005. If value of financing

deficit is 1, then net equity issue is 0.053. (Constant) value of financing deficit on newly retained

earning is 0.074, beta coefficients of financing deficit are -0.042. It indicates that if there is no

financing deficit, then newly retained earning is 0.074. If value of financing deficit is 1, then

newly retained earning is 0.032.

B. The Standardised Beta Coefficients

Beta(s) are the standardised coefficients. These are the coefficients that we would obtain

if we standardised all of the variables in the regression, including the dependent and all of the

independent variables, and ran the regression. By standardising the variables before running the

regression, we have put all of the variables on the same scale, and we can compare the magnitude

of the coefficients to see which one has more of an effect. We will also notice that the larger betas

are associated with the larger t-values. The standardised beta coefficients give a measure of the

contribution of each variable to the model. A large value indicates that a unit change in this

predictor variable has a large effect on the criterion variable.

For hypothesis 1, standardised beta coefficients of profitability, growth, tangibility, risk,

and size on short-term leverage, long-term leverage, total leverage, market leverage are as follow:

standardised coefficients of profitability, growth, tangibility, risk, and size on short-term leverage

as dependent variable are -0.277, -0.196, 0.071, 0.346, and 0.136. Standardised coefficients of

profitability, growth, tangibility, risk, and size on long-term leverage as dependent variable are -

0.213, 0.364, -0.029, -0.232, and 0.234. Standardised coefficients of profitability, growth,

tangibility, risk, and size on total leverage as dependent variable are -0.502, 0.093, 0.090, 0.151,

and 0.356. Standardised coefficients of profitability, growth, tangibility, risk, and size on market

leverage as dependent variable are as follow: -0.513, 0.109, -0.080, 0.049, and -0.666.

159

For hypothesis 2, the result of the standardised beta coefficients of financing deficit on

net debt issued, net equity issued, and newly retained earning for all samples of firms is explained

as follows. Standardised coefficients of financing deficit (the predictor) on net debt issued the

(criterion) is 0.775. Standardised coefficients of financing deficit (the predictor) on net equity

issued the (criterion) is 0.464. Standardised coefficients of financing deficit (the predictor) on

newly retained earning the (criterion) is -0.236. Standardised coefficients of financing deficit (the

predictor) on repurchase equity (criterion) is -0.002.

It is the same with the result of unstandardised beta coefficients, standardised coefficients

of financing deficit on net debt issued is the highest. In other words the correlation coefficient

between financing deficit on net debt issued is the strongest. It indicates that if firms face 1%

financing deficit then they tend to issue 28.1% net debt and/or 16.9% net equity.

For hypothesis 3, standardised coefficients of net debt issued on monthly and yearly stock

price are positive, ranging from 0.189 to 0.159. From these results, we can conclude that the

highest value is the January stock price while the lowest value is the December stock price.

Standardised coefficients of net equity issued on monthly and yearly stock price are

positive, ranging from -0.082 to -0.064. From these results, we can conclude that the highest value

is the September stock price while the lowest value is the December stock price.

Standardised coefficients of repurchase equity on monthly and yearly stock price are

positive, ranging from 0.881 to 0.347. From these results, we can conclude that the highest value is

the November stock price while the lowest value is the February stock price.

For hypothesis 4, the result of the standardised beta coefficients of financing deficit on

net debt issued, net equity issued, and newly retained earning for growth and mature firms is

explained as follows.

For mature firms, standardised coefficients of financing deficit 1 on its net debt issue is

0.383, it means that the correlation coefficient between the predictor and the criterion variable is

0.383. Standardised coefficient of financing deficit on its net equity issue is 0.254. It means that

the correlation coefficient between the predictor and the criterion variable is 0.254. Standardised

coefficient of financing deficit on its newly retained earning is -0.177. It means that the correlation

coefficient between the predictor and the criterion variable is -0.177.

For growth firms, standardised coefficients of financing deficit 1 on net debt issue is

0.385, it means that the correlation coefficient between the predictor and the criterion variable is

0.385. It is higher than mature firms. Standardised coefficient of financing deficit on net equity

issue is 0.177. It means that the correlation coefficient between the predictor and the criterion

variable is 0.177. It is lower than mature firms. Standardised coefficient of financing deficit of

growth firms on its newly retained earning is -0.285. It means that the correlation coefficient

between the predictor and the criterion variable is -0.285. It is more negative than mature firms.

C. Analysis of Variance (ANOVA)

The F-statistic will be calculated for analysis of variance (ANOVA) to test whether group

population means are all equal or not. When the F-statistic is found significant, we may conclude

that at least one of the population means of the groups differs from the others but ANOVA does

not tell us which groups are different from which others (Bekiro, 2001).

160

For hypothesis 1, short-term leverage, long-term leverage, total leverage, market leverage

as dependent variable and growth, tangibility, risk, size, and profitability as predictors, reaches

statistical significance with F-value of 18.878 and significant value of 0.000 for short-term

leverage, F-value of 15.362 and significant value of 0.000 for long-term leverage, F value of

72.059 and significant value of 0.000 for total leverage, F-value of 67.082 and significant value of

0.000 market leverage. Hence, the statistical significance as depicted in the ANOVA analysis (see

table in appendix) indicates that the models of hypothesis 1 reach statistical significance less than

5%.

For hypothesis 2, after reaching F-statistic result analysis of variance to test whether

group population means are all equal or not, we did not find significant result for all models.

F-value between net debt issued and financing deficit is 76.620 with significance value of

.000. F-value between net equity issued and financing deficit is 13.971 with significance value of

.000. F-value between newly retained earning and financing deficit is 3.010 with significance

value of .089. F-value between repurchase equity and financing deficit is 0.000 with significance

value of 0.993. F-value between net debt issued and financing deficit is higher than F-value

between net equity issued and financing deficit.

From these results, the significance F-values obtained were between financing deficit and

net debt issued and net equity issued, while F-values between financing deficit and newly retained

earning and repurchase equity were not significant.

For hypothesis 3, the statistical significance as depicted in the table of ANOVA shows

that variable of issued debt from January to December did not yield statistical significance The

statistical significance from January to December and yearly stock price became weaker.

For hypothesis 4, for mature and growth firms, the statistical significance as shown in the

ANOVA table in appendix indicates that the models for growth and mature firms reach statistical

significance of less than 5%.

For mature firms, F-value between net debt issue and financing deficit is 15.463 with

significance value of 0.000. F-value between net equity issue and financing deficit is 6.196 with

significance value of 0.015.

For growth firms, F-value between net debt issue and financing deficit is 22.556 (it is

higher than mature firm) with significance value of 0.000. F-value between net equity issue and

financing deficit is 4.219 (it is lower than mature firm) with significance value of 0.042.

D. Coefficient of Determination (R-squared)

For hypotheses 1, R Squared (R2) is the square of the measure of correlation and indicates

the proportion of the variance in the criterion variable which is accounted for by our model. For

hypotheses 1, we also see the adjusted R-square which attempts to yield a more honest value to

estimate the R-squared for the population. When the number of observations is small and the

number of predictors is large, there will be a much greater difference between R-square and

adjusted R-square. By contrast, when the number of observations is very large compared to the

number of predictors, the value of R-square and adjusted R-square will be much closer.

R-squared for hypothesis 1 would be the value between short-term leverage, long-term

leverage, total leverage, market leverage as dependent variable and growth, tangibility, risk, size,

and profitability as predictors.

161

R-squared shows a predictor profitability, growth, tangibility, risk, and size of 0.332 with

short-term leverage as dependent variable. This means that 33.2% of the short-term leverage could

be explained by the existence of those variables. The adjusted R-squared value of tangibility,

growth, risk, size, and profitability as predictors for short-term leverage is 0.314. These provide

evidence that 31.4% of the short-term leverage could be explained by the existence of these

predictors.


long-term leverage as dependent variable. This means that 28.8% of the long-term leverage could


growth, risk, size, and profitability as predictors for long-term leverage is 0.269. These provide

evidence that 26.9% of the long-term leverage could be explained by the existence of these

predictors.


total leverage as dependent variable. This means that 65.5% of the total leverage could be

explained by the existence of those variables. The adjusted R-squared value of tangibility, growth,

risk, size, and profitability as predictors for total leverage is 0.646. These provide evidence that

64.6% of the total leverage could be explained by the existence of these predictors.

R-squared shows a predictor of profitability, growth, tangibility, risk, and size of 0.638

with market leverage as dependent variable. This means that 63.8% of the market leverage could


growth, risk, size, and profitability as predictors for market leverage is 0.629. These provide

evidence that 62.9% of the market leverage could be explained by the existence of these

predictors. Overall, there is no multicollinearity in the regression model of hypothesis 1.

For hypothesis 2, R-squared would be the value between the financing deficit and net

debt and net equity issue as dependent variables. For all sample of firms, R-squared shows a

predictor financing deficit of 0.600, 0.215, 0.056 with net debt issue, net equity issue, newly

retained earning as dependent variables. This means that 60%, 21.5%, and 5.6% of the net debt

issue, net equity issue, and newly retained earning could be explained by the existence of

financing deficit.

For all firms, R-squared shows a predictor financing deficit of 0.000 with repurchase

equity as dependent variables. This means that the repurchase equity almost cannot be explained

by the existence of financing deficit.

For augmented models, for all firms, adjusted R-squared shows a predictor financing

deficit and financing deficit 1 square of (0.603) and with net debt issue as dependent variable. This

means that 60.3% of the net debt issue can be explained by the existence of financing deficit and

financing deficit square.

R-squared of a predictor financing deficit with net debt issue as dependent variable is

higher than net equity issue. This means that the percentage of the net debt issue can be explained

more than net equity issue by the existence of financing deficit.

For hypothesis 3, R-squared would be the correlation between the stock price and the net

debt as dependent variables, the net equity and the debt issuance to repurchase equity as

independent variable.

162

R-squared shows a predictor net debt issued between 0.025 to 0.036 and 0.001 with

monthly and yearly stock price as dependent variables. This means that 2.5% to 3.6%, and 0.1% of

the monthly and yearly stock price could be explained by the existence of net debt issued. We can

see that a predictor net debt issued on January stock price as dependent variables has the highest

R-squared, while R-squared December stock price was the lowest.

The R-squared of a predictor of net equity issuance on January to December ranged

between 0.007 to 0.004 and the yearly stock price of 0.004 as dependent variable. This means that

between 0.7 - 0.4% and 0.4% of the increasing or decreasing of stock price could be explained by

the existence of net equity issue. On September the R-squared got the highest figure, while in

February, March, May, June, and December, it reached the lowest figure.

For all firms, from January to December and yearly stock price, R-squared showed a

predictor issue debt to repurchase equity of between 0.314-0.162 and 0.130 with the stock price as

dependent variable. This means that between 31.4-16.2% and 13.0% of the increasing or

decreasing of stock price could be explained by the existence of issue debt to repurchase equity.

For all firms, R-squared shows a predictor issue debt to repurchase equity on the

December stock price as dependent variables, has the highest R-squared, while R-squared of the

February stock price has the lowest.

For hypothesis 4, R-squared consists of the value for growth and mature firms. For

mature firms, R-squared shows a predictor financing deficit of 0.147 and 0.064 with net debt issue

and net equity issue as dependent variable. This means that 14.7% and 6.4% of the net debt issue

and net equity issue could be explained by the existence of financing deficit.

For growth firms, R-squared shows a predictor financing deficit of 0.148 (it is higher than

of mature firms) and 0.031 (it is lower than of mature firms) with net debt issue and net equity

issue as dependent variable. This means that 14.8% and 3.1% of the net debt issue and net equity

issue could be explained by the existence of financing deficit. Therefore there is no

multicollinearity in the regression model.

For augmented models of mature firms, adjusted R-squared shows a predictor of

financing deficit and financing deficit square of 0.229 and with net debt issue as dependent

variable. This means that 14.8% of the net debt issue could be explained by the existence of

financing deficit and financing deficit square.

For growth firms, adjusted R-squared shows a predictor financing deficit and financing

deficit square of 0.273 (it is higher than mature firm) and with net debt issue as dependent

variable. This means that 14.8% of the net debt issue could be explained by the existence of

financing deficit and financing deficit square.

E. Descriptive Statistics

For hypothesis 1, the average value of short-term leverage is 0.3665 while long-term

leverage is 0.2704, total leverage is 0.6461 and market leverage is 0.7890. The average value of

tangibility, growth, risk, size, and profitability are 0.0717, 0.3887, 15.0107, 0.0747, and 0.8810.

From these results, the highest average is market leverage.

For hypotheses 2, the average value of each variable of hypothesis 2 for all samples of

firms is as follows: The average value of net debt issued is 0.1555 while net equity issued is

0.0777, and newly retained earning is 0.0656. The average value of financing deficit on net debt

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issued, net equity issued, and newly retained earning is 0.5493. The average value of net debt

issued is higher than net equity issued, hence if firms face financing deficit, they rely more heavily

on debt than on equity (indicated by R2, anova, and coefficient of regression). When firms issue

debt to repurchase equity, the average of net debt issued to repurchase equity is 0.149302, while

the average of repurchase equity is negative 0.021233. The financing deficit they face is 0.691921.

From this result we concluded that the average value of net debt issued is the highest.

For variables of hypothesis 3, the monthly stock price is in the range of 2558.6813 to

2384.4211 with July as the highest while the lowest is October. Meanwhile, the yearly stock price

is 3622.2704. The average value of net equity is 0.0317 to 0.0363, while the average value of

repurchase equity is -0.020687.

For hypothesis 4, descriptive statistics for variables of hypothesis 4 consist of average

value of growth and mature firms. Net debt issue (0.0484) of growth firms (NDebt_G) is lower

than net debt (0.0800) of mature firms (NDebt_M), while net equity issue 0.0610 of growth

(NEquity_G) firms is higher than net equity issue 0.0156 of mature firms (NEquity_M) as growth

firm has lower cashflow. The average value of financing deficit for the growth firms is 0.5520

while that of the mature firms is 0.3682 as mature firms has higher cashflow than growth firm.

Even though growth firms issue more equity than mature firms, and mature firms issue

more debt than growth firms, but when mature firms face financing deficit, they rely more heavily

on equity while growth firms rely more heavily on debt. It indicated by R2, anova, coefficient of

regression and augmented. A mature firm has higher cash flow than a growth firm to secure the

debt.

Financing deficit of growth firms is higher than financing deficit of mature firms, as

growing firms have lower cashflow than mature firms. Dividend (0.0026) of growing firms is

lower than dividend (0.0554) of mature firms. Mature firms pay more dividend to shareholders as

they have more cash flow to distribute to shareholders. Long-term leverage 0.3136 of growth firms

is higher than long-term leverage (0.2029) of mature firms. Fixed asset (0.4664) of growth firms is

higher than fixed asset (0.2815) of mature firms. dWorking capital (0.0637) of growth firms is

lower than dWorking capital (0.1016) of mature firms. Cashflow (0.0277) of growth firms is lower

than cashflow (0.0997) of mature firms. Newly retained earning (0.0075) of growth firms is lower

than newly retained earning (0.0587) of mature firms.

6.6. Regression Assumptions of Hypotheses 1, 2, 3, and 4

Before analysing regression coefficients of variables, we must first make several

assumptions about the population of the research. They represent an idealisation of reality, and as

such, they are never likely to be entirely satisfied for the population in any real study (Van Horne,

1998). A good regression model should not have the following assumptions:

1. Multicollinearity

The goal of the multicollinearity test of hypotheses 1, 2, 3, and 4 is to analyse whether there is

correlation between variables. In our research, we test multicollinearity in the regression model by

testing the correlation matrix (Ghozali, 2002), the tolerance values and VIF (Hair et al. 1998). Our

results are as follow:

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Correlations between Variables

For hypothesis 1, the table gives details of the correlation between each pair of variables.

We do not want strong correlations between the criterion and the predictor variables. The values

here are acceptable.

From the correlations matrix, it shows that there is no quite high correlation value (more

than 0.90). Correlation coefficient between profitability and tangibility is -0.394 with significant

value of 0.000. This is an indication that the higher/lower profitability the lower/higher tangibility.

Correlation coefficient between profitability and size is -0.192 with significant value of 0.004.

This is an indication that the higher/lower profitability the lower/higher size of the firm.

Correlation coefficient between profitability and risk is -0.421 with significant value of 0.000. This

is an indication that the higher/lower profitability the lower/higher risk. Correlation coefficient

between profitability and growth is -0.210 with significant value of 0.002. This is an indication

that the higher/lower profitability the lower/higher growth. Correlation coefficient between size

and tangibility is 0.448 with significant value of 0.000. This is an indication that the higher/lower

size, the higher/lower tangibility. Correlation coefficient between size and growth is 0.243 with

significant value of 0.000. This is an indication that the higher/lower size the higher/lower growth.

Correlation coefficient between risk and growth is 0.222 with significant value of 0.001. This is an

indication that the higher/lower risk the higher/lower growth. From this result we concluded that

multicollinearity does not exist in the regression model of hypothesis 1.

For hypothesis 2, correlation between net debt and net equity issued and financing deficit

are significantly positive, while correlation between newly retained earning and financing deficit

are insignificantly negative. For firms, correlation between repurchase equity and financing deficit

are insignificantly negative.

For hypothesis 3, correlation between net debt and January-June stock price are

significantly positive while correlation between net debt and July-December stock price are

positive but not significant. Correlation between net equity and the yearly stock price or the price

for each month during the year is negative but not significant. But if we compare with the

repurchase equity, the stock price is positive but not significant.

For hypothesis 4, the values here are acceptable. For mature and growing firms,

correlation between net debt and net equity issue and financing deficit are positively significant. It

indicates that the higher financing deficit the bigger the net debt and net equity issue.

The Tolerance and Variance Inflation Factor (VIF) Value

For hypothesis 1, the objective of analysing the tolerance values are to measure the

correlation between the predictor variables which can vary between 0 and 1. The closer to zero the

tolerance value is for a variable, the stronger the relationship between this and the other predictor

variables. We should worry about variables that have a very low tolerance. SPSS will not include a

predictor variable in a model if it has a tolerance of less that 0.0001. Meanwhile, variance inflation

factor (VIF) value is an alternative measure of collinearity (in fact it is the reciprocal of tolerance)

in which a large value indicates a strong relationship between predictor variables.

For variables of hypothesis 1, short-term leverage, long-term leverage, total leverage, and

market leverage as dependent variables and growth, tangibility, risk, size, and profitability as

predictors, the tolerance values were also above the cut-off point 0.10 and the VIF values were

below 10, indicating that multicollinearity was not a problem (Hair et al. 1998).

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For variables of hypothesis 2, the tolerance values for net debt issued, net equity issued,

newly retained earning, repurchase equity, financing deficit1 were above the cut-off point 0.10 and

the VIF values were below 10.

For variables of hypothesis 3, the tolerance values for net debt issued on monthly and

yearly stock price were above the cut-off point 0.10 and the VIF values were below 10. While the

tolerance values for net equity issued on monthly and yearly stock price were above the cut-off

point 0.10, the VIF values were below 10. The tolerance values for issuing debt to repurchase

equity on monthly and yearly stock price were above the cut-off point 0.10 and the VIF values

were below 10.

For variables of hypothesis 4, for mature and growing firms, the tolerance values for net

equity issued, net debt issued, and financing deficit 1 were above the cut-off point 0.10 and the

VIF values were below 10. Hence, from tolerance and VIF values of hypotheses 1, 2, 3, and 4

testing results indicate that multicollinearity was not a problem.

2. Autocorrelation

For hypotheses 1, 2, 3, and 4, a test of autocorrelation aims to examine whether in a linear

regression model has correlation between gadfly errors in the period t with an error in the periodt-1

(before). One of the methods that we used to detect autocorrelation is the Durbin Watson (DW).

DW value shows that there is no autocorrelation in the regression model.

As a conservative rule of thumb, Field (2009) suggests that DW values less than 1 or

greater than 3 are definitely cause for concern, however, values closer to 2 may still be

problematic depending on the sample and model.

For hypothesis 1, DW value between short-term leverage as dependent variable and

predictors of growth, tangibility, risk, size, and profitability is 1.335. For all firms, DW value

between long-term leverage as dependent variable and predictors of growth, tangibility, risk, size,

and profitability is 1.274. For all firms, DW value between total leverage as dependent variable

and predictors of growth, tangibility, risk, size, and profitability is 0.994. For all firms, DW value

between market leverage as dependent variable and predictors of growth, tangibility, risk, size, and

profitability is 1.133. A value greater than 2 indicates a negative correlation between adjacent

residuals whereas a value below 2 indicates a positive correlation.

For hypothesis 2, DW value between net debt issued, net equity issued, newly retained

earning, repurchase equity were 1.667, 2.502, 1.494, and 1.907. A value greater than 2 indicates a

negative correlation between adjacent residuals whereas a value below 2 indicates a positive

correlation.

For hypothesis 3, DW value of predictor net debt issued and monthly stock price as

dependent variables ranged from 0.862 to 0.546. DW value of predictor net debt issued and yearly

stock price as dependent variable was 1.558. A value greater than 2 indicated a negative

correlation between adjacent residuals whereas a value below 2 indicated a positive correlation.

For net equity issued, it ranged from 0.820 to 0.532. The DW value of predictor was 1.574. The

DW value of predictor for debt issuance to repurchase equity ranged from 1.049 to 0.598. The DW

value for yearly stock price as dependent variable was 1.577.

For hypothesis 4, DW value of net debt and net equity issue and financing deficit are

1.602 and 2.284. For growing firms, DW value between net debt and net equity issue and

financing deficit are 1.670 and 2.108. A value greater than 2 indicates a negative correlation

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between adjacent residuals whereas a value below 2 indicates a positive correlation. Hence, for

growing and mature firms, there are no DW values less than 1 or greater than 3 which definitely

cause concern.

3. Heteroscedasticity

Test of heteroscedasticity of hypotheses 1, 2, 3, and 4 aims to interpret whether the

regression model has the differences residual variance from one observation to another observation

(Ghozali, 2002). If the residual variance from one observation to another observation is the same,

it is called homoscedasticity.

The graphic of scatterplot (in appendix) shows that the dots have not established a

specific pattern. Some of the dots located adjacent but some other dots spread above and below the

numbers of 0 at the axis Y. Thus, data in the graphics exhibits homoscedasticity.

4. Normally Distributed

From the result of testing hypotheses 1, 2, 3, and 4, to test the normal distribution that we

can see from the graphics of histogram and normal P-P plot (in appendix), we concluded that the

histogram gave the normal pattern of distribution. Meanwhile, the graphic of normal P-P plot

shows that the dots spread around the diagonal line, and the spreading follows the diagonal line.

Both of graphics show that the data meets reasonable assumption of normality.

Therefore, based on the results of assumptions of population described above, the

regression model does not have the assumptions of heteroscedasticity, multicollinearity,

autocorrelation, and the data are normally distributed. Thus, our regression model is appropriate to

use for testing the hypotheses 1, 2, 3, and 4.

6.7. Results of Panel Data Regression Analysis and the Comparison to Regression Analysis

We applied mixed-effect linear regression to analyse longitudinal/panel data which

reported both fixed effects and random effects. Panel data (also known as longitudinal or cross-

sectional time-series data) is a dataset in which the behaviour of entities is observed across time.

Panel data allows us to control for variables we cannot observe or measure; or variables that

change over time but not across entities with panel data we can include variables at different levels

of analysis suitable for multilevel or hierarchical modeling.

Two techniques use to analyse panel data are fixed effects and random effects. Hence, we

use multilevel mixed-effect models to analyse our data with using mixed-effect linear regression

so that we can report both fixed effects and random effects of the models. Fixed-effects are used

whenever we are only interested in analysing the impact of variables that vary over time. Fixed-

effects explore the relationship between predictor and outcome variables within an entity. Each

entity has its own individual characteristics that may or may not influence the predictor variables.

When using fixed-effects, we assume that something within the individual may impact or bias the

predictor or outcome variables and we need to control for this. This is the rationale behind the

assumption of the correlation between the entity‟s error term and predictor variables. Fixed-effects

remove the effect of those time-invariant characteristics from the predictor variables so we can

assess the predictors‟ net effect.

Another important assumption of the fixed-effects model is that those time-invariant

characteristics are unique to the individual and should not be correlated with other individual

characteristics. Each entity is different, therefore the entity‟s error term and the constant (which

captures individual characteristics) should not be correlated with the others. If the error terms are

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correlated then fixed-effects is not suitable since inferences may not be correct and we need to

model that relationship (probably using random-effects).

Meanwhile, the rationale behind the random effects model is that, unlike the fixed effects

model, the variation across entities is assumed to be random and uncorrelated with the predictor or

independent variables included in the model: “…the crucial distinction between fixed and random

effects is whether the unobserved individual effect embodies elements that are correlated with the

regressors in the model, not whether these effects are stochastic or not” (Green, 2008).

If we have reason to believe that differences across entities have some influence on our

dependent variable then we should use random effects. An advantage of random effects is that we

can include time invariant variables. Random effects assume that the entity‟s error term is not

correlated with the predictors which allows for time-invariant variables to play a role as

explanatory variables. In random-effects we need to specify those individual characteristics that

may or may not influence the predictor variables. The problem with this is that some variables

may not be available therefore leading to omitted variable bias in the model. Random-effects are

allowed to generalise the inferences beyond the sample used in the model.

Tables in appendix are our results of mixed-effect linear regression which reports both

fixed effects and random effects for each hypothesis. Meanwhile, if we compare the results of

regression and panel data regression, the following are the summary for each hypothesis and

analysis.

Table 6.24. Summary of Panel Data Regression and Regression Results of Hypothesis 1

Variables Panel Data Regression Result Regression Result

PRFT Negative significant Negative significant

TANG Negative significant Negative significant

SIZE Positive not significant Positive not significant

RISK Positive significant Positive significant

GROW Positive significant Positive significant

Dependent variable: STL



TANG Positive significant Positive significant

SIZE Negative not significant Negative not significant

RISK Negative not significant Negative significant


Dependent variable: LTL



TANG Positive significant Positive not significant

SIZE Positive not significant Positive not significant

RISK Positive not significant Positive significant


Dependent variable: TLV



TANG Positive significant Positive significant

SIZE Positive not significant Negative not significant

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RISK Positive not significant Positive not significant

GROW Negative significant Negative significant

Dependent variable: MRL

For hypothesis 1, the results of regression and panel data regression are consistent for all

variables except for SIZE on MRL. The influence of PRFT and TANG on STL are negative

significant while the influence of RISK and GROW on STL are positive significant. However, the

influence of SIZE on STL is positive but not significant. The influence of TANG and GROW on

LTL are positive significant while the influence of PRFT on LTL are negative significant.

However, the influence of SIZE on LTL is negative but not significant. The influence of RISK on

LTL is negative.

The influence of GROW on TLV is positive significant while the influence of PRFT on

TLV is negative significant. However, the influence of SIZE on TLV is positive but not

significant. The influence of TANG and RISK on TLV is positive. The influence of TANG on

MRL is positive significant while the influence of PRFT and GROW on MRL are negative

significant. However, the influence of RISK on MRL is positive but not significant.



NDEBT Positive significant Positive significant

NEQUITY Negative not significant Positive significant

NRE Negative significant Negative not significant

Independent variable: FD


FD Positive significant Positive significant

FDSQR Positive not significant Positive not significant

Dependent variable: NDEBT


FD Negative significant Negative not significant

Dependent variable: REPO EQUITY

For hypothesis 2, the results of regression and panel data regression are consistent for all variables

except FD on NEQUITY. The influence of FD on NDEBT is positive significant while the

influence of FD on NRE is negative significant from panel data regression, but not significant

from regression result. However, the influence of FD on NEQUITY is negative insignificant from

panel data regression, but positive significant from regression result. The influence of FD on

NDEBT is positive significant while the influence of FDSQR on NDEBT is positive but not

significant. Meanwhile, the influence of FD on REPOEQUITY is negative significant from panel

data regression, but insignificant from regression result.



NDEBT Positive not significant Positive not significant

NEQUITY Negative not significant Negative not significant

REPO EQUITY Positive significant Positive not significant

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Dependent Variable: P_Yearly

For hypothesis 3, the results of regression and panel data regression are consistent for all

variables. The influence of NDEBT on P_Yearly is positive insignificant while the influence of

NEQUITY on P_Yearly is negative insignificant. Meanwhile, the influence of REPOEQUITY on

P_Yearly is positive significant from panel data regression, but positive insignificant from

regression result.



NDEBT_M Positive significant Positive significant

NEQUITY_M Positive significant Positive significant

NRE_M Negative significant Negative not significant

Independent Variable: FD_M


FD_M Positive significant Positive significant

FDSQR_M Negative not significant Negative significant

Dependent Variable: NDEBT_M


NDEBT_G Positive significant Positive significant

NEQUITY_G Positive significant Positive significant

NRE_G Negative significant Negative significant

Independent Variable: FD_G


FD_G Positive significant Positive significant

FDSQR_G Negative significant Negative significant

Dependent Variable: NDEBT_G

For hypothesis 4, for mature firms, the results of regression and panel data regression are

consistent for all variables. The influence of FD on NDEBT and NEQUITY are positive

significant while the influence of FD on NRE is negative significant from panel data regression,

but not significant from regression result. The influence of FD on NDEBT is positive significant

while the influence of FDSQR on NDEBT is negative significant from regression result, but not

significant from panel data regression result.

For hypothesis 4, for growth firms, the results of regression and panel data regression are

consistent for all variables. The influence of FD on NDEBT and NEQUITY are positive

significant while the influence of FD on NRE is negative significant. The influence of FD on

NDEBT is positive significant while the influence of FDSQR on NDEBT is negative significant.

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7. CONCLUSION

7.1. Conclusion

Based on the results analysis of each hypotheses testing, overall, our conclusions are as

follow: For hypothesis 1, profitability has a negative significant regression coefficient on short-

term leverage, long-term leverage, and total leverage. This suggests that highly profitability firms

are less likely to use short-term leverage, long-term leverage, and total leverage, for financing their

investments than the low profitability firms. Finally, profitability has a negative significant

regression coefficient on market leverage. This suggests that highly profitability firms are less

likely to use market leverage for financing their investments than the low profitability firms.

Tangibility has a negative significant regression coefficient on short-term leverage, this

suggests that highly tangibility firms are less likely to use short-term leverage for financing their

investments than the low tangibility firms. Tangibility has a positive significant regression

coefficient on long-term leverage; this suggests that highly tangibility firms are more likely to use

long-term leverage for financing their investments than the low tangibility firms.

Tangibility has a positive but not significant regression coefficient on total leverage,

while tangibility has a positive significant regression coefficient on market leverage. This suggests

that highly tangibility firms are more likely to use market leverage for financing their investments

than the low tangibility firms.

Size has a positive but not significant regression coefficient on short-term leverage and

total leverage, while size has a negative but not significant regression coefficient on long-term

leverage. However, size has a negative significant regression coefficient on market leverage. This

suggests that high size firms are less likely to use market leverage for financing their investments

than low size firms.

Risk has a positive significant regression coefficient on short-term leverage and total

leverage. This suggests that highly risk firms are more likely to use short-term leverage and total

leverage for financing their investments than the low risk firms. Meanwhile, risk has a negative

significant regression coefficient on long-term leverage this suggests that highly risk firms are less

likely to use long-term leverage for financing their investments than the low risk firms. However,

risk has a positive but not significant regression coefficient on market leverage.

Growth has a positive significant regression coefficient on short-term, long-term, and

total leverage, which suggests that highly growth firms are more likely to use short-term, long-

term, and total leverage for financing their investments than the low growth firms. However,

growth has a negative significant regression coefficient on market leverage. This suggests that

high growth firms are less likely to use market leverage for financing their investments than the

low growth firms.

For hypothesis 2, from tables, we can conclude that the financing deficit has positive

significant effects on net debt issue and on net equity issue. This result suggests that high deficit

firms would tend to issue more net debt and net equity to finance the financing deficit. The

financing deficit has negative but not significant effects on newly retained earning. This result

suggests that high deficit firms would not tend to use newly retained earning to finance the

financing deficit. The financing deficit has negative but not significant effects on repurchase

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equity. This result suggests that high deficit firms would not tend to repurchase equity to finance

the financing deficit.

From the descriptive table, we see that the amount of net debt issue is more than net

equity issue and it is consistent with regression results. For the augmented model, our result shows

a positive coefficient on the financial deficit and also on the squared deficit term. However, for the

squared deficit term, the coefficient was not significant. It implies that firms are limited by their

debt capacity constraints and they have to resort to issuing equity. A squared deficit coefficient

that is not large in absolute value implies a less reliance on equity finance for values of the

financing deficit.

Therefore, we can conclude that our sample of firm prefers external to internal financing

and debt to equity if external financing is used. However, firms are limited by their debt capacity

constraints and they have to resort to issuing equity.


the stock price of from January to December. This indicates that net debt has no significant impact

on the yearly stock price. Net equity has no negative significant impact on the stock price from

January to December. The result indicates that net equity has no significant impact on the stock


stock price, while issue more net debt, the firm would tend to have increasing stock price. Result

also suggests that firms that repurchase equity would tend to have increasing stock price.

For hypothesis 4, the growing firms, from tables we can conclude that the financing

deficit has positive significant effects on net debt issue, financing deficit has positive significant

effects on net equity issue, and financing deficit has negative significant effects on newly retained

earning. For mature firms, from tables we can conclude that the financing deficit has positive

significant effects on net debt issue and net equity issue, while financing deficit has negative

insignificant effects on newly retained earning. From these results, we can conclude that our

mature and the sample of growth firms prefer external to internal financing and debt to equity if

external financing is used. Overall, we find that the pecking order theory describes the financing

patterns of growth firms better than mature firms as mature firms are more closely adopted by

analysts and are better known to investors, and hence, should suffer less from problems of

information asymmetry.

7.2 Conclusion regarding Result and Its Consistency with Condition of Indonesian Capital

Market

Our findings are implied that high growth firms in the manufacturing sector of the LQ45 Index are

more likely to use short-term leverage, long-term leverage, and total leverage for financing their

investments than low growth firms. However, firms with relatively high growth use less market

leverage. Firms with relatively high growth will tend to issue securities less subject to information

asymmetries, i.e. shot-term debt. Firms in the manufacturing sector of the LQ45 Index with

relatively high growth are also to use more long-term and total leverage as when they use long-

term leverage and total leverage for financing their investments, they have asset tangibility to

secure their long-term debt.

Even though high growth firms will face more information asymmetries, in the Indonesia

Capital Market has already had the regulation to minimise information asymmetries, such as

Regulation of Bapepam-LK No.X.K.1 regarding disclosure of information that must be announced

to the public, and the attachment of Chairman Decision of Bapepam No.Kep-86/PM/1996 dated 24

January 1996 and Decision of the Board of Directors of PT. Indonesia Stock Exchange No: Kep-

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306/BEJ/07-2004 dated July 19, 2004 concerning Rule Number I-E on the Obligation to Deliver

Information.

For firms in the manufacturing sector of the LQ45 Index, financing constraints will be

more easily solved, as they have more access to banking. Banks will be more recognised and

trusted than the companies. It is not excessive considering each moment banks can determine the

condition of the company's financial through various disclosure of information which announced

by the company in the Stock exchange. The rate of interest charged may also be lower, considering

that the credit risk of public companies is relatively smaller. Generally the buyer of a letter of debt

would certainly prefer if the company which issues letters of debt has become a public company,

especially firms from the LQ45 Index.

High profitability firms in the manufacturing sector of the LQ45 Index are less likely to

use short-term leverage, long-term leverage, total leverage, and market leverage for financing their

investments. Profitability firms in the manufacturing sector has low risk, firms prefer use more

internal funds to external funds. High profitability firms in the manufacturing sector of the LQ45

Index use their retained earning and do not want to take benefit from the tax shield.

Result showed that high risk firms in the manufacturing sector of the LQ45 Index have

lower long-term leverage as long-term leverage need more collateral to secure this leverage.

Earning volatility is proxy for the probability of financial distress and the firm will have to pay

risk premium to outside fund providers. To reduce the cost of capital, a firm will first use

internally generated funds and then outsider funds. However, our results showed that high risk

firms in the manufacturing sector use more short-term leverage, total leverage, and market

leverage than low risk firms. In Indonesia, for firms in the manufacturing sector of the LQ45

Index, financing constraints will be more easily solved, and rate of interest charged may also be

lower, considering that the credit risk of public companies is relatively smaller, and generally the

buyer of a letter of debt would certainly prefer if the company is from the LQ45 Index.

Small firms often suffer the problems associated with asymmetric information, such as

adverse selection, and they have to face higher bankruptcy costs, greater agency costs and bigger

costs to resolve the higher informational asymmetries. That is why there is a positive relationship

between size and STL and TLV of our manufacturing firm. As Rajan and Zingales (1995) argued

that there was less asymmetrical information about the larger firms. This reduced the chances of

undervaluation of the new equity issue and thus encouraged the large firms to use equity financing.

Hence, larger firms in the manufacturing sector of the LQ45 Index have less long-term leverage

and market leverage. Meanwhile, size positively related with total leverage and short-term

leverage was consistent with trade-off theory. It implies that larger firms would take the tax shield

benefit.


Index use more long-term leverage, total leverage, and market leverage. Having the incentive of

getting debt at lower interest rate, a firm with higher percentage of fixed asset is expected to

borrow more as compared to a firm whose cost of borrowing is higher because of having less fixed

assets. However, high tangibility firms in the manufacturing sector of the LQ45 Index use less

short-term leverage, it implies that short-term leverage needs less tangibility of assets.

For hypothesis 2, we imply that manufacturing firms of the LQ45 Index prefers external

to internal financing and debt to equity if external financing is used. It follows pecking order

theory.

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In Indonesia, all listed firms, including older-mature-large and young-growth-small firms

have less problems of information asymmetry as the government of Indonesia has issued the

regulations in order to make all listed firms announce all information about firms.

Firms in the manufacturing sector of the LQ45 Index firms have a good reputation to

mitigate the adverse selection problem between borrowers and lenders. In Indonesia, by listing on

the Indonesia Stock Exchange (IDX), banks can determine the condition of the company's

financials through various disclosure of information which announced by the company in the

Stock exchange. Rate of interest charged may also be lower considering that the credit risk of

public companies is relatively smaller.

The company in Indonesia has a variety of alternative to choose sources of funding,

whether from inside or outside the company. Alternative funding from company is generally using

retained earnings of the company. While alternative financing from external company comes from

creditors in the form of debt, other forms of financing or the issuance of debentures, as well as

equity in the form of shares.

In Indonesia, the stock is one of the most popular financial market instruments. Issuing of

shares is one of option to the company when they want to raise the fund. On the other hand, the

stock is an investment instrument that has been chosen by the investor, because shares are able to

provide an attractive rate of return. Stock can be defined as a sign of ownership of a person or

party (entity) within a company. With the stock,that party has a claim on corporate earnings,

claims on corporate assets, and the right to attend the general meeting of shareholders.

The Indonesian capital market issued various regulations. However, all provisions will

help companies to develop in a good way in the future. By issuing equity, many benefits can be

obtained by the company including: obtaining new funding sources, providing competitive

advantage for business development, merger or acquisition another company through the issuance

of new shares, and increased the corporate value.


the stock price of from January to December. This indicates that net debt has no significant impact

on the yearly stock price. Net equity has no negative significant impact on the stock price from

January to December. The result indicates that net equity has no significant impact on the stock


stock price, while issue more net debt, the firm would tend to have increasing stock price. Result

also suggests that firms that repurchase equity would tend to have increasing stock price.

In Indonesia, why can the stock price go up and down? Stock price movements are

determined by supply and demand for these shares. Demand increases, the stock price increases

and vice versa. Factors that affect stock price movements are including the movements in interest

rates, inflation, exchange rate of the Rupiah, performance of the company, such as sales and profit

increases, for dividends.

For hypothesis 4, we imply that our growth and mature firms in the manufacturing sector

of the LQ45 Index prefers external to internal financing and debt to equity if external financing is

used. Therefore, both kinds of firms are following the pecking order theory. Specifically, the

results imply that deficit of mature firms is solved more by net equity issue while deficit of growth

firms is solved more by net debt issue.

Following pecking order theory, growth firms should face more asymmetric information

in capital markets. However, in the Indonesian capital market, namely IDX, information

174

asymmetry both for growth and mature firms has rarely happened as the government of Indonesia

has stipulated the regulations regarding information asymmetry. The efforts of the government are

as follow: doing Rationalisation for Information Disclosure Obligations of Issuer, develop

Protection Scheme of Investor, and improving the Quality of Financial Transparency Information

of Capital Market Industry.

7.3. To What Extent is the Study Scientifically Relevance

The pecking order theory of capital structure is one of the most influential theories of

corporate leverage. Firms seeking outside finance naturally face an adverse selection, and hence

mispricing, problem. In order to avoid mispricing, firms finance investments internally if they can,

and if they cannot, they prefer debt to equity since debt is less sensitive to outside investors not

knowing the value of firms‟ investment projects (Myers and Majluf, 1984). Shyam-Sunder and

Myers (1999) show that the pecking order is a good first order description for the time series of

debt finance for large mature firms. But these firms should face little asymmetric information in

capital markets.

Frank and Goyal (2003) argue that the support for the standard pecking order in Shyam-

Sunder and Myers depends critically on their sample selection. Frank and Goyal argue that the

sample selection of Shyam-Sunder and Myers picks large mature firms and that the standard

pecking order is not a good description of the capital structure decisions for small, young firms in

their larger sample. The results of Frank and Goyal (2003) study, conclude that the pecking order

theory did not explain broad patterns in the data, and they argue that the sample selection of

Shyam-Sunder and Myers picks large mature firms and that the standard pecking order is not a

good description of the capital structure decisions for small, young firms in their larger sample.

The Halov and Heider (2003) argument is that there is no reason to expect the standard pecking

order to work well for all firms.

However, our result summarised that the pecking order was a good descriptor of

corporate financing behaviour for sample of corporations. Our result shows that firms prefer

external to internal financing. Result also seems to suggest that firms rely more heavily on debt

financing rather than on equity financing and it follow pecking order theory.

Regarding the context of a firm‟s life cycle, our results also show that mature and growth

firms are following the pecking order theory, even though 38.5% of our sample are mature firms.

The evidence seems to suggest that mature and growth firms rely more heavily on external

financing to internal financing and debt to equity if external financing is used, therefore they

follow the pecking order theory. Our results also imply that the deficit of mature firms is solved

more by net equity issue while deficit of growth firms is solved more by net debt issue. The

pecking order theory predicts that firms with the greatest information asymmetry problems

(specifically young, growth firms) are precisely those that should be making financing choices.

Therefore, the pecking order theory describes the financing patterns of growth firms better than

mature firms as our finding.

On the subject of the determinants of capital structure of firms in the manufacturing

sector in the Indonesian capital market, the effect of growth on leverage, our results showed that

growth was positively related with short-term leverage, long-term leverage, and total leverage. It

was consistent with the pecking order theory. Our results were in line with what agency

costs/trade-off theory that the growth was negatively related with market leverage. For the effect

of profitability on leverage, comparing the results with the theory, all of our results are negative

and they are in line with the pecking order theory but contradicting with the trade-off theory. For

175

the influence of risk on leverage, our results showed that risk was negatively related with long-

term leverage and it was in line with pecking order theory and trade-off theory.

For the influence of size on leverage, our results showed a positive relation between size

and short-term leverage and total leverage while our results that the size was negatively related

with market leverage and long-term leverage were consistent with the pecking order theory.

According to the pecking order theory, there will be a negative relationship between leverage and

firm size. For the impact of tangibility on leverage, if we compare the results to the theory, the

tangibility is negatively related with short-term leverage, and it is not in line with the pecking

order theory and trade-off theory. For the relationship between tangibility and long-term leverage,

total leverage, and market leverage, are in line with the pecking order theory (positive) and trade-

off theory (positive).

Concerning the effect of issuing debt on stock price, there were several theories that

explained the relationship between capital structure and stock price, such as signalling through

capital structure, pecking order theory, and trade-off theory. Our result is positive. This is

consistent with signalling through capital structure that the increased level of debt indicates the

confidence of the management in the future. Hence it carries greater conviction than a mere

announcement of undervaluation of the firm by the management. The markets normally react

favourably to moderate increases in leverage and negatively to fresh issue of equity. Our result is

also consistent with the pecking order theory, as securities with less adverse selection (debt) will

result in less negative or no market reaction. Finally, our result is in line with the trade-off theory.

If the firm issued securities to take advantage of a promising new opportunity, so it would be good

news to the market.

Regarding the influence of issuing equity on stock price, when we compared the results to

the theory of predictions, our results were consistent with the theory of signalling through capital

structure, pecking order theory, and Jung et al. (1996). Jung et al. (1996) suggested an agency

perspective and argued that equity issues by firms with poor growth prospects reflected agency

problems between managers and shareholders where stock prices would react negatively to news

of equity issues. Regarding repurchased the stock on stock price. Literature offers multiple

explanations for buybacks. One of these explanations is the information/signalling hypothesis.

Because of the asymmetric information between managers and shareholders, share repurchase

announcements are considered to reveal private information that managers have about the value of

the company. According to the information/signalling hypothesis, repurchase announcements

should be accompanied by positive price changes. Hence, overall, our result is in line with the

information/ signalling hypothesis that has immediate implications: repurchase announcements

should be accompanied by positive price changes.

From our analysis above, most of our result is consistent with the theories prediction.

Therefore, our study is scientifically still relevant.

7.4. Recommendations and Suggestions for Further Research

Based on the findings and limitations of the research, the following recommendations can

be made for further research:

1. As we have got low R-squared and adjusted R-squared, it is recommended to extend

longer sampling period and to add the number of sample firms, so that we can reach

higher R-squared and adjusted R-squared.

176

2. In result of hypotheses testing 2 and 3, scatterplot and normal p-p plot indicate that dots

are rarely distributed as data we used are limited. Hence, longer sampling period and

larger amount of sample firms are recommended to use in further research.

3. In further research, the other indices are recommended to use as sample, so that we can

compare our result with another result.

4. As the purpose of our research will not be to produce a theory that is generalisable to all

populations, but will be simply to try to explain what is happening with our research

setting, the Indonesian capital market, therefore, it may be suggested to other researchers

to test the other research settings in a follow-up study.

7.5. Suggestions for Managers

As the result indicates that net debt issue has positive impact on the stock price of from January to

December, and on the yearly stock price, it is a good choice if firms issue more debt and inform

the market. So stock price will increase. On the other hand, the net equity issue has negative effect

on the stock price from January to December. It is suggested to firms to issue less net equity to

anticipate the fall of stock price. Result also suggests that firms can repurchase equity and

announce to public, and will follow by getting higher stock price.

7.6. Managerial Implication

The issue of capital structure is an important strategic financing decision that firms have

to make. Therefore, the results of this study provide some useful information about the capital

structures of firms in the manufacturing sector of the LQ45 Index in Indonesia. As a conclusion, it

can be stated that the findings show evidence that the pecking order theory and trade-off theory

appear to dominate the firms‟ capital structure in Indonesia.

From the results, we can recognise exactly to what extent the firms in manufacturing

sector in Indonesia choose or mix capital structure, based on the following results:

- Determinants or firms characteristics in the manufacturing sector in Indonesia.

- How firms in the manufacturing sector in Indonesia finance their deficit.

- The impact of choosing capital structure on the firm‟s stock price.

- What is the choice of capital structure over the firms‟ life cycle in the manufacturing sector in

Indonesia to finance the investments.

So that the firms can make the financial policy to what extent they choose or mix capital structure

based on the following consideration:

- Determinant or firms characteristics

- Hierarchy preference and cost and benefit which need to trade-off

- The impact on firm stock price

- Firms life cycle

177

BIBLIOGRAPHY

Akhtar, Shumi and Oliver, Barry, “The Determinants of Capital Structure for Japanese

Multinational and Domestic Corporations,” Working Paper Series in Finance, no. 01, (2006 ), 1-

28.

Andrade, G., and Kaplan, S., “How Costly is Financial (not Economic) Distress? Evidence from

Highly Leveraged Transactions that Became Distressed,” Journal of Finance, (1998), 53, 1443-

1494.

Antweiler, W., and M.Z. Frank, “Do U.S. stock markets typically overreact to corporate news

stories?”, working paper, UBC and University of Minnesota, (2006).

Agca, Senay and Mozumdar Abon, “Firm Size, Debt Capacity, and Corporate Financing Choices,”

working paper, (2004).

Barclay, M., Morellec, E., Smith Jr., C.W., “On the debt capacity of growth options,” The Journal

of Business, forthcoming, (2003).

Bulan, Laarni, Subramanian, Narayanan and Tanlu, Lloyd, “On the Timing of Dividend

Initiations,” Financial Management, forthcoming, (2007).

Bulan and Yan, “The Pecking Order of Financing and the Firm‟s Life Cycle,” (2007).

Bulan and Yan, “The Pecking Order of Financing and the Firm‟s Life Cycle,” Banking and

Finance Letters, (2009), 1-16.

Byoun, Soku, “How and When Do Firms Adjust Their Capital Structures Towards Targets?”,

Journal of Finance, forthcoming, (2007).

Barclay, M. J., and Smith, C. W., and Watts, R. L., “The Determinants of Corporate Leverage and

Dividend Policies,” Journal of Applied Corporate Finance, 7, (1995), 4, 4-19.

Barclay, M. J. and Smith, C. W., “On Financial Architecture: Leverage, Maturity and Priority,”

Journal of applied Corporate Finance, 8, (1996), 4, 4-17.

Barclay, M. J. and Smith, C. W., “The Capital Structure Puzzle: Another Look at the Evidence,”

Journal of Applied Corporate Finance, 12, (1999), 1, 8-20.

Berger, P. G., Ofek, Yermack D. L., “Managerial Entrenchment and Capital Structure Decisions,”

Journal of Finance, 52, (1997), 4, 1411-1438.

Berger, A. N. and Udell, G. F., “The economics of small business finance: the roles of private

equity and debt markets in the financial growth cycle,” Journal of Banking and Finance, (1998),

22, 613-673.

Barclay, M.J., Marx, L.M., and Smith, C.W., “The joint determination of leverage and maturity,”

Journal of Corporate Finance, (2003), 9, 149-167.

178

Bernstein, L., and Wild, J.J., “Financial Statement Analysis: Theory, Application and

interpretation,” (Irwin –Mcgraw-hill, 1998).

Booth, Laurence, Varouj, Aivazian, Asli Demirguc-Kunt, and Vojislav, Maksimovic, “Capital

Structures in Developing Countries,” Journal of Finance, (2001), 56, 87-130.

Bradley, Michael, George A. Jarrell, and E. Han Kim, “On the existence of an optimal capital

structure: theory and evidence,” Journal of Finance, (1984), 39, 857-880.

Burgman, T.A., “An empirical examination of multinational corporate capital structure,” Journal

of International Business Studies 27, (1996), 3, 553-57.

Çağlayan and Şak, “The Determinants of Capital Structure: Evidence from the Turkish Banks,”,

Journal of Money, Investment and Banking, (2010), 15, 1-9.

Chang, Chun, “Capital structure as optimal contracts,” North American Journal of Economics and

Finance 10, (1999), 2, 363-85.

Chaplinsky, Susan and Greg, Niehaus, “Do inside ownership and leverage share common

determinants?”, Quarterly Journal of Business and Economics 32, (1993), 4, 51-65.

Chen, C.J.P., Cheng, A., He, N. and Kim, J., “An investigation of the relationship between

international activities and capital structure,” Journal of International Business Studies 23, (1997),

3, 563-77.

Chen, Yinghong and Hammes, Klaus, “Capital Structure : Theories and Empirical Results - A

Panel Data Analysis,” CERGU‟s Project Reports 04, (2003), 1, 1-32.

Chittenden, F., Hall, G., and Hutchinson, P., “Small Firm Growth, Access to Capital Markets and

Financial Strcture: Review of Issues and an Empirical Invesigation,” Small Business Economics,

(1996), 8, 59-67.

Chkir, I.E. and Cosset, J-C., “Diversification strategy and capital structure of multinational

corporations,” Journal of Multinational Financial Management (2001), 11, 17-37.

Chung, K. H., “Asset Characteristics and Corporate Debt Policy: An Empirical Test,” Journal of

Business Finance and Accounting 20, (1993), 1, 83-98.

Creswell, J. W. (2003), “Research Design : Qualitative, Quantiative, and Mixed Methods

Approaches” (2nd

. Ed), (Thousand Oaks, CA : Sage, 2003).

Dann, L., and W. Mikkelson, “Convertible debt issuance, capital structure change and financing-

related information: Some new evidence,” Journal of Financial Economics (1984), 13, 157–186.

DeAngelo, H. and R. Masulis, “Optimal capital structure under corporate and personal taxation,”

Journal of Financial Economics (1980), 8,3-29.

De jong, A., “An Empirical Analysis of Capital Structure Decisions in Dutch Firms”, Tilburg

University. Ph.D.Dissertation, (1999).

179

Donaldson, G., “Corporate Debt Capacity: A Study of Corporate Debt Policy and the

Determination of Corporate Debt Capacity,” Harvard Business School, Division of Research,

Harvard University, (1961).

Doukas, J.A. and Pantzalis, C., “Geographic diversification and agency costs of debt of

multinational firms,” Journal of Corporate Finance (2003), 9, 59-92.

Drobetz, Wolfgang and Fix, Roger, “What are the Determinants of the Capital Structure ? Some

Evidence for Switzerland,” Swiss Journal of Economics and Statistics Working Paper 4, (2003), 3,

1-38.

De Angelo, Harry, Linda De Angelo and Rene Stulz, “Dividend Policy and the

Earned/Contributed Capital Mix: A Test of the Lifecycle Theory,” Journal of Financial

Economics, forthcoming, (2005).

Dickinson,Victoria, “Firm Life Cycle and Future Profitability and Growth,” working paper,

School of Business, University of Wisconsin – Madison, (2006).

Diamond, Douglas W., “Reputation Acquisition in Debt Market,” Journal of PoliticalEconomy 97,

(1989), 4, 828-862.

Dwidjowijoto. R.N., ”Indonesia 2020. Sebuah Sketsa tentang Visi & Strategi dalam

Kepemimpinan Manajemen Politik and Ekonomi,” RBI Reseach, (1998).

Evans, David S., “The Relationship between Firm Growth, Size, and Age: Estimates for 100

Manufacturing Industries,” the Journal of Industrial Economics, Vol. 35, No. 4, The Empirical

Renaissance in Industrial Economics, (1987), 567-581.

Easterbrook, F., “Two agency cost explanations of dividends,” American Economic Review,

(1984), 74, 650-659.

Easterby-Smith, M., Thorpe, R. and Lowe, A., (2002), “Management Research : An Introduction”

(2nd ed), (London, Sage, 2002).

Eckbo, B.E., “Valuation effects of corporate debt offerings,” Journal of Financial Economics,

(1986), 15, 119–151.

Eckbo, B.E., and R.W. Masulis, “Seasoned equity offerings: A survey,” in Robert A. Jarrow,

Vojislav Maksimovic, and William Ziemba, (eds.) Finance, (1995), pp.1017–1072 (Vol 9 of

Handbooks in Operation Research and Management Science, North-Holland, Chapter 31).

Eckbo, B.E., R.W. Masulis, and O. Norli, “Security offerings”, in B.E. Eckbo, (ed.) Handbook of

Corporate Finance: Empirical Corporate Finance, Vol. 1. In: Handbook of Finance Series, Chapter

6 (Elsevier/North-Holland, Amsterdam, 2007).

Eckbo, B.E., and O. Norli, “The choice of seasoned-equity selling mechanisms: Theory and

evidence,” dartmouth,Working paper, (2004).

Emmery, D.R., and Finnerty, J.D., “Corporate financial management”, (Prentice Hall: Upper

Saddle River NJ., 2001).

180

Fama, Eugene, F., and French, Kenneth, “Testing tradeoff and pecking order predictions about

divideds and debt,” The Center for research in Security Prices Workinh Paper, 506, (2000).

Fama, E.F. and Jensen, Michael, “Agency problem and residual claims,” Journal of Law and

Economics, (1983), 26, 327-349.

Fama, E. and K. French, “The Corporate Cost of Capital and The Return on Corporate

Investment,” Journal of Finance, (1999), 54, 1939-1967.

Frank, Murray, Z. and Goyal, Vidhan, K., “Capital structure decisions,” Journal of Financial

Economics, (2003), 67, 217-248.

Friend, Irwin, and Larry H.P. Lang, “An empirical test of the impact of managerial self-interest on

corporate capital structure,” Journal of Finance, (1988), 43, 271-281.

Frank, Murray Z. and Vidhan K.Goyal, “Testing the Pecking Order Theory of Capital Structure,”

Journal of Financial Economics, (2003), 67,217-24.

Ghozali, Imam. “Aplikasi Analisis Multivariate dengan Program SPSS”. Publishers of University

of Diponegoro, (2002).

Givoly, D., Hayn, C., Ofer, A. and Sarig, O., “Taxes and capital structure: Evidence from firms

response to the Tax reform act of 1986,” The Review of Financial Studies 5, (1992), 2, 331- 255.

Grullon, Gustavo, Roni Michaely and Bhaskaran Swaminathan, “Are dividend changes a sign of

firm maturity?”, Journal of Business 75, No.3, (2002), 387-424.

Green, J.C., Caracelli, V.J., and Graham, W.F., “Toward a Conceptual Framework for Mixed

Method Evaluation Designs,” Educational Evaluation and Policy Analysis, 11, (1989), 3, 255-274.

Greene, J.C., and Caracelli, V.J. (Eds), “Advances in mixed-method eveluation: the challenges and

benefits of integrating diverse paradigms”, (New Directions for evaluation no. 74), (San Francisco:

Jossey-bass, 1997).

Green, William, (2008), Econometric Analysis (6th edition), (Pearson Prentice Hall, New Jersey,

US, 2008).

Gul, F., “Growth opportunities, capital structure and dividend policies in Japan,” Journal of

Corporate Finance, (1999), 5, 141-168.

Hair, J. F. et al., „‟Multivariate Data Analysis,‟‟ (New Jersey: Prentice-Hall, 1998).

Harris, Milton, and Artur, Raviv, “Capital structure and the information role of debt,” Journal of

Finance, (1990), 45, 321-349.

Haugen, R., and L. Senbet, “The Insignificance of Bankruptcy Costs to the Theory of Optimal

Capital Structure,” Journal of Finance, (1977), 23, 383-393.

Halov, Nikolay and Florian Heider, “Capital Structure, Risk and Asymmetric Information,”

Working Paper, (2005).

181

Helwege, Jean and Nellie Liang, “Is There a Pecking Order? Evidence from a Panel of IPO

Firms,” Journal of Financial Economics, (1996), 40, 429-458.

Hsia, C. C., “Coherence of the modern theories of finance,” Financial Review, Winter, (1981), 27-

42.

Huang, Samuel G. H. and Song, Frank M., “The Determinants of Capital Structure: Evidence from

China,” HIEBS (Hong Kong Institute of Economics and Business Strategy) Working Paper,

(2002), 1-35. See at http://www.hiebs.hku.hk/working_papers.

Hufft, Edward M and Uric Dufrene, “Small Firm Capital Structure Decisions : The Effect of

Agency Cost,” United States Association for Small Business and Entrepreneurship, (1996).

Husnan, S., “Indonesia in Corporate Governance and Finance in East Asia: A Study of Indonesia,

Republic of Korea, Malaysia, Philippines, and Thailand,” Volume Two, (2001).

Zhuang J., David Edwards & Viginita A. Capulong, Asian Development Bank.

Hutchinson, P., “The capital structure and investment decision of small owner – managed firm:

some exploratory issues,” Small Business Economics, (1995), 7, 231-239.

Jensen, Michael C., and William H. Meckling, “Theory of the firm: Managerial Behaviour, agency

costs and ownership structure,” Journal of Financial Economics, (1976), 3, 305-360.

Jensen, M.C and Smith, C.W., ”Shareholders, Managers, and creditor interest: Applications of

agency theory,” (1985), (reprinted in Michael C. Jensen. A theory of the firm: Governance,

Residual Claims and Organisational Forms). Harvard University Prees, (2000), 1-41.

Jensen, M. C., “Agency costs of free cash flow, corporate finance, and takeovers,” The American

Economic Review, 76, May, (1986), 2, 323-339.

Jensen, M., D. Solberg, and T. Zorn, “Simultaneous Determination of Insider Ownership, Debt

and Dividend Policies,” Journal of Financial and Quantitative Analysis, (1992), 27, 247-261.

Jick, T.D., „Mixing Qualitative and Quantiative Methods : Triangulation in Action,‟

Administrative Science Quarterly, December, (1979), 24, 602-611.

JSX LQ45, (2000, 2002, 2005).

Jung, Kooyul, Yong Cheol Kim, and Rene M. Stulz, “Timing, Investment Opportunities,

Managerial Discretion, and Security Issue Decision,” Journal of Financial Economics, (1996), 42,

159-185.

Kester, Carl W., “Capital and Ownership Structure: a Comparison of United States and Japanese

Manufacturing Corporations,” Journal of Financial Management, (1986), 15, 5-16.

Kim, Wi Saeng and Eric H. Sorensen, “Evidence on the Impact of the Agency Costs of Debt in

Corporate Debt Policy,” Journal of Financial and Quantitative Analysis, (1986), 21, 131-144.

Kim, E.H., "A Mean-Variance Theory of Optimal Capital Structure and Corporate Debt Capacity,"

Journal of Finance, March, (1978), 33, 45-63.

http://www.hiebs.hku.hk/working_papers.asp?ID=60

182

Kraus, A. and R.H. Litzenberger, "A State Preference Model of Optimal Financial Leverage,"

Journal of Finance, September, (1973), 911-922.

La Porta, Rafael, Florencio Lopez-de-Silanes, Andrei Shleifer, and Robert Vishny, “Law and

finance,” NBER Working Paper, (1996).

La Porta, Rafael, Florencio Lopez-de-Silanes, Andrei Shleifer, and Robert Vishny, “Legal

determinants of external finance,” Journal of Finance, (1997), 52, 1131-1150.

La Porta, Rafael, Lopez-de Silanes, Florencio, Shleifer, Andrei and Vishny, Robert, “Corporate

ownership around the World,” Journal of Finance, (1999), 54, 471-518.

Lasfer, M. A., “Agency Costs, Taxes and Debt: The U. K. Evidence,” European Financial

Management, 1, (1995), 3, 265-285.

Lee, K. and Kwok C.Y., “Multinational corporations vs. domestic corporations”, (1988).

International environmental factors and determinants of capital structure,” Journal of International

Business Studies 19, 195-217.

Leland, Hayne E., and David L. Pyle, “Informational asymmetries, financial structure, and

financial intermediation,” Journal of Finance, (1977), 32, 371-387.

Leland, H.E. and Toft., K.B., “Optimal Capital structure, endogeneous bankruptcy, and the term

structure of credit spread,” Journal of Finance, (1996), 51, 987-1019.

Long, Michael and Ileen, Maltiz, “The investment-financing nexus: Some empirical evidence,”

Midland Corporate Finance Journal, (1985), 3, 53-59.

Leary, Mark T. and Michael R.Roberts, “The Pecking Order, Debt Capacity, and Information

Asymmetry,” working paper, (2006).

Lemmon, Michael L. and Jaime F.Zender, “Debt Capacity and Tests of Capital Structure

Theories,” working paper, (2004).

Marsh, Paul, “The choice between equity and debt: An empirical study,” Journal of Finance,

(1982), 37, 121-144.

Marshall, Catherine and Gretchen B. Rossman, “Designing Qualitative Research” (3rd

ed),

(Thousand Oaks, CA, Sage Publications, 1999).

Mikkelson, Wayne and M. Megan Partch, “Valuation effects of security offerings and the issuance

process,” Journal of Financial Economics, (1986),15-31-60.

Michaelas, N. Chittenden, F. and Poutziouris, P., “Financial policy and capital structure choice in

UK SMEs: empirical evidence from company panel data,” Small Business Economics, (1999), 12,

113-130.

Miller, Merton, “Debt and Taxes,” Journal of finance, (1977), 32, 261-78.

Milles, M.B. and Huberman, A.M., “Qualitative Data Analysis : A Sourcebook of New Methods”

(2nd

ed.), (Newbury Park, CA : Sage, 1994).

183

Mittoo, U. and Zhang, Z., “The capital structure of multinational corporations: Canadian

evidence,” Working paper, I.H. Asper School of Business, University of Manitoba, (2005).

Modigliani, Franco, and Merton, Miller, “The cost of capital, corporation finance and the theory of

investment,” American Economic Review, (1958), 48, 261-275.

Modigliani, Franco, and Merton H. Miller, “Corporate income taxes and the cost of capital,”

American Economic Review, (1963), 53, 433-443.

Morgan, D., “Practical strategies for combining qualitative and quantitative methods: applications

to health research,” Qualitative Health Research, 8, (1998), 3, 362-376.

Myers, S. C., “Determinants of corporate borrowing,” Journal of Financial Economics, (1977), 5,

147-175.

Myers, S.C., “The Capital Structure Puzzle,” The Journal of Finance, 39, (1984), 3, 575-592.

Myers, Stewart C. and Nicholas S. Majluf, “Corporate financing and investment decisions when

firms have information that investors do not have,” Journal of Financial Economics, (1984), 13,

187-221.

Ocaña, C.; Salas, V. and Vallés, J., “Un análisis empírico de la financiación de la pequeña y

mediana empresa manufacturera española: 1983-1989,” Moneda y Crédito, (1994), 199, 57-96.

Pandey, I. M., “Capital Structure and the Firm Characterstics: Evidence from an Emerging

Market,” IIMA Working Paper, No. 2001-10-04, (2001), 1-19.

Patton, M.Q., “Qualitative Evaluation and Research Methods” (2nd

ed.). (Newbury Park, CA :

Sage, 1990).

Peirson, G., Brown, R, Easton., S. and Howard, P., “Business Finance”, (McGraw Hill: Sydney,

2002).

Petersen, Mitchell A. and Raghuram G. Rajan, “The Effect of Credit Market Competition on

Lending Relationships,” The Quarterly Journal of Economics, 110, (1995), 2, 407-443

Raimond, P., “Management Projects: Design, Research and Presentation,” London, Chapman and

Hall, (1993).

Rajan G. Raghuram, and Luigi, Zingales, “What do we know about capital structure? Some

evidence from international data,” Journal of Finance, (1995), 50, 1421-1460.

Robson, C., (2002), “Real World Research” (2nd edn), (Oxford, Blackwell, 2002).

Rogers, C.R., “On Becoming a Person, Constable”, (London, 1961).

Romano, C. A. Tanewski, G. A. and Smyrnios, K. X., “Capital structure decision making: a model

for family business,” Journal of Business Venturing, 16, (2000), 3, 285-310.

Ross, S., “The Determination of Financial Structure: The Incentive Signalling Approach,” Journal

of Economics, (1977), 8, 23-40.

184

Sartono, A., “Long-term Financing Decisions: Views and Practices of Financial Managers of

Listed Public Firms in Indonesia,” International Journal of Business-Gadjah Mada University 3-1,

(2001), 35-44.

Setiawan, Augustinus, “An empirical Analysis of Debt-equity Choice in Indonesian Companies.

Thesis Submitted for the Degree of Doctor of Philosophy”, Malaysia: the School of Menagement

University Sains, (2004).

Saunders, Mark, Phillip, Lewis, and Adrian, Thornhill, “Research Method for Business Students”

(3rd

ed.), (Prentice Hall, 2003).

Shyam-Sunder, Lakshmi and Myers S.C., “Testing Static Trade-off against Pecking Order Models

of Capital Structure,”, Journal of Financial Economics, (1999), 51, 219-244.

Smith, H., “Strategies of Social Research: The Methodological Imagination”, Englewood Cliffs,

NJ, (Prentice-Hall, 1975).

Smith, Clifford and Ross, Watts, “The Investment Opportunity Set and Corporate Financing,

Dividend, and Compensation Policies,” Journal of Financial Economics, (1992), 32, 263-292.

Sogorb, Mira F, López-Gracia J., “Pecking order versus trade-off: An empirical approach to the

small and medium enterprise capital structure,” Instituto Valenciano de Investigaciones

Económicas (IVIE) working paper, WP-EC 2003-09, (2003), 1-36.

Stiglitz, J.E., "A Re-Examination of the Modigliani-Miller Theorem," American Economic

Review 59, December, (1972), 784-793.

Stiglitz, E. Joseph, “Principal and agent, in the new palgrave Dictionary of money and Finance,”

(1992), 2, 185-190.

Stohs, M. H. and Mauer, D. C., “The Determinants of Corporate Debt Maturity Structure,” Journal

of Business, 69, (1996), 3, 279-312.

Stulz, R. M., “Managerial discretion and optimal financing policies,” Journal of Financial

Economics, (1990), 26, 3-27.

Sbeiti, Wafaa, “The Determinants of Capital Structure: Evidence from the GCC Countries,”

International Research Journal of Finance and Economics, (2010), 47, 1-27.

Shah and Khan, “Determinants of Capital Structure: Evidence from Pakistani Panel Data,”

International Review of Business Research Papers Vol. 3 No.4 October, (2007), 265-282.

Song, Han-Suck, ”Capital Structure Determinants An Empirical Study of Swedish Companies,”

The Royal Institute of Technology,.CESIS, Electronic Working Paper Series, Paper No. 25,

January, (2005), 1-26.

Sugiarto, “Struktur Modal, Struktur Kepemilikan Perusahaan, Permasalahan Keagenan dan

Informasi Asimetri “, (Graha Ilmu, Yogyakarta, 2009).

Tashakkori, A., and Teddlie, C., “Mixed methodology: Combining qualitative and quantitative

approaches”, (Thousand Oaks, CA: Sage, 1998).

185

Tashakkori, A., and Teddlie, C. (Eds), “Handbook of mixed methods in the social and behavioural

sciences”, (Thousand Oaks, CA: Sage, 2003).

Titman, S. and Wessels, R., “The determinants of capital structure choice,” The Journal of

Finance, 43, March, (1988), 1, 1-19.

Van Horne, James C., “Financial Management and Policy” (11th

Edition), (Prentice Hall, 1998).

Vos, Ed; Forlong, Carolyn, "The Agency Advantage of Debt Over the Lifecycle of the Firm,”

Journal of Entrepreneurial and Small Business Finance, Vol. 5, No. 3, (1998), 193-211.

Wald, John K., “How Firm Characteristics Affect Capital Structure: An International

Comparison,” Journal of Financial Research, 22, (1999), 2, 161-187.

Warner, J., “Bankruptcy Costs: Some Evidence,” Journal of Finance, (1977), 32, 337-347.

Wells, P., “Ethics in Business and Management Research,” in Wass, V.J. and wells, P.E. (eds),

Principles and Practice in Business and Management Research, Aldershot, Dartmouth, (1994),

227-97.

Whited, Toni M., “Debt, Liquidity Constraints, and Corporate Investment: Evidence from Panel

Data,” Journal of Finance, (1992), 47, 1425-1460.

Wiwattanakantang, Yupana, (1999), “An empirical study on the determinants of the capital

structure of Thai firms,” Pacific-Basin Finance Journal, (1999), 7, 371-403.

www.bps.go.id

Yin, Robert K., (2003), “Case Study Research : Design and methods” (3rd

ed.), (Thousand Oaks,

CA, Sage Publications, 2003).

Zikmund, W.G., “Business Research Methods” (6th edn), Forth Worth, TX, (Dryden press, 2000).

Zhuang, J., Edwards, Webb, D., and Capulong, M.V., “Corporate Governance and Finance in East

Asia: a Study of Indonesia, Republic of Korea, Malaysia, Philippines, and Thailand,” Volume

One, Asian Development Bank Discussion Paper, (2000), 1-93.

http://www.bps.go.id/

186

APPENDIX

APPENDIX A

Regression Results

Regression Results of Hypothesis 1

Correlation Test Results

STL PRFT TANG SIZE RISK GROW

Pearson

Correlation

STL -.388 -.061 .046 .491 .290

PRFT -.394 -.192 -.421 -.210

TANG .448 -.012 -.009

SIZE -.068 .243

RISK .222

Pearson

Correlation

LTL PRFT TANG SIZE RISK GROW

LTL -.302 .435 .248 -.093 .217

Pearson

Correlation

TL PRFT TANG SIZE RISK GROW

TLV -.694 .326 .303 .434 .516

Pearson

Correlation

MRL PRFT TANG SIZE RISK GROW

MRL -.421 .281 -.098 .120 -.568

Sig. (1-

tailed)

STL . .000 .199 .262 .000 .000

PRFT .000 .004 .000 .002

TANG . .000 .434 .450

SIZE .172 .000

RISK . .001

LTL . .000 .000 .000 .097 .001

TLV .000 .000 .000 .000 .000

MRL . .000 .000 .086 .046 .000

Descriptive Statistics


Mean Std. Deviation

STL .3665 .26502

LTL .2704 .23108

TLV .6461 .25257

MRL .7890 .22083

PRFT .0717 .16581

TANG .3887 .22593

SIZE 15.0107 1.58543

RISK .0747 .07534

187

GROW .8810 .39231

Anova Test Results

ANOVA

Model Sum of

Squares

df Mean

Square

F Sig.

STL Regression 4.546 5 .909 18.878 .000a

Residual 9.150 190 .048

Total 13.696 195

LTL Regression 2.998 5 .600 15.362 .000a

Residual 7.415 190 .039

Total 10.413 195

TLV Regression 8.144 5 1.629 72.059 .000a

Residual 4.295 190 .023

Total 12.439 195

MRL Regression 6.071 5 1.214 67.082 .000a

Residual 3.439 190 .018

Total 9.509 195

a. Predictors: (Constant), GROW, TANG, RISK, SIZE, PRFT

Anova Test Results

Model R Square Adjusted R

Square

ANOVA-F Sig. Durbin-

Watson

STL .332 .314 18.878 .000a 1.335

LTL .288 0.269 15.362 .000a 1.274

TLV .655 0.646 72.059 .000a .994

MRL .638 0.629 67.082 .000a 1.133

a. Predictors: (Constant), Growth, Tangibility, Risk, Size, Profitability

Model Summary

Model Summaryb

Model R R Square Adjusted R

Square

Std. Error of the

Estimate

Durbin-

Watson

STL .576a .332 .314 .21945 1.335

LTL .537a .288 0.269 .19755 1.274

TLV .809a .655 0.646 .15035 .994

MRL .799a .638 0.629 .13453 1.133

a. Predictors: (Constant), GROW, TANG, RISK, SIZE, PRFT

188

Result of Regression of Hypothesis 2

Issue Debt and Issue Equity



Mean Std. Deviation

NDEBT .1555 .13253

FD .5493 .36528

NEQUITY .0777 .13305

FD .5493 .36528

NRE .0656 .05692

FD .5493 .36528

NDEBT .1555 .13253

FD .5493 .36528

FDSQR .4326 .74641

Correlations Test Results

Correlations

NDEBT FD

Pearson

Correlation

NDEBT 1.000 .775

FD .775 1.000

Sig. (1-tailed) NDEBT . .000

FD .000 .

NEQUITY FD

Pearson

Correlation

NEQUITY 1.000 .464

FD .464 1.000

Sig. (1-tailed) NEQUITY . .000

FD .000 .

NRE FD

Pearson

Correlation

NRE 1.000 -.236

FD -.236 1.000

Sig. (1-tailed) NRE . .044

FD .044 .

Augmented Test Results

Correlations

NDEBT FD FDSQR

Pearson

Correlation

NDEBT 1.000 .775 .723

FD .775 1.000 .903

FDSQR .723 .903 1.000

Sig. (1-tailed) NDEBT . .000 .000

FD .000 . .000

FDSQR .000 .000 .

189

Model Summary

Model Summaryb


Square

Std. Error of

the Estimate

Durbin-

Watson

NDEBT .775a .600 .593 .08460 1.667

NEQUITY .464a .215 .200 .11903 2.502

NRE .236a .056 .037 .05585 1.494

NDEBT .777a .603 .588 .08511 1.683

a. Predictors: (Constant), FDSQR, FD

Anova Test Results

ANOVAb

Model Sum of

Squares

df Mean

Square

F Sig.

NDEBT Regression .548 1 .548 76.620 .000a

Residual .365 51 .007

Total .913 52

NEQUIT

Y

Regression .198 1 .198 13.971 .000a

Residual .723 51 .014

Total .921 52

NRE Regression .009 1 .009 3.010 .089a

Residual .159 51 .003

Total .168 52

NDEBT Regression .551 2 .276 38.037 .000a

Residual .362 50 .007

Total .913 52

a. Predictors: (Constant), FDSQR, FD

b. Dependent Variable: NDEBT




Mean Std. Deviation

ISSUEDEBT .149302 .1580033

FD .691921 .4486788

REPOEQUITY -.021233 .0536195

FD .691921 .4486788

NRE -.024417 .2207257

FD .691921 .4486788

190


Correlations

ISSUEDEBT FD

Pearson

Correlation

ISSUEDEBT 1.000 .508

FD .508 1.000

Sig. (1-tailed) ISSUEDEBT . .004

FD .004 .

REPOEQUITY FD

Pearson

Correlation

REPOEQUITY 1.000 -.002

FD -.002 1.000

Sig. (1-tailed) REPOEQUITY . .497

FD .497 .

NRE FD

Pearson

Correlation

NRE 1.000 -.691

FD -.691 1.000

Sig. (1-tailed) NRE . .000

FD .000 .

Model Summary

Model Summaryb


Square

Std. Error of

the Estimate

Durbin-

Watson

ISSUEDEBT .508a .258 .227 .1388900 2.045

REPOEQUITY .002a .000 -.042 .0547251 1.907

NRE .691a .477 .455 .1628877 2.187

a. Predictors: (Constant), FD

b. Dependent Variable: NRE

Anova Test Results

ANOVAb

Model Sum of

Squares

df Mean

Square

F Sig.

ISSUEDEBT Regression .161 1 .161 8.354 .008a

Residual .463 24 .019

Total .624 25

REPOEQUITY Regression .000 1 .000 .000 .993a

Residual .072 24 .003

Total .072 25

NRE Regression .581 1 .581 21.906 .000a

Residual .637 24 .027

Total 1.218 25

a. Predictors: (Constant), FD

b. Dependent Variable: NRE

191

Collinearity Diagnostics

Collinearity Diagnosticsa

Model Dimension Eigen

value

Condition

Index

Variance

Proportions

(Constant) FD

ISSUEDEBT 1 1.844 1.000 .08 .08

2 .156 3.436 .92 .92

REPOEQUITY 1 1.844 1.000 .08 .08

2 .156 3.436 .92 .92

NRE 1 1.844 1.000 .08 .08

2 .156 3.436 .92 .92

a. Dependent Variable: NRE


H3a-NDEBT



Mean Std. Deviation

Jan 2513.0682 4287.90289

NDebt -.0469 .24293

Feb 2491.9886 4232.57989

NDebt -.0469 .24293

Mar 2400.6250 3975.33522

NDebt -.0469 .24293

Apr 2419.1111 3802.51296

NDebt -.0407 .24596

May 2476.4444 3830.31669

NDEBT -.0407 .24596

Jun 2475.1667 3762.53250

NDebt -.0407 .24596

Jul 2558.6813 4040.41223

NDebt -.0406 .24459

Aug 2412.8022 3774.77102

NDebt -.0406 .24459

Sep 2429.1935 3726.00696

NDebt -.0376 .24289

Oct 2384.4211 3877.05586

NDebt -.0405 .24106

Nov 2437.9794 3960.91603

NDebt -.0400 .23875

Dec 2528.8614 4112.29370

NDebt -.0406 .23408

P_yearly 3622.2704 9102.34729

NDebt .0682 .26454

192


Correlations

Jan NDEBT

Pearson Correlation Jan 1.000 .189

NDEBT .189 1.000

Sig. (1-tailed) Jan . .039

NDEBT .039 .

Feb NDEBT

Pearson Correlation Feb 1.000 .183

NDEBT .183 1.000

Sig. (1-tailed) Feb . .044

NDEBT .044 .

Mar NDEBT

Pearson Correlation Mar 1.000 .185

NDEBT .185 1.000

Sig. (1-tailed) Mar . .042

NDEBT .042 .

Apr NDEBT

Pearson Correlation Apr 1.000 .176

NDEBT .176 1.000

Sig. (1-tailed) Apr . .049

NDEBT .049 .

May NDEBT

Pearson Correlation may 1.000 .181

NDEBT .181 1.000

Sig. (1-tailed) May . .044

NDEBT .044 .

Jun NDEBT

Pearson Correlation Jun 1.000 .182

NDEBT .182 1.000

Sig. (1-tailed) Jun . .043

NDEBT .043 .

Jul NDEBT

Pearson Correlation Jul 1.000 .172

NDEBT .172 1.000

Sig. (1-tailed) Jul . .051

NDEBT .051 .

Aug NDEBT

Pearson Correlation Aug 1.000 .171

NDEBT .171 1.000

Sig. (1-tailed) Aug . .052

NDEBT .052 .

Sep NDEBT

Pearson Correlation Sep 1.000 .164

NDEBT .164 1.000

Sig. (1-tailed) Sep . .058

NDEBT .058 .

193


Correlations

Oct NDEBT

Pearson Correlation Oct 1.000 .164

NDEBT .164 1.000

Sig. (1-tailed) Oct . .056

NDEBT .056 .

Nov NDEBT

Pearson Correlation Nov 1.000 .161

NDEBT .161 1.000

Sig. (1-tailed) Nov . .058

NDEBT .058 .

Dec NDEBT

Pearson Correlation Dec 1.000 .159

NDEBT .159 1.000

Sig. (1-tailed) Dec . .057

NDEBT .057 .

Correlations Test Results - P_yearly

Correlations

P_yearly NDEBT

Pearson

Correlation

P_yearly 1.000 .027

NDEBT .027 1.000

Sig. (1-tailed) P_yearly . .351

NDEBT .351 .

P_yearly 196 196

NDEBT 196 196

Model Summary

Model Summaryb

Model R R Square Adjusted

R Square

Std. Error of the

Estimate

Durbin-

Watson

Jan .189a .036 .025 4234.66124 .813

Feb .183a .034 .022 4185.18249 .862

Mar .185a .034 .023 3929.10592 .785

Apr .176a .031 .020 3764.66869 .626

May .181a .033 .022 3788.69224 .669

Jun .182a .033 .022 3720.81323 .636

Jul .172a .030 .019 4002.18252 .678

Aug .171a .029 .018 3739.88399 .616

Sep .164a .027 .016 3695.46306 .546

Oct .164a .027 .016 3845.05185 .662

Nov .161a .026 .016 3929.81298 .580

Dec .159a .025 .015 4080.68740 .594

194

Yearly .027a .001 -.004 9122.33323 1.558

a. Predictors: (Constant), NDEBT

b. Dependent Variable: P_yearly

Anova Test Results

Model Sum of

Squares

df Mean

Square

F Sig.

Jan

Regression 5.741E7 1 5.741E7 3.201 .077a

Residual 1.542E9 86 1.793E7

Total 1.600E9 87

Feb

Regression 5.223E7 1 5.223E7 2.982 .088a

Residual 1.506E9 86 1.752E7

Total 1.559E9 87

Mar

Regression 4.723E7 1 4.723E7 3.059 .084a

Residual 1.328E9 86 1.544E7

Total 1.375E9 87

Apr

Regression 3.966E7 1 3.966E7 2.798 .098a

Residual 1.247E9 88 1.417E7

Total 1.287E9 89

May

Regression 4.258E7 1 4.258E7 2.966 .089a

Residual 1.263E9 88 1.435E7

Total 1.306E9 89

Jun

Regression 4.163E7 1 4.163E7 3.007 .086a

Residual 1.218E9 88 1.384E7

Total 1.260E9 89

Jul

Regression 4.369E7 1 4.369E7 2.728 .102a

Residual 1.426E9 89 1.602E7

Total 1.469E9 90

Aug

Regression 3.758E7 1 3.758E7 2.687 .105a

Residual 1.245E9 89 1.399E7

Total 1.282E9 90

Sep

Regression 3.451E7 1 3.451E7 2.527 .115a

Residual 1.243E9 91 1.366E7

Total 1.277E9 92

Oct

Regression 3.802E7 1 3.802E7 2.571 .112a

Residual 1.375E9 93 1.478E7

Total 1.413E9 94

Nov

Regression 3.900E7 1 3.900E7 2.526 .115a

Residual 1.467E9 95 1.544E7

Total 1.506E9 96

Dec

Regression 4.255E7 1 4.255E7 2.555 .113a

Residual 1.649E9 99 1.665E7

Total 1.691E9 100

Yearly

Regression 1.219E7 1 1.219E7 .146 .702a

Residual 1.614E10 194 8.322E7

Total 1.616E10 195

a. Predictors: (Constant), NDEBT

195


H3b-NEQUITY



Mean Std. Deviation

Jan 2513.0682 4287.90289

NEQUITY .0317 .14489

Feb 2491.9886 4232.57989

NEQUITY .0317 .14489

Mar 2400.6250 3975.33522

NEQUITY .0317 .14489

Apr 2419.1111 3802.51296

NEQUITY .0324 .14360

May 2476.4444 3830.31669

NEQUITY .0324 .14360

Jun 2475.1667 3762.53250

NEQUITY .0324 .14360

Jul 2558.6813 4040.41223

NEQUITY .0363 .14761

Aug 2412.8022 3774.77102

NEQUITY .0363 .14761

Sep 2429.1935 3726.00696

NEQUITY .0357 .14605

Oct 2384.4211 3877.05586

NEQUITY .0352 .14454

Nov 2437.9794 3960.91603

NEQUITY .0341 .14324

Dec 2528.8614 4112.29370

NEQUITY .0327 .14062

P_yearly 3622.2704 9102.34729

NEQUITY .0458 .13826


Correlations

Jan NEQUIT

Y

Pearson Correlation Jan 1.000 -.067

NEQUIT

Y

-.067 1.000

Sig. (1-tailed) Jan . .266

NEQUIT

Y

.266 .

Feb NEQUITY

196

Pearson Correlation Feb 1.000 -.066

NEQUIT

Y

-.066 1.000

Sig. (1-tailed) Feb . .269

NEQUIT

Y

.269 .

Mar NEQUITY

Pearson Correlation Mar 1.000 -.067

NEQUIT

Y

-.067 1.000

Sig. (1-tailed) Mar . .268

NEQUIT

Y

.268 .

Apr NEQUITY

Pearson Correlation Apr 1.000 -.067

NEQUIT

Y

-.067 1.000

Sig. (1-tailed) Apr . .265

NEQUIT

Y

.265 .

May NEQUITY

Pearson Correlation May 1.000 -.066

NEQUIT

Y

-.066 1.000

Sig. (1-tailed) May . .267

NEQUIT

Y

.267 .

Jun NEQUITY

Pearson Correlation Jun 1.000 -.067

NEQUIT

Y

-.067 1.000

Sig. (1-tailed) Jun . .265

NEQUIT

Y

.265 .

Jul NEQUITY

Pearson Correlation Jul 1.000 -.080

NEQUIT

Y

-.080 1.000

Sig. (1-tailed) Jul . .227

NEQUIT

Y

.227 .

Aug NEQUITY

Pearson Correlation Aug 1.000 -.079

NEQUIT

Y

-.079 1.000

Sig. (1-tailed) Aug . .230

NEQUIT

Y

.230 .

Sep NEQUITY

197

Pearson Correlation Sep 1.000 -.082

NEQUIT

Y

-.082 1.000

Sig. (1-tailed) Sep . .217

NEQUIT

Y

.217 .


Correlations

Oct NEQUITY

Pearson Correlation Oct 1.000 -.076

NEQUIT

Y

-.076 1.000

Sig. (1-tailed) Oct . .232

NEQUIT

Y

.232 .

Nov NEQUITY

Pearson Correlation Nov 1.000 -.073

NEQUIT

Y

-.073 1.000

Sig. (1-tailed) Nov . .238

NEQUIT

Y

.238 .

Dec NEQUITY

Pearson Correlation Dec 1.000 -.064

NEQUIT

Y

-.064 1.000

Sig. (1-tailed) Dec . .261

NEQUIT

Y

.261 .

P_yearly NEQUITY

Pearson Correlation P_yearly 1.000 -.067

NEQUIT

Y

-.067 1.000

Sig. (1-tailed) P_yearly . .176

NEQUIT

Y

.176 .

Model Summary

Model Summaryb

Model R R

Square

Adjusted R

Square

Std. Error of the

Estimate

Durbin-

Watson

Jan .067a .005 -.007 4302.95049 .766

Feb .066a .004 -.007 4247.73093 .820

Mar .067a .004 -.007 3989.42032 .742

Apr .067a .005 -.007 3815.42095 .600

198

May .066a .004 -.007 3843.54969 .638

Jun .067a .004 -.007 3775.32764 .610

Jul .080a .006 -.005 4050.18176 .659

Aug .079a .006 -.005 3784.18677 .596

Sep .082a .007 -.004 3733.72683 .532

Oct .076a .006 -.005 3886.60177 .652

Nov .073a .005 -.005 3970.99520 .567

Dec .064a .004 -.006 4124.44223 .586

yearly .067a .004 .000 9105.34743 1.574

a. Predictors: (Constant), NEQUITY


Anova Test Results

ANOVAb

Model Sum of

Squares

df Mean Square F Sig.

Jan

Regression 7268743.051 1 7268743.051 .393 .533a

Residual 1.592E9 86 1.852E7

Total 1.600E9 87

Feb

Regression 6864970.917 1 6864970.917 .380 .539a

Residual 1.552E9 86 1.804E7

Total 1.559E9 87

Mar

Regression 6155433.770 1 6155433.770 .387 .536a

Residual 1.369E9 86 1.592E7

Total 1.375E9 87

Apr

Regression 5805872.844 1 5805872.844 .399 .529a

Residual 1.281E9 88 1.456E7

Total 1.287E9 89

May

Regression 5735080.005 1 5735080.005 .388 .535a

Residual 1.300E9 88 1.477E7

Total 1.306E9 89

Jun

Regression 5669226.872 1 5669226.872 .398 .530a

Residual 1.254E9 88 1.425E7

Total 1.260E9 89

Jul

Regression 9290256.505 1 9290256.505 .566 .454a

Residual 1.460E9 89 1.640E7

Total 1.469E9 90

Aug

Regression 7914473.120 1 7914473.120 .553 .459a

Residual 1.274E9 89 1.432E7

Total 1.282E9 90

Sep

Regression 8642603.153 1 8642603.153 .620 .433a

Residual 1.269E9 91 1.394E7

Total 1.277E9 92

Oct

Regression 8139221.850 1 8139221.850 .539 .465a

Residual 1.405E9 93 1.511E7

Total 1.413E9 94

Regression 8093885.404 1 8093885.404 .513 .475a

199

Nov Residual 1.498E9 95 1.577E7

Total 1.506E9 96

Dec

Regression 7004597.840 1 7004597.840 .412 .523a

Residual 1.684E9 99 1.701E7

Total 1.691E9 100

Yearly

Regression 7.226E7 1 7.226E7 .872 .352a

Residual 1.608E10 194 8.291E7

Total 1.616E10 195

a. Predictors: (Constant), NEQUITY


H3c-Issue Debt to Repurchase Equity



Mean Std. Deviation

Jan 6826.666667 9.1839548E3

NDEBT .071248 .0655959

NEQUITY -.020687 .0301779

Feb 6771.111111 9.2494111E3

NDEBT .071248 .0655959

NEQUITY -.020687 .0301779

Mar 6340.000000 8.2777952E3

NDEBT .071248 .0655959

NEQUITY -.020687 .0301779

Apr 6107.222222 7.4446869E3

NDEBT .071248 .0655959

NEQUITY -.020687 .0301779

May 5984.444444 7.4346118E3

NDEBT .071248 .0655959

NEQUITY -.020687 .0301779

Jun 5695.000000 6.9278947E3

NDEBT .071248 .0655959

NEQUITY -.020687 .0301779

Jul 6130.000000 7.8145341E3

NDEBT .071248 .0655959

NEQUITY -.020687 .0301779

Aug 5909.444444 7.2999030E3

NDEBT .071248 .0655959

NEQUITY -.020687 .0301779

Sep 5592.222222 6.6337170E3

NDEBT .071248 .0655959

NEQUITY -.020687 .0301779

Oct 4705.000000 5.5191842E3

NDEBT .071248 .0655959

NEQUITY -.020687 .0301779

Nov 5206.111111 6.2086285E3

200

NDEBT .071248 .0655959

NEQUITY -.020687 .0301779

Dec 4867.000000 6.0052825E3

NDEBT .064430 .0654957

NEQUITY -.023205 .0295450

P_yearly 4358.695652 5.7944408E3

NDEBT .163867 .1622490

NEQUITY -.022900 .0567996


Correlations

Jan NDEBT NEQUITY

Pearson

Correlation

Jan 1.000 -.347 .407

NDEBT -.347 1.000 -.825

NEQUITY .407 -.825 1.000

Sig. (1-tailed) Jan . .180 .138

NDEBT .180 . .003

NEQUITY .138 .003 .

Feb NDEBT NEQUITY

Pearson

Correlation

Feb 1.000 -.352 .401

NDEBT -.352 1.000 -.825

NEQUITY .401 -.825 1.000

Sig. (1-tailed) Feb . .176 .142

NDEBT .176 . .003

NEQUITY .142 .003 .

Mar NDEBT NEQUITY

Pearson

Correlation

Mar 1.000 -.351 .442

NDEBT -.351 1.000 -.825

NEQUITY .442 -.825 1.000

Sig. (1-tailed) Mar . .177 .117

NDEBT .177 . .003

NEQUITY .117 .003 .

Apr NDEBT NEQUITY

Pearson

Correlation

Apr 1.000 -.343 .471

NDEBT -.343 1.000 -.825

NEQUITY .471 -.825 1.000

Sig. (1-tailed) Apr . .183 .100

NDEBT .183 . .003

NEQUITY .100 .003 .

May NDEBT NEQUITY

Pearson

Correlation

May 1.000 -.343 .441

NDEBT -.343 1.000 -.825

NEQUITY .441 -.825 1.000

Sig. (1-tailed) May . .183 .118

NDEBT .183 . .003

NEQUITY .118 .003 .

Jun NDEBT NEQUITY

201

Pearson

Correlation

Jun 1.000 -.328 .432

NDEBT -.328 1.000 -.825

NEQUITY .432 -.825 1.000

Sig. (1-tailed) Jun . .195 .123

NDEBT .195 . .003

NEQUITY .123 .003 .


Jul NDEBT NEQUIT

Y

Pearson

Correlation

Jul 1.000 -.330 .405

NDEBT -.330 1.000 -.825

NEQUITY .405 -.825 1.000

Sig. (1-tailed) Jul . .193 .140

NDEBT .193 . .003

NEQUITY .140 .003 .

Aug NDEBT NEQUITY

Pearson

Correlation

Aug 1.000 -.311 .414

NDEBT -.311 1.000 -.825

NEQUITY .414 -.825 1.000

Sig. (1-tailed) Aug . .207 .134

NDEBT .207 . .003

NEQUITY .134 .003 .

Sep NDEBT NEQUITY

Pearson

Correlation

Sep 1.000 -.297 .447

NDEBT -.297 1.000 -.825

NEQUITY .447 -.825 1.000

Sig. (1-tailed) Sep . .219 .114

NDEBT .219 . .003

NEQUITY .114 .003 .

Oct NDEBT NEQUITY

Pearson

Correlation

Oct 1.000 -.213 .448

NDEBT -.213 1.000 -.825

NEQUITY .448 -.825 1.000

Sig. (1-tailed) Oct . .291 .113

NDEBT .291 . .003

NEQUITY .113 .003 .

Nov NDEBT NEQUITY

Pearson

Correlation

Nov 1.000 -.193 .440

NDEBT -.193 1.000 -.825

NEQUITY .440 -.825 1.000

Sig. (1-tailed) Nov . .310 .118

NDEBT .310 . .003

NEQUITY .118 .003 .

Dec NDEBT NEQUITY

Pearson

Correlation

Dec 1.000 -.111 .486

NDEBT -.111 1.000 -.662

202

NEQUITY .486 -.662 1.000

Sig. (1-tailed) Dec . .380 .077

NDEBT .380 . .019

NEQUITY .077 .019 .

P_yearly NDEBT NEQUITY

Pearson

Correlation

P_yearly 1.000 -.292 .223

NDEBT -.292 1.000 -.041

NEQUITY .223 -.041 1.000

Sig. (1-tailed) P_yearly . .088 .154

NDEBT .088 . .426

NEQUITY .154 .426 .

Anova Test Results

ANOVAb

Model Sum of

Squares

df Mean Square F Sig.

Jan Regression 1.120E8 2 5.602E7 .597 .580a

Residual 5.627E8 6 9.379E7

Total 6.748E8 8

Feb Regression 1.111E8 2 5.554E7 .581 .588a

Residual 5.733E8 6 9.556E7

Total 6.844E8 8

Mar Regression 1.076E8 2 5.378E7 .732 .519a

Residual 4.406E8 6 7.344E7

Total 5.482E8 8

Apr Regression 1.013E8 2 5.067E7 .889 .459a

Residual 3.420E8 6 5.701E7

Total 4.434E8 8

May Regression 8.636E7 2 4.318E7 .728 .521a

Residual 3.558E8 6 5.930E7

Total 4.422E8 8

Jun Regression 7.276E7 2 3.638E7 .701 .532a

Residual 3.112E8 6 5.187E7

Total 3.840E8 8

Jul Regression 8.005E7 2 4.003E7 .588 .585a

Residual 4.085E8 6 6.808E7

Total 4.885E8 8

Aug Regression 7.434E7 2 3.717E7 .634 .563a

Residual 3.520E8 6 5.866E7

Total 4.263E8 8

Sep Regression 7.588E7 2 3.794E7 .824 .483a

Residual 2.762E8 6 4.603E7

Total 3.520E8 8

203

Anova Test Results

ANOVAb

Oct Regression 6.774E7 2 3.387E7 1.155 .376a

Residual 1.760E8 6 2.933E7

Total 2.437E8 8

Nov Regression 8.782E7 2 4.391E7 1.195 .366a

Residual 2.206E8 6 3.676E7

Total 3.084E8 8

Dec Regression 1.020E8 2 5.100E7 1.604 .267a

Residual 2.226E8 7 3.180E7

Total 3.246E8 9

yearly Regression 9.586E7 2 4.793E7 1.491 .249a

Residual 6.428E8 20 3.214E7

Total 7.387E8 22

a. Predictors: (Constant), NEQUITY, NDEBT


Model Summary

Model Summaryb


Square

Std. Error of the

Estimate

Durbin-

Watson

Jan .407a .166 -.112 9.6842906E3 .987

Feb .403a .162 -.117 9.7752614E3 1.049

Mar .443a .196 -.072 8.5694609E3 .890

Apr .478a .229 -.029 7.5502543E3 .684

May .442a .195 -.073 7.7009209E3 .813

Jun .435a .189 -.081 7.2019407E3 .762

Jul .405a .164 -.115 8.2510944E3 .868

Aug .418a .174 -.101 7.6591030E3 .754

Sep .464a .216 -.046 6.7843834E3 .598

Oct .527a .278 .037 5.4152935E3 .806

Nov .534a .285 .046 6.0629603E3 .800

Dec .561a .314 .118 5.6388036E3 .888

yearly .360a .130 .043 5.6692401E3 1.577

a. Predictors: (Constant), REPOEQUITY, NDEBT



Growth and Mature Firms


Sum Mean Sum Mean

LTL_G 46.10 0.3136 LTL_M 20.49 0.2029

FIXAS_G 68.56 0.4664 FIXAS_M 28.43 0.2815

204

DIV_G 0.39 0.0026 DIV_M 5.59 0.0554

dWC_G 8.60 0.0637 dWC_M 9.76 0.1016

CF_G 4.07 0.0277 CF_M 10.07 0.0997

FD_G 74.52 0.5520 FD_M 35.35 0.3682

FDSQR_G 63.10 0.4674 FDSQR_M 30.33 0.3159

NRE_G 0.99 0.0075 NRE_M 5.40 0.0587

NEQUITY_G 8.06 0.0610 NEQUITY_M 1.43 0.0156

NDEBT_G 6.39 0.0484 NDEBT_M 7.36 0.0800

Valid N

(listwise)

Valid N

(listwise)

Descriptive Statistics Test Results

Mean Std.

Deviation

NDEBT_M 0.0800 0.16855

FD_M 0.3606 0.42807

NEQUITY_M 0.0156 0.09798

FD_M 0.3606 0.42807

NDEBT_G 0.0484 0.29963

FD_G 0.5434 0.40539

NEQUITY_G 0.0610 0.16648

FD_G 0.5434 0.40539


NDEBT_M FD_M

Pearson

Correlation

NDEBT_M 1.000 0.383

FD_M 0.383 1.000

Sig. (1-tailed) NDEBT_M . 0.000

FD_M 0.000 .

NEQUITY_M FD_M

Pearson

Correlation

NEQUITY_M 1.000 0.254

FD_M 0.254 1.000

Sig. (1-tailed) NEQUITY_M . 0.007

FD_M 0.007 .

NDEBT_G FD_G

Pearson

Correlation

NDEBT_G 1.000 0.385

FD_G 0.385 1.000

Sig. (1-tailed) NDEBT_G . 0.000

FD_G 0.000 .

NEQUITY_G FD_G

Pearson

Correlation

NEQUITY_G 1.000 0.177

FD_G 0.177 1.000

Sig. (1-tailed) NEQUITY_G . 0.021

FD_G 0.021 .

205

Model Summary

Model R R

Square

Adjusted

R Square

Std. Error of

the Estimate

Durbin-

Watson

NDEBT_M 0.383a 0.147 0.137 0.15657 1.602

NEQUITY_M 0.254a 0.064 0.054 0.09529 2.284

NDEBT_G 0.385a 0.148 0.141 0.27766 1.670

NEQUITY_G 0.177a 0.031 0.024 0.16448 2.108

a. Predictors: (Constant), FD_G

Model Summary

Model R R

Square

Adjusted

R Square

Std. Error of

the Estimate

Durbin-

Watson

NDEBT_M 0.495a 0.245 0.229 0.14804 1.486

NDEBT_G 0.533a 0.285 0.273 0.25540 1.822

a. Predictors: (Constant), FDSQR_G, FD_G and M

b. Dependent Variable: NDEBT_G and M

Anova Test Results

Model Sum of

Squares

df Mean

Square

F Sig.

NDEBT_M Regression 0.379 1 0.379 15.463 0.000a

Residual 2.206 90 0.025

Total 2.585 91

NEQUITY_M Regression 0.056 1 0.056 6.196 0.015a

Residual 0.817 90 0.009

Total 0.874 91

NDEBT_G Regression 1.739 1 1.739 22.556 0.000a

Residual 10.022 130 0.077

Total 11.761 131

NEQUITY_G Regression .114 1 0.114 4.219 0.042a

Residual 3.517 130 0.027

Total 3.631 131

a. Predictors: (Constant), FD_G

206

APPENDIX B

List of Acronyms

Table B.1. Name of Firms

Name of Firms Acronyms

ASII Astra International

AUTO Astra Otoparts

ADMG Polychem Indonesia

BRPT Barito Pacific

BUDI Budi Acid Jaya

CPIN Charoen Pokphand Indonesia

DNKS Dankos Laboratories

FASW Fajar Surya Wisesa

GGRM Gudang Garam

GJTL Gajah Tunggal

HMSP Hanjaya Mandala Sampoerna

INAF Indofarma

INDF Indocement Tunggal Prakasa

INDR Indorama Synthetics

INKP Indah Kiat Pulp and Paper

INTP Indocement Tunggal Prakasa

KAEF Kimia Farma

KLBF Kalbe Farma

KOMI Komatsu Indonesia

RMBA Bentoel International Investama

SMCB Holcim Indonesia

SMGR Semen Gresik (Persero)

TKIM Pabrik Kertas Tjiwi Kimia

TSPC Tempo Scan Pacific

UNVR Unilever Indonesia

SULI Sumalindo Lestari Jaya

Table B.2. Variables and Its Sub-variables of Research

Variables of Research Acronyms

Aug August‟ stock price

Apr April‟ stock price

CAPEX Capital expenditures

CF Operating cash flow (after interest and taxes)

CF_M Cash flow of mature firm

CF_G Cash flow of growth firm

207

DIV Dividend payments

DIV_G Dividend payments of growth firm

DIV_M Dividend payments of mature firm

DC Domestic corporation

Dec December‟ stock price

dWC_G Change in working capital of growth firm

dWC_M Change in working capital of mature firm

dWorking capital The net change in working capital

dTA Change in total asset

dRE Change in retained earning

dEq Change in book equity

∆ Fixed Assets Change in fixed assets

∆ Working Capital Change in working capital

∆ Long Term Debt Change in long term debt

ΔTD Change in total debt (long term plus short term)

Feb February‟ stock price

FD Financing deficit

FDSQR Financing deficit square

FD_G Financing deficit of growth firm

FDSQR_G Financing deficit square of growth firm

FD_M Financing deficit of mature firm

FDSQR_M Financing deficit square of mature firm

FD_L Financing deficit of large firm

FDSQR_L Financing deficit square of large firm

FD_S Financing deficit of small firm

FDSQR_S Financing deficit square of small firm

FD_O Financing deficit of old firm

FDSQR_O Financing deficit square of old firm

FD_Y Financing deficit of young firm

208

FDSQR_Y Financing deficit square of young firm

FIXAS_G Fixed asset of growth firm

FIXAS_M Fixed asset of mature firm

GCC countries Gulf Cooperation Council (GCC) countries

Growth Growth firm

GROW Growth

Jan January‟ stock price

JM Jensen and Meckling

Jun June‟ stock price

Jul July‟ stock price

LTL_M Long-term leverage of mature firm

LTL_G Long-term leverage of growth firm

LTD payment Long-term debt payment

Large Large firm

LTL Long-term leverage

May May‟ stock price

MRL Market leverage

MM The Modigliani-Miller

MNC Multi National Corporation

Mature Mature firm

Mar March‟ stock price

MV of equity Market value of equity

NDEBT_G Net debt issue of growth firm

NEQUITY_G Net equity issue of growth firm

NDEBT_M Net debt issue of mature firm

NEQUITY_M Net equity issue of mature firm

209

NRE_G Newly retained earning of growth firm

NRE_M Newly retained earning of mature firm

NDEBT_L Net debt issue of large firm

NEQUITY_L Net equity issue of large firm

NRE_L Newly retained earning of large firm

NDEBT_S Net debt issue of small firm

NEQUITY_S Net equity issue of small firm

NRE_S Newly retained earning of small firm

NDEBT_O Net debt issue of old firm

NEQUITY_O Net equity issue of old firm

NRE_O Newly retained earning of old firm

NDEBT_Y Net debt issue of young firm

NEQUITY_Y Net equity issue of young firm

NRE_Y Newly retained earning of young firm

Net debtit Net debt issued in period t scaled by total assets

at the beginning of period t (assett-1)

Net debtt Long-term debt issuance at t minus long-term

debt reduction at t divided by total assets at t-1.

NPV Net present value

Nov November‟ stock price

NDEBT Net debt issue

NEQUITY Net equity issue

NRE Newly retained earning

Oct October‟ stock price

Old Old firm

P_Yearly Yearly stock price

PRFT Profitability

POT Pecking order theory

210

REPO EQUITY_G Repurchase equity of growth firm

REPO EQUITY_L Repurchase equity of large firm

REPO EQUITY_S Repurchase equity of small firm

REPO EQUITY_Y Repurchase equity of young firm

REPO EQUITY_O Repurchase equity of old firm

REPO EQUITY_M Repurchase equity of mature firm

RISK Risk

ROA Return on Asset

REPO EQUITY Repurchase equity

Sep September‟ stock price

SIZE Firm‟s size

STL Short-term leverage

Small Small firm

TANG Asset tangibility

Tobin‟s Q Proxy of future growth opportunities

TE Total equity

TLV Total leverage

Young Young firm

Table B.3. Variables of Capital Market

Variables of Capital

Market

Acronyms

BAPEPAM Badan Pengawas Pasar Modal (Capital Market

Supervisory Agency)

Bapepam-LK Badan Pengawas Pasar Modal (Capital Market

Supervisory Agency) – Lembaga Keuangan

BEI Bursa Efek Indonesia

BISNIS-27 Business 27

CPI Consumer Price Index

CSPI Composite Stock Price Index

DBX Development Board Index

GDP Gross Domestic Product

211

ICT Information and communication technology

IDR Indonesian Rupiah

IDX Indonesia Stock Exchange

IPO Initial Public Offering

JATS Jakarta Automatic Trading System

JSX Jakarta Stock Exchange

Kompas 100 Index consists of 100 shares of Listed Companies

that are selected based on considerations of liquidity

and market capitalisation

LQ45 Index Liquid 45 Index

MBX Main Board Index

MUI The Majelis Ulama Indonesia (the Sharia

Supervisory Board)

No.Kep-86/PM/1996 Nomor Keputusan-86/Pasar Modal/1996

PEFINDO-25 Pemeringkat Efek Indonesia 25 (rating agencies)

PT Perusahaan terbatas

KEHATI Sustainable Responsible Investment-Indonesian

Biodiversity Foundation

SME Small Medium Enterprises

TBK Terbuka

The BNDES The state-owned development bank

USD U.S. Dollar

U.S. United States

YOY Year on year

Table B.4. Variables in Statistics

Statistics

Acronyms

Adjusted R-squared Adjusted R Squared is designed to more closely

reflect how well the model fits the population and is

usually of interest for models with more than one

predictor.

ANOVA Analysis of Variance

B Unstandardised Coefficients

Beta Standardised Coefficients Beta

212

DW Durbin Watson Test of autocorrelation

F-statistic The t test results of two groups to three or more

groups

H1, H2, H3, and H4 Hypothesis 1, Hypothesis 2, Hypothesis 3, and

Hypothesis 4

OLS regressions Ordinary least square regressions

R-squared The Coefficient of Determination = its value is always

between 0 and 1, and interpreted as the percentage of

variation of the response variables explained by the

regression line.

R The multiple correlation coefficients are the linear

correlation between the model-predicted and the

observed values of the dependent variable.

Normal P-P plot The histogram gave the normally pattern of

distribution

N Number of observation

QUAN Quantitative

QUAL Qualitative

Sig. Significance level

SPSS Statistical Package for Social Science

Std. Deviation Standard deviation

Std. Error of Skewness Standard error of skewness

Std. Error of Kurtosis Standard error of kurtosis

Std. Error Standard error of unstandardised coefficients

T t-value of regression

The p-value Significance level

VIF Variance Inflation Factor

213

APPENDIX C

List of Figures from Regression Results

Figures of Hypothesis 1 Testing Result

214


216


218

Figures of Hypothesis 4Testing Result

220

APPENDIX D

Results of Panel Data Regression Analysis

Result of Hypothesis 1

221

Warning: convergence not achieved; estimates are based on iterated EMNote: LR test is conservative and provided only for reference.

LR test vs. linear regression: chi2(5) = 34.15 Prob > chi2 = 0.0000 sd(Residual) .0356326 . . . sd(GROW) .1207096 . . . sd(RISK) .9367825 . . . sd(SIZE) .0060267 . . . sd(TANG) .2134466 . . . sd(PRFT) .4176825 . . .STL: Independent Random-effects Parameters Estimate Std. Err. [95% Conf. Interval]

_cons .1059268 .1282458 0.83 0.409 -.1454303 .3572839 GROW .1187629 .0435091 2.73 0.006 .0334865 .2040392 RISK .7094852 .281901 2.52 0.012 .1569695 1.262001 SIZE .0142148 .0094468 1.50 0.132 -.0043007 .0327303 TANG -.2436425 .0750988 -3.24 0.001 -.3908334 -.0964515 PRFT -.3022391 .1162004 -2.60 0.009 -.5299878 -.0744904 STL Coef. Std. Err. z P>|z| [95% Conf. Interval]

Log restricted-likelihood = 26.566101 Prob > chi2 = 0.0000 Wald chi2(5) = 40.82

max = 2 avg = 1.0 Obs per group: min = 1

Group variable: STL Number of groups = 195Mixed-effects REML regression Number of obs = 196

Hessian has become unstable or asymmetricflat or discontinuous region encounterednumerical derivatives are approximateIteration 8: log restricted-likelihood = 29.333856 (not concave)Iteration 7: log restricted-likelihood = 29.327564 (not concave)Iteration 6: log restricted-likelihood = 29.323711 (not concave)Iteration 5: log restricted-likelihood = 29.319962 (not concave)Iteration 4: log restricted-likelihood = 29.311165 (not concave)Iteration 3: log restricted-likelihood = 29.283631 (not concave)Iteration 2: log restricted-likelihood = 29.150288 (not concave)Iteration 1: log restricted-likelihood = 28.718764 Iteration 0: log restricted-likelihood = 26.566101

Performing gradient-based optimization:

Performing EM optimization:

> near. xtmixed STL PRFT TANG SIZE RISK GROW, || STL: PRFT TANG SIZE RISK GROW, noconstant covariance(independent) colli

222


LR test vs. linear regression: chi2(5) = 89.72 Prob > chi2 = 0.0000 sd(Residual) .0268583 . . . sd(GROW) .1150654 . . . sd(RISK) .6095126 . . . sd(SIZE) .0048396 . . . sd(TANG) .2169043 . . . sd(PRFT) .2605002 . . .LTL: Independent Random-effects Parameters Estimate Std. Err. [95% Conf. Interval]

_cons .0344509 .0505651 0.68 0.496 -.064655 .1335567 GROW .1835823 .0351526 5.22 0.000 .1146845 .2524801 RISK -.0285189 .2173788 -0.13 0.896 -.4545735 .3975357 SIZE -.0016283 .0043934 -0.37 0.711 -.0102392 .0069826 TANG .3714454 .0607135 6.12 0.000 .2524491 .4904417 PRFT -.2743938 .0857723 -3.20 0.001 -.4425045 -.1062831 LTL Coef. Std. Err. z P>|z| [95% Conf. Interval]



Group variable: LTL Number of groups = 180Mixed-effects REML regression Number of obs = 196

Hessian has become unstable or asymmetricnearby values are missingnumerical derivatives are approximateIteration 2: log restricted-likelihood = 80.472139 Iteration 1: log restricted-likelihood = 78.338489 (not concave)Iteration 0: log restricted-likelihood = 74.323102



> near. xtmixed LTL PRFT TANG SIZE RISK GROW, || LTL: PRFT TANG SIZE RISK GROW, noconstant covariance(independent) colli

223


LR test vs. linear regression: chi2(5) = 32.60 Prob > chi2 = 0.0000 sd(Residual) .0223941 . . . sd(GROW) .1181186 . . . sd(RISK) .4925419 . . . sd(SIZE) .003591 . . . sd(TANG) .0908822 . . . sd(PRFT) .28782 . . .TL: Independent Random-effects Parameters Estimate Std. Err. [95% Conf. Interval]

_cons .184592 .0878415 2.10 0.036 .0124259 .3567581 GROW .3872779 .0347176 11.16 0.000 .3192326 .4553232 RISK .3493213 .1850803 1.89 0.059 -.0134295 .7120721 SIZE .0061865 .0066917 0.92 0.355 -.0069291 .019302 TANG .1316364 .0496431 2.65 0.008 .0343377 .2289351 PRFT -.5917751 .0813442 -7.27 0.000 -.7512069 -.4323434 TL Coef. Std. Err. z P>|z| [95% Conf. Interval]



Group variable: TL Number of groups = 194Mixed-effects REML regression Number of obs = 196

Hessian has become unstable or asymmetricflat or discontinuous region encounterednumerical derivatives are approximatenearby values are missingnumerical derivatives are approximatenearby values are missingnumerical derivatives are approximateIteration 7: log restricted-likelihood = 104.58068 Iteration 6: log restricted-likelihood = 104.58063 Iteration 5: log restricted-likelihood = 104.58043 Iteration 4: log restricted-likelihood = 104.57945 Iteration 3: log restricted-likelihood = 104.57578 Iteration 2: log restricted-likelihood = 104.55367 Iteration 1: log restricted-likelihood = 104.49277 Iteration 0: log restricted-likelihood = 97.639783



> ar. xtmixed TL PRFT TANG SIZE RISK GROW, || TL: PRFT TANG SIZE RISK GROW, noconstant covariance(independent) colline

224

.


LR test vs. linear regression: chi2(5) = 79.41 Prob > chi2 = 0.0000 sd(Residual) .0193811 . . . sd(GROW) .0924701 . . . sd(RISK) .3999447 . . . sd(SIZE) .0045323 . . . sd(TANG) .0873281 . . . sd(PRFT) .3232137 . . .MRLV: Independent Random-effects Parameters Estimate Std. Err. [95% Conf. Interval]

_cons 1.042339 .0500458 20.83 0.000 .9442508 1.140427 GROW -.3065398 .0297983 -10.29 0.000 -.3649434 -.2481362 RISK .1051473 .153619 0.68 0.494 -.1959404 .406235 SIZE .0003319 .004036 0.08 0.934 -.0075786 .0082423 TANG .0852128 .0412762 2.06 0.039 .0043129 .1661127 PRFT -.6887384 .0763719 -9.02 0.000 -.8384245 -.5390523 MRLV Coef. Std. Err. z P>|z| [95% Conf. Interval]



Group variable: MRLV Number of groups = 169Mixed-effects REML regression Number of obs = 196

Hessian has become unstable or asymmetricnearby values are missingnumerical derivatives are approximatenearby values are missingnumerical derivatives are approximateIteration 1: log restricted-likelihood = 145.03256 (not concave)Iteration 0: log restricted-likelihood = 142.16087 (not concave)



> linear. xtmixed MRLV PRFT TANG SIZE RISK GROW, || MRLV: PRFT TANG SIZE RISK GROW, noconstant covariance(independent) col

225


Warning: convergence not achieved; estimates are based on iterated EM

LR test vs. linear regression: chibar2(01) = 0.00 Prob >= chibar2 = 1.0000 sd(Residual) .0525549 . . . sd(FD) .1291078 . . .NDEBT: Identity Random-effects Parameters Estimate Std. Err. [95% Conf. Interval]

_cons .0217334 .0194652 1.12 0.264 -.0164178 .0598845 FD .2377811 .0430574 5.52 0.000 .1533902 .322172 NDEBT Coef. Std. Err. z P>|z| [95% Conf. Interval]



Group variable: NDEBT Number of groups = 52Mixed-effects REML regression Number of obs = 53

missing values encounteredcould not calculate numerical derivativesIteration 3: log restricted-likelihood = 50.64306 flat or discontinuous region encounterednumerical derivatives are approximateIteration 2: log restricted-likelihood = 50.64306 flat or discontinuous region encounterednumerical derivatives are approximateIteration 1: log restricted-likelihood = 50.64306 flat or discontinuous region encounterednumerical derivatives are approximateIteration 0: log restricted-likelihood = 49.945592



Note: single-variable random-effects specification; covariance structure set to identity. xtmixed NDEBT FD, || NDEBT: FD, noconstant covariance(independent) collinear

226

LR test vs. linear regression: chibar2(01) = 1138.23 Prob >= chibar2 = 0.0000 sd(Residual) 1.6e-116 . . . sd(FD) .2502 .0247735 .2060658 .3037867NEQUITY: Identity Random-effects Parameters Estimate Std. Err. [95% Conf. Interval]

_cons -8.8e-215 1.3e-116 -0.00 1.000 -2.6e-116 2.6e-116 FD (dropped) NEQUITY Coef. Std. Err. z P>|z| [95% Conf. Interval]

Log restricted-likelihood = 602.34541 Prob > chi2 = . Wald chi2(0) = .


Group variable: NEQUITY Number of groups = 49Mixed-effects REML regression Number of obs = 53

Computing standard errors:

Iteration 2: log restricted-likelihood = 602.34541 (not concave)Iteration 1: log restricted-likelihood = 602.34541 Iteration 0: log restricted-likelihood = 46.0107



Note: single-variable random-effects specification; covariance structure set to identity. xtmixed NEQUITY FD, || NEQUITY: FD, noconstant covariance(independent) collinear

227


LR test vs. linear regression: chibar2(01) = 0.00 Prob >= chibar2 = 1.0000 sd(Residual) .0514687 . . . sd(FD) .0441781 . . .NRE: Identity Random-effects Parameters Estimate Std. Err. [95% Conf. Interval]

_cons .0988427 .0152893 6.46 0.000 .0688762 .1288093 FD -.062957 .027202 -2.31 0.021 -.1162719 -.0096421 NRE Coef. Std. Err. z P>|z| [95% Conf. Interval]



Group variable: NRE Number of groups = 53Mixed-effects REML regression Number of obs = 53

missing values encounteredcould not calculate numerical derivativesIteration 6: log restricted-likelihood = 71.826867 flat or discontinuous region encounterednumerical derivatives are approximateIteration 5: log restricted-likelihood = 71.826867 flat or discontinuous region encounterednumerical derivatives are approximateIteration 4: log restricted-likelihood = 71.826867 flat or discontinuous region encounterednumerical derivatives are approximateIteration 3: log restricted-likelihood = 71.826867 flat or discontinuous region encounterednumerical derivatives are approximateIteration 2: log restricted-likelihood = 71.826867 flat or discontinuous region encounterednumerical derivatives are approximateIteration 1: log restricted-likelihood = 71.826867 flat or discontinuous region encounterednumerical derivatives are approximateIteration 0: log restricted-likelihood = 70.209966



Note: single-variable random-effects specification; covariance structure set to identity. xtmixed NRE FD, || NRE: FD, noconstant covariance(independent) collinear

228

Note: LR test is conservative and provided only for reference.

LR test vs. linear regression: chi2(2) = 1.38 Prob > chi2 = 0.5009 sd(Residual) .0648553 .0178199 .03785 .1111284 sd(FD) .0989246 .0452294 .0403762 .2423724 sd(FDSQR) .0000439 .0322742 0 .NDEBT: Independent Random-effects Parameters Estimate Std. Err. [95% Conf. Interval]

_cons .0306117 .0269366 1.14 0.256 -.0221831 .0834065 FD .1784661 .0890874 2.00 0.045 .003858 .3530743 FDSQR .0639377 .0679679 0.94 0.347 -.0692769 .1971522 NDEBT Coef. Std. Err. z P>|z| [95% Conf. Interval]



Group variable: NDEBT Number of groups = 52Mixed-effects REML regression Number of obs = 53


Iteration 10: log restricted-likelihood = 49.139212 Iteration 9: log restricted-likelihood = 49.13921 Iteration 8: log restricted-likelihood = 49.139204 Iteration 7: log restricted-likelihood = 49.139179 Iteration 6: log restricted-likelihood = 49.139023 Iteration 5: log restricted-likelihood = 49.138334 Iteration 4: log restricted-likelihood = 49.135126 Iteration 3: log restricted-likelihood = 49.123212 Iteration 2: log restricted-likelihood = 49.047105 Iteration 1: log restricted-likelihood = 48.941722 Iteration 0: log restricted-likelihood = 47.499627



. xtmixed NDEBT FDSQR FD, || NDEBT: FDSQR FD, noconstant covariance(independent) collinear

229

LR test vs. linear regression: chibar2(01) = 26.87 Prob >= chibar2 = 0.0000 sd(Residual) .0005239 .0002134 .0002358 .001164 sd(FD) .0916837 .0141489 .0677536 .1240657REPOEQUITY: Identity Random-effects Parameters Estimate Std. Err. [95% Conf. Interval]

_cons -.0002445 .0003409 -0.72 0.473 -.0009126 .0004235 FD -.0415067 .0195653 -2.12 0.034 -.0798541 -.0031594 REPOEQUITY Coef. Std. Err. z P>|z| [95% Conf. Interval]



Group variable: REPOEQUITY Number of groups = 22Mixed-effects REML regression Number of obs = 26


Iteration 4: log restricted-likelihood = 46.674918 Iteration 3: log restricted-likelihood = 46.674918 Iteration 2: log restricted-likelihood = 46.674867 Iteration 1: log restricted-likelihood = 46.655047 Iteration 0: log restricted-likelihood = 40.549694



Note: single-variable random-effects specification; covariance structure set to identity. xtmixed REPOEQUITY FD, || REPOEQUITY: FD, noconstant covariance(independent) collinear

230



LR test vs. linear regression: chibar2(01) = 0.00 Prob >= chibar2 = 1.0000 sd(Residual) 8761.69 . . . sd(NDEBT) 22661.17 . . .STCKPRICE: Identity Random-effects Parameters Estimate Std. Err. [95% Conf. Interval]

_cons 3711.952 726.2874 5.11 0.000 2288.455 5135.449 NDEBT 2565.906 4151.617 0.62 0.537 -5571.114 10702.93 STCKPRICE Coef. Std. Err. z P>|z| [95% Conf. Interval]

Log restricted-likelihood = -2064.4246 Prob > chi2 = 0.5365 Wald chi2(1) = 0.38


Group variable: STCKPRICE Number of groups = 146Mixed-effects REML regression Number of obs = 196

missing values encounteredcould not calculate numerical derivativesIteration 2: log restricted-likelihood = -2048.2052 flat or discontinuous region encounterednumerical derivatives are approximateIteration 1: log restricted-likelihood = -2048.2052 flat or discontinuous region encounterednumerical derivatives are approximateIteration 0: log restricted-likelihood = -2064.4246



Note: single-variable random-effects specification; covariance structure set to identity. xtmixed STCKPRICE NDEBT, || STCKPRICE: NDEBT, noconstant covariance(independent) collinear

231

.


LR test vs. linear regression: chibar2(01) = 0.00 Prob >= chibar2 = 1.0000 sd(Residual) 9070.635 . . . sd(NEQUITY) 26337.34 . . .STCKPRICE: Identity Random-effects Parameters Estimate Std. Err. [95% Conf. Interval]

_cons 4017.316 713.2208 5.63 0.000 2619.429 5415.203 NEQUITY -7757.124 7958.142 -0.97 0.330 -23354.8 7840.548 STCKPRICE Coef. Std. Err. z P>|z| [95% Conf. Interval]




missing values encounteredcould not calculate numerical derivativesIteration 3: log restricted-likelihood = -2047.1948 flat or discontinuous region encounterednumerical derivatives are approximateIteration 2: log restricted-likelihood = -2047.1948 flat or discontinuous region encounterednumerical derivatives are approximateIteration 1: log restricted-likelihood = -2047.1948 flat or discontinuous region encounterednumerical derivatives are approximateIteration 0: log restricted-likelihood = -2054.8936



Note: single-variable random-effects specification; covariance structure set to identity. xtmixed STCKPRICE NEQUITY, || STCKPRICE: NEQUITY, noconstant covariance(independent) collinear

232


LR test vs. linear regression: chibar2(01) = 0.00 Prob >= chibar2 = 1.0000 sd(Residual) 5697.896 . . . sd(REPOEQ~Y) 90214.21 . . .STCKPRICE: Identity Random-effects Parameters Estimate Std. Err. [95% Conf. Interval]

_cons 5235.905 1377.997 3.80 0.000 2535.08 7936.73 REPOEQUITY 57057.42 62830.01 0.91 0.364 -66087.13 180202 STCKPRICE Coef. Std. Err. z P>|z| [95% Conf. Interval]




missing values encounteredcould not calculate numerical derivativesIteration 3: log restricted-likelihood = -211.95536 nearby values are missingnumerical derivatives are approximateIteration 2: log restricted-likelihood = -211.95536 flat or discontinuous region encounterednumerical derivatives are approximateIteration 1: log restricted-likelihood = -211.95536 Iteration 0: log restricted-likelihood = -213.03597



Note: single-variable random-effects specification; covariance structure set to identity. xtmixed STCKPRICE REPOEQUITY, || STCKPRICE: REPOEQUITY, noconstant covariance(independent) collinear

233

Result of Hypothesis 4-Mature Firms

LR test vs. linear regression: chibar2(01) = 29.28 Prob >= chibar2 = 0.0000 sd(Residual) .1052617 .0120024 .0841808 .1316218 sd(FD_M) .1908582 .0482506 .116284 .3132577NDEBT_M: Identity Random-effects Parameters Estimate Std. Err. [95% Conf. Interval]

_cons -.0166008 .0200766 -0.83 0.408 -.0559502 .0227487 FD_M .3225679 .0640241 5.04 0.000 .197083 .4480528 NDEBT_M Coef. Std. Err. z P>|z| [95% Conf. Interval]



Group variable: NDEBT_M Number of groups = 71Mixed-effects REML regression Number of obs = 71


Iteration 3: log restricted-likelihood = 40.962361 Iteration 2: log restricted-likelihood = 40.962361 Iteration 1: log restricted-likelihood = 40.960056 Iteration 0: log restricted-likelihood = 40.648661



Note: single-variable random-effects specification; covariance structure set to identity. xtmixed NDEBT_M FD_M, || NDEBT_M: FD_M, noconstant covariance(independent) collinear

234

LR test vs. linear regression: chibar2(01) = 0.01 Prob >= chibar2 = 0.4578 sd(Residual) .0960272 .008422 .0808613 .1140376 sd(FD_M) .0124699 .0616314 7.74e-07 200.8429NEQUITY_M: Identity Random-effects Parameters Estimate Std. Err. [95% Conf. Interval]

_cons -.0065849 .0144804 -0.45 0.649 -.0349658 .0217961 FD_M .0551153 .0257855 2.14 0.033 .0045767 .1056539 NEQUITY_M Coef. Std. Err. z P>|z| [95% Conf. Interval]



Group variable: NEQUITY_M Number of groups = 49Mixed-effects REML regression Number of obs = 71


Iteration 4: log restricted-likelihood = 60.136437 Iteration 3: log restricted-likelihood = 60.136437 Iteration 2: log restricted-likelihood = 60.136413 Iteration 1: log restricted-likelihood = 60.134653 Iteration 0: log restricted-likelihood = 56.764696



Note: single-variable random-effects specification; covariance structure set to identity. xtmixed NEQUITY_M FD_M, || NEQUITY_M: FD_M, noconstant covariance(independent) collinear

235


LR test vs. linear regression: chibar2(01) = 0.00 Prob >= chibar2 = 1.0000 sd(Residual) .097344 . . . sd(FD_M) .1150813 . . .NRE_M: Identity Random-effects Parameters Estimate Std. Err. [95% Conf. Interval]

_cons .0921292 .0173153 5.32 0.000 .0581919 .1260666 FD_M -.0970227 .0480552 -2.02 0.043 -.1912091 -.0028363 NRE_M Coef. Std. Err. z P>|z| [95% Conf. Interval]



Group variable: NRE_M Number of groups = 70Mixed-effects REML regression Number of obs = 71

missing values encounteredcould not calculate numerical derivativesIteration 4: log restricted-likelihood = 54.786413 flat or discontinuous region encounterednumerical derivatives are approximateIteration 3: log restricted-likelihood = 54.786413 flat or discontinuous region encounterednumerical derivatives are approximateIteration 2: log restricted-likelihood = 54.786413 flat or discontinuous region encounterednumerical derivatives are approximateIteration 1: log restricted-likelihood = 54.786413 flat or discontinuous region encounterednumerical derivatives are approximateIteration 0: log restricted-likelihood = 52.056684



Note: single-variable random-effects specification; covariance structure set to identity. xtmixed NRE_M FD_M, || NRE_M: FD_M, noconstant covariance(independent) collinear

236

.


LR test vs. linear regression: chi2(2) = 15.55 Prob > chi2 = 0.0004 sd(Residual) .1151912 .0106356 .0961231 .1380419 sd(FD_M) .0001066 .0772917 0 . sd(FDSQR_M) .1107299 .0542753 .0423682 .289394NDEBT_M: Independent Random-effects Parameters Estimate Std. Err. [95% Conf. Interval]

_cons -.0341197 .0236224 -1.44 0.149 -.0804187 .0121793 FD_M .4545407 .1046305 4.34 0.000 .2494686 .6596127 FDSQR_M -.0865629 .0911667 -0.95 0.342 -.2652464 .0921205 NDEBT_M Coef. Std. Err. z P>|z| [95% Conf. Interval]



Group variable: NDEBT_M Number of groups = 71Mixed-effects REML regression Number of obs = 71


Iteration 10: log restricted-likelihood = 41.421959 Iteration 9: log restricted-likelihood = 41.421958 Iteration 8: log restricted-likelihood = 41.421947 Iteration 7: log restricted-likelihood = 41.421882 Iteration 6: log restricted-likelihood = 41.421585 Iteration 5: log restricted-likelihood = 41.420292 Iteration 4: log restricted-likelihood = 41.415146 Iteration 3: log restricted-likelihood = 41.398807 Iteration 2: log restricted-likelihood = 41.259676 Iteration 1: log restricted-likelihood = 41.169184 (not concave)Iteration 0: log restricted-likelihood = 38.445682



. xtmixed NDEBT_M FDSQR_M FD_M, || NDEBT_M: FDSQR_M FD_M, noconstant covariance(independent) collinear

237

Result of Hypothesis 4-Growth Firms

LR test vs. linear regression: chibar2(01) = 37.69 Prob >= chibar2 = 0.0000 sd(Residual) .1804201 .0160204 .151601 .2147175 sd(FD_G) .2548314 .0341446 .1959754 .3313634NDEBT_G: Identity Random-effects Parameters Estimate Std. Err. [95% Conf. Interval]

_cons -.0918211 .0281735 -3.26 0.001 -.1470402 -.036602 FD_G .3107493 .0531525 5.85 0.000 .2065724 .4149263 NDEBT_G Coef. Std. Err. z P>|z| [95% Conf. Interval]



Group variable: NDEBT_G Number of groups = 152Mixed-effects REML regression Number of obs = 153


Iteration 4: log restricted-likelihood = 1.0795205 Iteration 3: log restricted-likelihood = 1.0795205 Iteration 2: log restricted-likelihood = 1.0795059 Iteration 1: log restricted-likelihood = 1.0781159 Iteration 0: log restricted-likelihood = .83363296 (not concave)



Note: single-variable random-effects specification; covariance structure set to identity. xtmixed NDEBT_G FD_G, || NDEBT_G: FD_G, noconstant covariance(independent) collinear

238

LR test vs. linear regression: chibar2(01) = 55.25 Prob >= chibar2 = 0.0000 sd(Residual) .0911003 .0084938 .0758853 .1093659 sd(FD_G) .1949145 .024343 .1525938 .2489726NEQUITY_G: Identity Random-effects Parameters Estimate Std. Err. [95% Conf. Interval]

_cons .0143126 .0145739 0.98 0.326 -.0142518 .042877 FD_G .0762942 .0327744 2.33 0.020 .0120576 .1405308 NEQUITY_G Coef. Std. Err. z P>|z| [95% Conf. Interval]



Group variable: NEQUITY_G Number of groups = 109Mixed-effects REML regression Number of obs = 153


Iteration 2: log restricted-likelihood = 89.238384 Iteration 1: log restricted-likelihood = 89.238383 Iteration 0: log restricted-likelihood = 89.232193



Note: single-variable random-effects specification; covariance structure set to identity. xtmixed NEQUITY_G FD_G, || NEQUITY_G: FD_G, noconstant covariance(independent) collinear

239

LR test vs. linear regression: chibar2(01) = 38.91 Prob >= chibar2 = 0.0000 sd(Residual) .0888251 .0065607 .0768538 .1026611 sd(FD_G) .0961413 .0133906 .0731736 .126318NRE_G: Identity Random-effects Parameters Estimate Std. Err. [95% Conf. Interval]

_cons .0501018 .0133079 3.76 0.000 .0240188 .0761849 FD_G -.0673261 .0237537 -2.83 0.005 -.1138825 -.0207696 NRE_G Coef. Std. Err. z P>|z| [95% Conf. Interval]



Group variable: NRE_G Number of groups = 150Mixed-effects REML regression Number of obs = 153


Iteration 3: log restricted-likelihood = 120.65754 Iteration 2: log restricted-likelihood = 120.65754 Iteration 1: log restricted-likelihood = 120.65744 Iteration 0: log restricted-likelihood = 120.43421



Note: single-variable random-effects specification; covariance structure set to identity. xtmixed NRE_G FD_G, || NRE_G: FD_G, noconstant covariance(independent) collinear

240


LR test vs. linear regression: chi2(2) = 47.27 Prob > chi2 = 0.0000 sd(Residual) .1852066 .0125883 .1621069 .2115981 sd(FD_G) .0000683 .0490102 0 . sd(FDSQR_G) .1997736 .0346679 .1421752 .2807064NDEBT_G: Independent Random-effects Parameters Estimate Std. Err. [95% Conf. Interval]

_cons -.1152962 .0270128 -4.27 0.000 -.1682403 -.0623521 FD_G .6283551 .0777452 8.08 0.000 .4759773 .780733 FDSQR_G -.3936119 .0843204 -4.67 0.000 -.5588769 -.2283469 NDEBT_G Coef. Std. Err. z P>|z| [95% Conf. Interval]



Group variable: NDEBT_G Number of groups = 152Mixed-effects REML regression Number of obs = 153


Iteration 12: log restricted-likelihood = 15.667864 Iteration 11: log restricted-likelihood = 15.667863 Iteration 10: log restricted-likelihood = 15.667856 Iteration 9: log restricted-likelihood = 15.667824 Iteration 8: log restricted-likelihood = 15.667694 Iteration 7: log restricted-likelihood = 15.667111 Iteration 6: log restricted-likelihood = 15.664364 Iteration 5: log restricted-likelihood = 15.651562 Iteration 4: log restricted-likelihood = 15.602732 Iteration 3: log restricted-likelihood = 15.389318 Iteration 2: log restricted-likelihood = 14.629784 Iteration 1: log restricted-likelihood = 11.295448 (not concave)Iteration 0: log restricted-likelihood = 8.620027 (not concave)



. xtmixed NDEBT_G FDSQR_G FD_G, || NDEBT_G: FDSQR_G FD_G, noconstant covariance(independent) collinear

241

APPENDIX E

CV

1. PERSONAL

Siti Rahmi Utami

Born Jakarta, September 13, 1976

2. EDUCATION Master of Philosophy (MPhil), 2005-2008, Maastricht School

of Management (Netherlands).

Master of Management (MM), 2001-2003, University of

Trisakti (Indonesia).

Bachelor Degree (ST) in Environmental Engineering, 1995-

2000, University of Trisakti (Indonesia).

3. PUBLICATIONS

International Journal The Pecking Order Theory : Evidence from Manufacturing

Firms in Indonesia (with Prof. Eno L. Inanga), published by

Independent Business Review, Issue 1, No.1, (2008).

Foreign Exchange Rates, Interest Rates, and Inflation Rates in

Indonesia the International Fisher Effect Theory (with Prof.

Eno L. Inanga), published by International Research Journal of

Finance and Economics, Issue 26 (April 2009), pp.151-169.

Agency Costs of Free Cash Flow, Dividend Policy, and

Leverage of Firms in Indonesia (with Prof. Eno L. Inanga),

published by European Journal of Economics, Finance and

Administrative Sciences, Issue 33 (2011), pp.1-18.

Significance of Accounting Information in Explaining Market

and Book Values: The Case of Indonesian Banks (with Noraya

Soewarno, Siti Rahmi Utami as co-Author), published by

International Research Journal of Finance and Economics,

Issue 55 (2010), pp.146-157.

242

Indonesian Journal Efficient Market Hypothesis : Evidence from Indonesia Stock

Exchange (IDX), published by Ultima Accounting, University

of ultimedia Nusantara, Volume 1 (December 2009), pp.10-

17.

Seminar Paper Analisis January Effect pada Indeks LQ45, presented in

National Seminar at University of Atmajaya, Indonesia, 25-

26th

of May 2010.

4. ACCEPTED ARTICLE FOR PUBLICATION

Titled The Relationship between Capital Structure and the Life

Cycle of Firms in the Manufacturing Sector of Indonesia (with

Prof. Eno L. Inanga), will be published in International Research

Journal of Finance and Economics.

1 determinants of capital structure of firms in the manufacturing

Documents