Stock Return as Determinant of Debt – Equity Ratio: Companies Listed in Indonesia’s Capital Market (LQ 45 Index)

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Stock Return as Determinant of Debt – Equity Ratio:

Companies L

isted in Indonesia’s Capital Market (LQ 45 Index)

University of Groningen Faculty of Economics and Business

Msc Finance

Nur Andi Wijayanto Groningen, May 2008

Supervisors: Dr. Lammertjan Dam

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Preface

Throughout my study at the University of Groningen, I have been studying the Master of Science in Finance. This thesis is the final part of my master. The subject of my master thesis is “Stock Return as Determinant of Debt – Equity Ratio: Companies Listed in Indonesia’s Capital Market (LQ 45 Index)”.

With this preface, I would like to take the opportunity to thank a number of people for their help and support. In the first place my supervisor, Dr. Lammertjan Dam, who was always willing to advice me when necessary and at all times on short notice. Furthermore, I would like to thank to my friends Rossi and Maria for helping me during my study at Groningen. Moreover, I would like to thank my parents, Mr. and Mrs. Waldijono, my parents in law, Mr. and Mrs. Henny Leksmana for their big support and advice. To finish, I would like to thank my wife Inez and my son Rakha for always supporting, inspiring, and strengthening me, and without whom I could not have gone through all these processes this quick.

Nur Andi Wijayanto

Schoolholm 24A 9711 JH Groningen nurandi_wst@yahoo.co.id

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Abstract

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Table of Contents

1 Introduction………..6

2 Theoretical Framework………...10

2.1 Capital Structure and the Maximization of a Firm’s Value.………..10

2.1.1 Modigliani and Miller Theory………...10

2.1.2 Static Trade-Off Theory……….………...11

2.1.3 Pecking Order Theory……….………...12

2.2 Dynamic Capital Structure …..………...12

2.3 Some Other Considerations in the Capital Structure Decision…...14

2.3.1 Agency Conflict……….………...14

2.3.2 Market Timing………..……….………...15

2.3.3 Collateral Value of Asset...………….………...15

2.3.4 Profitability………....………….………...15

2.3.5 Size of Firms…..………....………….………...16

3 Brief Review of Indonesia Stock Exchange……….17

3.1 LQ-45 Index ………..…..………...17

3.1 Jakarta Islamic Index (JII) .…..………...18

4 Data and Basic Variable Description….………..20

2.1 Data Description……….…..………..20

2.2 Basic Variable Description….………26

5 Methodology…..……….27

5.1 Welch (2004) Approach…....……….27

5.2 Fama-MacBeth (1973) Methodology in Testing CAPM…...………29

5.3 Implemented Welch (2004) and Fama-MacBeth (1973) Approach..…….29

6 Empirical Result……..………...31

6.1 Change Regression……….…..………..32

6.2 Variance Decomposition...….………33

7 Discussion of Empirical Result……..………36

7.1 Static Trade off Analysis….……….…..………36

7.2 Pecking Order Theory Analysis……….36

7.2 Behavioural Theory Analysis……….37

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References………...40 Appendices.……….44

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1. Introduction

So far the determinant of capital structure has been the subject of considerable debate both in theory and in most empirical research. One of the most interesting debates is the managerial motive behind the capital structure decision.

In research literature over the past few decades, the determinant of capital structure has been given wide attention. In previous research, the better known proxy variable is used, such as tax costs, in which debt increases a firm’s value by reducing the corporate tax bill (Katharina Lewellen, 2003). This is because interest payments are tax deductible. Meanwhile, DeAngelo and Masulis (1980) find that tax deductions for depreciation and investment tax credits are substitutes for the tax benefits of debt financing. As a result, firms with large non-debt tax shield relative to their expected cash flow include less debt in their capital structures.

Another proxy of the determinant of capital structure is related to business risk as indicated by Jaffe and Westerfield (1987) that given the appropriate choice of parameters, the optimal debt level will be an increasing function of business risk. On the other hand, Castanias (1983) derives a decreasing optimal debt level-business risk relation over a certain range of business risk when cash flows are normally distributed. Meanwhile, Jayant R. Kale, Thomas H. Noe, and Gabriel G. Ramirez (1991) find that under corporate and personal taxation, it is demonstrated that the relationship between the optimal debt level and the business risk is roughly U-shaped. In the Growth Rate motive, Jensen and Meckling (1976), Smith Warnes (1979), and Green (1984) argue that the agency costs will be reduced if a firm issues convertible debt. This means that convertible debt ratios may be positively related to growth opportunities.

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Titman (1984) reports the motive behind the capital structure decision related to industry classification. He finds firms that make products requiring the availability of specialized servicing and spare parts will find liquidation very costly. This suggests that firms manufacturing machines and equipments should be financed with relatively less debt.

From all above motives behind the capital structure decision, Ivo Wech (2004) proposes an interesting alternative determinant of capital structure. He shows that over long time frames the stock price effects are considerably more important in explaining debt equity ratios than other previously identified proxies, such as tax costs, expected bankruptcy costs, earnings, profitability, market-book ratios, uniqueness, market timing, or the exploitation of undervaluation. He also identifies that U.S corporations do little to counteract the influence of stock price changes on their capital structures. By decomposing the capital structure changes into effects caused by corporate issuing net of retirement activity (called “net issuing” or just “issuing) and into effects caused by stock returns, he finds that all stock returns caused by equity growth can explain around 40 percent of capital structure, while all corporate issuing activities altogether can explain around 60 percent to 70 percent of capital structure. However, the corporate issuing motives are not used to counter-balance stock returns induced by equity value changes.

Another interesting issue behind managerial motive in the capital structure decision is the managerial issuing activity of debt and/or equity. According to corporate finance’s traditional view, the motive behind an issuing activity should be correlated closely to the motive to maintain the target of a firm’s capital structure. It should balance the costs and benefits associated with varying degrees of financial leverage; thus, when the firms are moving away from their targets, they tend to rebalance their leverages back to their target or optimal level.

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First, optimistic managers believe that the capital market undervalues their firm’s risky securities and may decline positive net present value projects that must be externally financed. Second, optimistic managers overvalue their own corporate projects and may wish to invest in negative net present value projects even when they are loyal to shareholders. This suggests that managers will be less inclined to issue equity to finance their project; on the other hand, they will optimistically raise fund from debt or internal fund.

The research to investigate the stock price effects on capital structure has been well documented for U.S corporations, but little research has been done for Indonesian companies. In view of the above, this thesis will try to contribute to the existing literature on capital structure by evaluating the influence of stock price on Indonesian companies. To this end, it will build on the works of Welch (2004) who studied the influence of stock price on capital structure. However, unlike his research, this thesis will examine Indonesian companies listed in the Indonesian market. Furthermore, this thesis tries to see whether the phenomena of stock return effects on capital structure happen only to U.S corporations or they also happen to companies in a developing market, like the Indonesian market.

In this study, a closer look will be taken at the companies listed in LQ-45 index of Indonesia Stock Exchange market; in more detail, a six year calendar data set from 2001 to 2006 will be investigated for stock price effects on capital structure. This is to empirically explore the stock price return effects on capital structure over one year, three year, and five year horizons. The main research question of this paper will therefore be:

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The purpose of this thesis in answering the question whether actual debt ratios behave as if firms readjust to their previous debt ratios or whether the firms permit their debt ratios to fluctuate with stock price fits into recently large mystery or unexplained motives behind the capital structure decisions.

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2. Theoretical Framework

Capital structure represents the major claim to a corporation’s assets. It includes publicly issued securities, private placements, bank debt, trade debt, leasing contracts, tax liabilities, deferred compensation to management and employees, performance guarantees, product warrantees, and other contingent liabilities. However, all capital can be classified into two basic types, namely debt and equity.

Generating external fund using debt, equity, or a combination of debt and equity will create a major corporate question whether or not there is an optimal mix of debt and equity that a firm should seek. Thus, searching for the optimal capital structure and the motive behind it has been becoming a major preoccupation of corporate finance.

2.1 Capital Structure and the Maximization of a Firm’s Value

The relationship between capital structure and a firm’s value has been the subject of considerable debate, both in theory and in empirical research. Throughout the literature, the debate has focused on whether there is an optimal capital structure for an individual firm or whether the proportion of debt usage is irrelevant to the individual firm’s value.

Basically, financial risk is the risk placed on the common stockholders as a result of the decision to use debt financing, or financial leverage, in the capital structure (Riahi and Belkauoi, 1999). Management is therefore interested in determining the amount of debt or financial risk that maximizes a firm’s value.

2.1.1 Modigliani and Miller Theory

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in 1963 which incorporated corporate taxes leading to a conclusion that leverage will increase a firm’s value. With corporate taxes, they concluded that leverage will increase a firm’s value.

2.1.2 Static Trade-Off Theory

This theory talks about costs and benefits of debt relative to equity and the optimal target of capital structure (Katharina Lewellen, 2003). It is determined by balancing tax shield of debt versus expected costs of financial distress. The theory does not provide a precise target but rather a range or an order of magnitude.

Tax Shield of Debt

Debt increases a firm’s value by reducing corporate tax bill. This is because interest payments are tax deductible. Personal taxes tend to reduce but not offset this effect. Thus, more of a leveraged firm’s operating income flows through investors.

Expected Costs of Financial Distress: Two Terms

There are two terms forming the expected costs of financial distress. They are probability of distress and cost if the distress actually happens. Several factors can stimulate probability of distress, like volatility of cash flow, the industry’s level of risk, the risk of firm’s strategy, the risk coming from macro economic shocks and seasonal fluctuation, and the risk emerging from technological and environmental changes. Having integrated and sophisticated management risk system to collect past data and present data and to execute the decision based on the collected information may become one of the best solutions to overcome this probability of distress. On the other hand, the component of cost of financial distress can emerge from many situations, like debt overhang when there is inability to raise funds to undertake investments or the scariness of customers and suppliers; thus the company should provide an implicit or an explicit warranty and the easiness of assets to be redeployed. This situation also plays an important role in the cost of financial distress.

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2.1.3 Pecking Order Theory

The introduction into economics of the explicit modeling of private information has made possible a number of approaches to explaining capital structure. In these theories, firm managers or insiders are assumed to possess private information about the characteristics of the form’s return stream or investment opportunities. In one set of approaches, the choice of the firm’s capital structure signals to outside investors the information of insiders.

In their pioneering work, Myers and Majluf (1984) show that, if investors are less well informed than current firm insiders about the value of the firm’s assets, the equity may be mis-priced by the market. If firms are required to finance new projects by issuing equity, under-pricing may be so severe that new investors capture more than the Net Present Value (NPV) of the new project, resulting in a net loss to existing shareholders. In this case, the project will be rejected even if its NPV is positive. This under-investment can be avoided if the firm can finance the new project using a security that is not so severely undervalued by the market, for example internal funds and/or riskless debt involve no under-valuations, and, therefore, will be preferred to equity by firms in this situation. Myers and Majluf (1984) refer to this as a “Pecking Order” theory of financing. According to the pecking order theory, firms seeking to finance new investments prefer to use funds according to a hierarchy: first internal funds, then debt issuance, and finally equity issuance. In other words, this pecking order arises because managers do not want to dilute existing shareholders. So the company will issue only overvalued securities.

2.2 Dynamic Capital Structure

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However, recent studies on the optimal capital structure try to test whether firms are actually engaged in such a dynamic rebalancing of their capital structure or not. The existence of transaction cost complicates the problem as Myers (1984) stated that large adjustment costs could possibly explain the observed wide variation in actual debt ratios, since firms would be forced into long excursions away from their initial debt ratios. If adjustment costs are large, then the attention to refining the static trade off theory should be less to understand what the adjustment costs are. These adjustment costs and how rational managers would respond to them are important. On the other hand, Welch (2004) finds that stock price’s fluctuation has influence on corporate capital structure. Meanwhile, Fama and French (2002) suggest that firms appear to respond in a longer time to leverage back to their optimal level. Moreover, Mark T. Leary and Michael R. Roberts (2005) argue that in the absence of adjustment costs, firms can continuously rebalance their leverage levels toward the optimal level of capital structures. If the costs of such adjustments are bigger than the benefits, firms will wait to recapitalize, resulting in extended moving away from their targets (Myers, 1984).

To cope with the recapitalization situation under which transaction cost is included, Fisher, Heinkel, and Zechner (1989) propose a concept by modeling capital structure choice in a continuous time framework. From this model, closed form solutions can be derived for the value of the firm’s debt and equity as a function of its dynamic recapitalization decisions. The result of optimal dynamic capital structure policy will depend upon the benefit of debt financing (e.g., a tax advantage from tax shield debt), potential costs of debt financing (e.g., bankruptcy costs), underlying asset variability, the riskless interest rate, and the size of the costs of recapitalizing.

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2.3 Some Other Considerations in the Capital Structure Decision

In recent years a number of theories have also been proposed to explain the variation in debt ratios across firms. The theories suggest that firms select capital structures depending on attributes that determine the various costs and benefits associated with debt and equity financing. Here some other attributes that different theories of capital structure suggest may affect the firm's debt-equity choice. These attributes are denoted as agency conflict, market timing, profitability, size, and collateral value of asset.

2.3.1 Agency conflict

Jensen and Meckling (1976) identify two types of conflict. First, the conflict between shareholders and managers arises because managers hold less than 100 percent of the residual claim. Consequently, they do not capture the entire gain from their profit enhancement activities. For example, managers can invest less effort in managing firm resources and may be able to transfer firm resources to their own personal benefit, e.g. , by consuming perquisites such as corporate jets, plush offices, building empires, etc. The manager bears the entire cost of refraining from these activities but captures only a fraction of the gain. As a result, managers overindulge in these pursuits relative to the level that would maximize a firm’s value. This inefficiency is reduced; the larger is the fraction of the firm’s equity owned by manager. Holding constant the manager’s absolute investment in the firm, increases in the fraction of the firm financed by debt increase the manager’s share of the equity and mitigate the loss from the conflict between the manager and shareholders. Moreover, as pointed out by Jensen (1986), since debt commits the firm to pay out cash, it reduces the amount of free cash available to managers to be engaged in the type of pursuits mentioned above. This mitigation of the conflicts between managers and equity holders constitutes the benefits of debt financing.

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projects, even if they are value decreasing. Such an investment results in a decrease in the value of the debt. The loss in the value of the equity from the poor investment can be more than offset by the gain in equity value captured at the expense of debt holders. Equity holders bear this cost to debt holders, however, when the debt is issued if the debt holders correctly anticipate equity holder’s future behaviour. In this case, the equity holders receive less for the debt than they otherwise would. Thus, the cost of the incentive to invest in a value decreasing project created by debt is borne by the equity holders who issue the debt. This effect, generally called the “asset substitution effect,” is an agency cost of debt financing. Jensen and Meckling (1976) argue that an optimal capital structure can be obtained by trading off the agency cost of debt against the benefit of debt as previously described.

2.3.2 Market Timing

Baker and Wurgler (2002) find that historical efforts to the timing of issuing equity in high market valuation have a persistent effect on corporate capital structure. It means that capital structure is a result of historical market timing efforts rather than a dynamic effort to optimize the company’s leverage level.

2.3.3 Collateral Value of Asset

Most capital structure theories argue that the type of assets owned by a firm in some way affects its capital structure choice. Scott (1977) suggests that, by selling secured debt, firms increase the value of their equity by expropriating wealth from their existing unsecured creditors. Myers and Mafluf (1984) also suggest that firms may find it advantageous to sell secured debt. Their model demonstrates that there may be cost associated with issuing securities about which the firm managers have better information than outside shareholders. Issuing debt secured by property with known values avoids these costs. For this reason, firms with assets that can be used as collateral may be expected to issue more debt to take advantage of this opportunity.

2.3.4 Profitability

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arise because of asymmetric information, or they can be transaction costs. In either case, the past profitability of a firm, and hence the amount of earnings available to be retained, should be an important determinant of its current capital structure.

2.2.5 Size of Firms

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3. Brief Review of Indonesia Stock Exchange

The capital market in Indonesia has actually existed long before the Independence of Indonesia. The first stock exchange in Indonesia was established in 1912 in Batavia during the Dutch colonial era. At that time, the Exchange was established for the interest of the Dutch East Indies (VOC).

During that era, the capital market grew gradually, and even became inactive for a period of time due to various conditions, such as World Wars I and II, power transition from the Dutch government to Indonesian government, etc.

Indonesian government reactivated its capital market in 1977, and it grew rapidly ever since, along with the support of incentives and regulations issued by the government. For the Indonesia stock market, there are two distinct indexes, apart from the Jakarta Composite Index (Indonesian: Indeks Harga Saham Gabungan, IHSG) which is an index of all stocks that are traded on the Indonesia Stock Exchange. Both two indices are LQ-45 and Jakarta Islamic Index (JII).

3.1 LQ-45 Index

LQ 45 is a stock market index for the Indonesia Stock Exchange (IDX). The LQ 45 index consists of 45 companies that fulfil certain criteria. The criteria are that the companies:

• are included in the top 60 companies with the highest market capitalization in the last 12 months,

• are included in the top 60 companies with the highest transaction value in a regular market in the last 12 months,

• have been listed in the Indonesia Stock Exchange for at least 3 months, and

• have good financial conditions, prospect of growth and high transaction value and frequency.

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3.2 Jakarta Islamic Index (JII)

Jakarta Islamic Index is intended as a benchmark to calculate the performance of syariah-based shares investment. Syariah-based Capital Market was officially established on March 14, 2003 together with the signing of MOU between the BAPEPAM and the National Syariah Board of Indonesian Council of Ulama (DSN-MUI). Even though it was established in 2003, the instrument of syariah capital market has existed in Indonesia since 1997. It was marked by the launching of Syariah-based Mutual Fund on July 3, 1997 by PT. Danareksa Investment Management. The Jakarta Stock Exchange then cooperated with PT. Danareksa Investment Management to launch the Jakarta Islamic Index (JII) on July 3, 2000. The purpose is to facilitate the investors who want to invest according to the Islamic principles.

Jakarta Islamic Index consists of 30 chosen shares that are in accordance with the Islamic syariah. The criteria used in choosing the shares in JII are determined by the Syariah Overseer Council of PT Danareksa Invesment Management.

Shares that are included in the Syariah Index are shares from issuers whose business activities are not against the Islamic law as below:

• Gambling business and any games that include gambling or prohibited trading.

• Conventional financial institution, including conventional banking and insurance.

• Businesses that produce, distribute, and trade food or drink that are prohibited by Islamic Law.

• Businesses that produce, distribute, and/or provide products and services that destroy morality and are harmful.

Besides the criteria above, the process of share selection in the JII by the IDX also considers the aspects of liquidity and financial condition of the issuers, in that:

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2. the company’s annual financial report or its mid-year financial report has the ratio of Obligation Assets for the maximum of 90%,

3. the company is included in the top 60 shares based on its last year average of market capitalization, and

4. the company is included in the top 30 shares based on its last year average of liquidity in the regular market.

Re-evaluation will be held every six months by considering the index components at the beginning of January and July each year. The changes in issuer’s line of business will be monitored all the time based on the public data available.

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4. Data and Basic Variable Description

4.1. Data Description

In order to achieve the objective and provide some contributions to research in this area, the thesis uses some data requirements as follows:

a) The annual reports of Indonesian companies listed in LQ-45 capital market from the period of 2001 to 2006 (source: company websites/other financial sites). b) The annual average stock returns of Indonesian companies listed in LQ-45 capital

market from the period of 2001 to 2006 (source: company websites/other financial sites).

c) The Debt-book value to Debt plus Equity–market value ratio of Indonesian companies listed in LQ-45 capital market from the period of 2001 to 2006.

The LQ45 Index is a capitalization-weighted index of the 45 most heavily traded stocks on the Indonesia Stock Exchange. The index was developed with a base value of 100 as of July 13, 1994. The data used here were obtained during the period of December 2001 to December 2006.

The total number of sample firms is 92 companies from the data during the period of 2001 to 2006. In total 270 firm-years qualify but only 249 firm-years have data in two consecutive years (for t and t+1; 32 firms for t ≡ 2001 and t+1 ≡ 2002, 42 firms for t ≡ 2002 and t+1 ≡ 2003, 45 firms for t ≡ 2003 and t+1 ≡ 2004, 41 firms for t ≡ 2004 and t+1 ≡ 2005, 45 firms for t ≡ 2005 and t+1 ≡ 2006, and 44 firms for t ≡ 2006 and

t+1 ≡ 2007), 152 firm-years have data over three years (for t and t+3; 32 firms for t ≡

2001 and t+3 ≡ 2004, 37 firms for t ≡ 2002 and t+3 ≡ 2005, 41 firms for t ≡ 2003 and

t+3 ≡ 2006, 42 firms for t ≡ 2004 and t+3 ≡2007), 69 firm-years have data over five

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ClosingPrice (in Rupiahs) Sales (in Millions Rupiahs) Asset (in millions Rupiahs)

Total Debt (in millions Rupiahs) Outstanding Shares Market Value of Equity (in millions), Et 2001 Min 30 135,352 230,310 83,659 162,298,701 4,869 Max 8,650 29,235,113 103,206,297 93,432,491 10,079,999,640 32,255,999 Standard Deviation 1,661 6,617,899 19,111,588 17,203,652 2,404,586,395 6,552,680 mean 1,209 5,139,577 10,728,152 8,159,812 2,682,259,255 3,485,210 median 600 2,097,454 2,657,713 1,191,391 1,956,625,502 1,043,208 2002 Min 5 21,187 73,968 4,849 450,000,000 11,500 Max 8,300 30,266,605 125,623,157 150,796,033 199,225,311,000 21,914,784 Standard Deviation 1,890 6,581,257 26,305,733 28,900,929 30,432,499,476 5,413,277 mean 1,262 4,665,651 12,298,509 10,456,508 7,598,248,733 3,399,770 median 425 1,995,457 2,851,727 1,463,909 1,994,581,502 1,102,580 2003 Min 90 340 74,066 6 210 0.107 Max 34,900 31,512,954 132,969,372 121,464,909 20,159,999,280 136,079,995 Standard Deviation 5,542 7,084,419 27,717,761 25,004,019 4,193,872,629 20,793,137 mean 2,705 4,765,051 12,526,186 9,281,834 3,705,422,979 8,015,914 median 1,200 2,151,505 3,416,276 1,672,446 1,924,088,000 1,966,442 2004 Min 110 103,606 165,662 48,336 503,302,000 521,933 Max 13,550 44,923,909 149,168,842 135,242,451 47,783,346,231 97,271,997 Standard Deviation 3,108 9,297,695 25,914,138 22,817,846 9,627,741,992 17,373,854 mean 2,281 6,297,915 15,413,729 11,516,327 7,453,628,219 11,004,786 median 775 2,858,538 5,220,120 3,600,176 4,133,979,422 4,406,168 2005 Min 90 50,147 237,213 25,538 503,302,000 916,010 Max 13,150 61,731,635 263,383,348 240,164,245 47,865,856,231 118,943,996 Standard Deviation 3,047 11,270,629 47,934,055 43,314,955 8,709,576,041 20,024,182 mean 2,555 6,815,025 25,215,266 20,097,899 7,702,866,906 13,198,910 median 1,040 3,027,081 6,402,714 3,373,069 4,157,214,436 5,908,662 2006 Min 155 429,959 1,739,140 278,019 993,633,872 777,836 Max 31,000 55,709,184 267,517,192 241,171,346 48,247,150,231 203,615,993 Standard Deviation 5,712 11,654,323 52,736,500 47,320,532 9,310,940,916 33,994,574 mean 4,072 7,926,572 27,336,362 21,308,321 8,745,979,242 21,118,375 median 1,270 3,702,660 7,170,936 3,494,268 4,741,361,153 9,293,679 TABLE 1 DESCRIPTIVE STATISTICS OF COMPANY LISTED IN LQ 45 INDEX OF

JAKARTA SROCK EXCHANGE FROM PERIOD OF 2001 UNTIL 2006

NOTE. - The sample is all publicly traded companies listed in LQ 45 index of Jakarta Stock Exchange. The index is a capitalization-weighted index of the 45 most heavily traded stocks on the Jakarta Stock Exchange, which is indexed twice a year in February - July and August - January. There were 92 companies included in this research in total for the period of 2001 until 2006. The companies used in here are those who are included in the August - January period. The closing price, sales, assets, and outstanding shares in here are the price, sales,assets, and outstanding shares at the end of fiscal year exactly time lined with total debt.

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2006, there are increases about 4,200 percent and 2,600 percent in stock price for both the minimum and the maximum over five year horizons. The average sales and asset of companies were Rp5,139,577 million and Rp10,728,152 million in 2001. Meanwhile, they were Rp7,926,572 million and Rp27,336,362 million in 2006, increasing 54 percent and 155 percent for both in average sales and asset respectively over five year horizons. An increase of about 162 percent in average issuing debt activity over five year horizons was recorded for the total debt of Rp8,159,812 million in 2001 and Rp21,308,321 million in 2006. For the outstanding shares (Nt), it was accounted approximately 2,682,259,255 and 8,745,979,242 shares on average for 2001 and 2006 respectively, meaning that there was an increase in issuing equity activity of about 226 percent. The average of market value of firm’s equity (Et) was Rp3,485,210 million in 2001 and Rp21,118,375 million in 2006, implying that there was an increase about 506 percent on average over five year horizons.

Table 2 shows that the mean of sample firms is about Rp24,182,730 million, the median firm is worth only about Rp6,890,945 million (25 percent of the mean firms) in the market value. The debt ratio has a mean of 47.62 percent and median of 44.22 percent.

A calculation of the relative importance of the dynamic components of debt ratios can be drawn from summary statistics for the component of debt ratio, normalized (divided) by firm’s market value (firm’s size) Et + Dt in the year in which issuances occur over six years (2001 – 2006). Companies in LQ 45 index experience stock return appreciation of 10.14 percent and 7.08 percent in average and median, respectively (this appreciation includes the dividend payout effects). The companies issue debt of 4.66 percent on average and 1.96 percent on median, and also issue equity of 24.57 percent and 2.48 percent on average and median respectively. All issuing activity has an average of 29.23 percent and median of 9.29 percent of its debt ratio.

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dividend effects because dividends show little cross section dispersion which indicates why subsequent results are indifferent to running test with stock returns or percent of equity growth.

Mean Median Std. Dev.

1 ADRt Actual Debt Ratio 47.62% 44.22% 26.06%

2 IDRt+k Implied Debt Ratio 44.92% 41.50% 28.60%

3 Et + Dt

Market Value ( in millions

Rupiahs) 24,182,730 6,890,945 46,131,361

Normalized by Market Value (Dt + Et)

4 TDNIt,t+k / (Dt + Et) Net Debt Issuing 4.66% 1.96% 20.78%

5 ENIt,t+k / (Dt + Et) Net Equity Issuing 24.57% 2.48% 153.88%

6 (TDNIt,t+k + ENIt,t+k) / ( Dt + Et) All Issuing Activity 29.23% 9.29% 156.79%

7 (rt,t+k x Et) / (Dt + Et) Total Rupiah Return 10.14% 7.08% 33.86%

3 Et + Dt

Rupiahs) 17,397,319 5,120,317 32,887,938

3 Et + Dt

Rupiahs) 13,844,115 3,201,573 28,715,800

There were 249 firm years in the one year panel, 152 firm years in three year panel, and 69 firm -years in the five year panel. The debt issuing activity used in here is all the debt being issued and retired in the year t, so are for the equity and all issuing activities which are equity being issued and retired also all issuing and retiring activities in the year t . Stock return rt,t+k used here includes dividend payment effects.

TABLE 2. Selected Descriptive Statistics

No Item Description Five Year

No Item Description Three Year

One Year

No Item Description

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Companies in the sample are not averse to issuing activity. In both mean and standard deviation, all corporate issuing activity is about one and a half times of total Rupiah return induced by equity value changes on average. In other words, issuing activity is large enough to counteract a good part of the capital structure effects of stock returns.

-45.95% -13.11% -2.20% 8.22% 15.42% 20.48% 35.74% 48.34% 60.91% 107.32%

1 TDNIt,t+1 Net Debt Issuing 1.09% 3.58% 0.56% 2.79% 3.58% 1.31% 5.30% 2.50% 0.71% 1.29%

2 ENIt,t+1 Net Equity Issuing 11.50% 0.60% 0.02% 0.19% 0.62% 1.14% 4.11% 8.33% 14.09% 29.68%

3 ADRt+1 Ending Actual Debt Ratio 41.94% 43.19% 46.89% 43.11% 41.02% 46.18% 45.21% 35.28% 22.19% 31.76%

4 ADRt Starting Actual Debt Ratio 39.00% 40.92% 45.56% 41.23% 46.18% 60.38% 49.62% 49.17% 38.23% 47.75%

5 IDRt+1 Implied Debt Ratio 55.57% 43.75% 46.83% 38.64% 42.69% 55.87% 40.79% 38.71% 28.17% 31.95%

-69.47% -25.00% 5.45% 40.36% 66.67% 101.69% 134.89% 209.57% 329.51% 484.62%

-67.48% -33.33% 12.94% 78.38% 138.78% 218.65% 303.67% 587.50% 766.67% 2000.00%

TABLE 3. Corporate Activity and Capital Structure, Classified by Stock Returns (Year Adjusted) A. Sort by Calendar Year and One - Year Net Stock Return (includes dividend effect)

C. Sort by Calendar Year and Five - Year Net Stock Return (includes dividend effect)

No Item Description Sort Criterion, Net Return (t,t+5)

No Sort Criterion, Net Return (t,t+3)

Sort Criterion, Net Return (t,t+1)

Note. - All numbers are medians and are quoted as percentages. Firms are first sorted by calendar year, then allocated to deciled on the basis of their stock return rank (with in each gorup of 10 consecutive firms). In each panel, the first two rows are normalized by firm size (Dt+ Et), and

the last three rows are not. In panel A. there are between 23 and 29 observations per decile, in panel B. there are between 14 and 18 observations per decile, in panel C. there are between 6 and 7 observations per decile.

B. Sort by Calendar Year and Three - Year Net Stock Return (includes dividend effect)

No Item Description

Item Description

Table 3 shows the significance of stock return relative to the debt issuing activity, equity issuing activity, both debt and equity issuing activity, Actual Debt Ratio, Ending Debt Ratio, and Implied Debt Ratio in a simple classification. All firms are sorted by stock return and firms are allocated into ten bins based on their stock return performance. The sorting procedure itself does not use any historical capital structure information.

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target or previous debt ratio. In other words, firms whose debt ratio are increasing or decreasing due to poor or good stock return performance do not use their issuing activity to readjust but even to amplify the stock return changes (Baker & Wurgler 2002). This trend also happens in the data on the three year horizons and five year horizons.

This thesis is less interested in issuing activity per se, but it is interested more in the actual capital structure relevance of the dynamic component which is explored in the last three row data. For example, in Table 3 there is a trend of decreasing in ending ADR across firms having experienced recently different rates of stock return performance over one year and three year horizons. Over annual horizon, there is a spread for the firms in the median stock return performance of -45.95 percent ends up with an actual debt ratio of 41.94 percent while firms in the median stock return performance of 107.32 percent end up with an actual debt ratio of 31.76 percent. In the horizon over three years, the worst stock return performance (with median stock return performance of -69.47 percent) ends up with debt ratios of 50.92 percent while the best stock return performers (with median stock return performance of 484.62 percent) ends up with actual debt ratios of 34.61 percent. In addition, over five year horizons the worst stock return performance (with median stock return performance of -67.48 percent) ends up with debt ratios of 13.92 percent while the best stock return performers (with median stock return performance of 2000 percent) ends up with actual debt ratios of 18.84 percent. However, the trend over five year horizon shows that there is a decrease in ending ADR across firms having experienced different rates of stock return.

From data in Table 3, we can see that there is unlikely relationship that the ending of actual debt ratio differences is caused by the originating or starting debt ratio (“starting ADR” rows). Over one year horizon, starting debt ratio does not correlate with the stock return performance. On average, stock return deciles start out with actual debt ratio about 45 percent and over three year horizons, the starting ADR is about 53 percent in average.

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(following equation (3)). It seems that implied debt ratio “IDR” can explain future debt ratio (“ending ADR” rows) better than the “starting ADR”. From Table 3, it is shown that implied debt ratio “IDR” rows fits better than the “starting ADR” rows visually in explaining “ending ADR” rows. For exact explanation of IDR, “starting ADR” and “ending ADR” relationship, the basic regression of Fama-Macbeth in testing CAPM will be used.

4.2 Basic Variable Description

Here, several basic variables in the thesis are used to investigate the effect of stock return on capital structure.

Debt Dt is defined as total debt, including both short term and long term debt of the firms at time t. It is assumed that the book value of debt is roughly equal to the market value of debt. Et is the market value of equity at time t. It is calculated by the number of shares time closing price at the end of fiscal year, exactly time lined with Dt.

Meanwhile, rt,t+k is the percentage price change in the market value of equity estimated by

[Stock Pricet+k – Stock Pricet ] / Stock Pricet (1)

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5. Methodology

This thesis investigates debt ratio dynamics towards the previous debt ratios and stock price fluctuation using the approach of Welch (2004) primarily in cross-section. A Fama–Mac Beth regression method is used here to estimate the relationship between the debt ratio dynamics, the previous debt ratio, and the fluctuation of stock return.

5.1 Welch (2004) Approach

This thesis takes the approach of Welch (2004) to test whether managers are actively engaged in setting target debt ratios or whether managers passively allow their debt ratios to commove with stock price’s fluctuation. In this section we introduce the method by Welch (2004).

The basic specification to be estimated is:

(2) t k t t k t ADR IDR ADR₊ =α₀ +α₁ +α₂ ₊ +ε

where ADR is the firm’s actual debt ratio, defined as the book value of debt (D) divided by the book value of debt plus the market value of equity (E),

t t t t D E D + = ADR (3)

while the term IDR is the implied debt ratio as result if the firms issue (net) neither debt nor equity,

(4) t k t t t t k t D r E D IDR + + = + + ) 1 ( _,

and rt,t+k is the stock return. Welch’s hypotheses are derived from equation (2):

Perfect readjustment : α1 = 1, α2 = 0

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Additionally, the intercept α0 can be included as a constant target debt ratio. The

empirical specifications are primarily cross-sectional following Fama–Macbeth methodology in testing CAPM.

The dynamics of the capital structure underlying equation (2) is formulated as follows. The amount of debt changes with new debt issues, debt retirements, coupon payments, and consequently debt value changes:

Dt+k =Dt +TDNIt,t+k (6)

where TDNI is total debt net issuing activity. The amount of corporate equity changes with stock returns and new equity issues net of equity repurchases. Corporate equity therefore evolves as:

E_t₊_k =E_t(1+r_t_,_t₊_k)+ENI_t_,_t₊_k (7)

where ENI is net equity issuing and stock repurchasing activity. Thus, debt ratio evolves as: ) ( _t _k _t _k k t D E D + + + + = k t ADR₊ (8) k t t k t t t k t t t k t t t r E D D + + + + + + + + + = , , , , ENI ) 1 ( TDNI TDNI

Mathematically, if the corporation issues debt and equity so that

k t t t k t t t k t t r D E + + + ₌ ₋ , , , TDNI ENI (9)

then ADR remains perfectly constant across periods .

In contrast, if the corporation issues debt and equity so that

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t k t t k t t t k t t t k t t D r D E + + + + ₌ ₊ _⋅ , , , , TDNI TDNI ENI (10)

then IDR perfectly predicts debt ratios

(

IDRt+k = ADRt ⇒α1 =0,α2 =1

)

_{, which} means that the debt ratio passively follows the stock movements. Unfortunately, equations (9) and (10) are not suitable for direct cross sectional estimation because many firms have zero or tiny debt levels.

5.2 Fama-MacBeth (1973) methodology in Testing CAPM

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(

it

)

it i

it X

R =γ₀ +γ₁β + γ₂ _, +ε_,

where Ri,t is the return of stock i at period t; βi is beta of stock i; εi,t is error at t.

The basic idea of Fama-MacBeth method in testing CAPM has two stage procedures as follows. First, beta of stock i is estimated using time series regression. Second, run cross sectional regression of equation (11) period by period. Running this regression for each period t will get the time series of coefficients γ0,t, γ1,t, etc. Then, compute mean and standard deviation of γ’s from these time series.

5.3 Implementation of Welch (2004) Approach and Fama-MacBeth (1973) Methodology

The empirical specifications of this thesis are primarily cross sectional following the approach of Welch (2004) and Fama-MacBeth methodology in testing CAPM. First, all data and information of LQ 45 companies from 2001 to 2006 are sorted by calendar year. Then, the data are rearranged to calculate the stock return, implied debt ratio, starting actual debt ratio, ending actual debt ratio in every given year using equations (3), (4),and (8).

The next step is running the cross sectional regression based on equation (12) for every 249 firm years data over one year horizon (period t and t+1),

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run cross sectional regression based on equation (13) for 152 firm years data over three year horizons (period t and t+3),

(13) t t t t ADR IDR ADR₊₃ =α₀ +α₁ +α₂ ₊₃+ε

and do also cross sectional regression based on equation (14) for 69 firm years data over five year horizons (period t and t+5),

(14) t t t t ADR IDR ADR₊₅ =α₀ +α₁ +α₂ ₊₅ +ε

Then compute mean and standard deviation of estimated coefficients from the time series of each period. In addition, pooling and overlapping all firms – instead of Fama-Mac Beth – when horizons are more than one year does not change the coefficient estimates because the same firm may appear in overlapping regression (Ivo Welch, 2004).

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6. Empirical Result

The regression result of equation (2) is presented in Table 4. The reported coefficients and standard errors are computed from the time series of cross sectional regression coefficients (using Fama–Mac Beth method in testing CAPM).

No

Horizon k (Fama -MacBeth)

Cons. IDRt+k ADRt Std. Error Cons. Std. Error IDR Std. Error ADR R2

Cross Sectional Regression 1 1 - Year - F-M - 69.53% 27.07% - 26.31% 24.60% 88.03% 6 2 3 - Year - F-M - 79.28% 13.15% - 13.82% 13.60% 54.04% 4 3 5 - Year - F-M - 67.52% 20.59% - 20.79% 16.45% 48.25% 2 1 1 - Year - F-M 0.93% 69.16% 25.94% 1.46% 26.08% 23.56% 88.13% 6 2 3 - Year - F-M 8.96% 76.51% 1.71% 5.72% 15.15% 17.80% 57.18% 4 3 5 - Year - F-M 7.49% 65.27% 11.19% 9.69% 17.87% 28.55% 50.55% 2

ADRt+k = [ α0 + ] α1 IDRt+k + α2 ADRt + t+k

Reported coefficients and standard error estimates are computed from the time series of cross sectional regression coefficients and quoted as percentage. A coefficient of 100 percent on impled debt ratio (IDRt+k) indicates perfect lack of readjustment, and a

coefficient of 100 percent on actual debt ratio (ADRt) indicates perfect readjustment. the R2 are time series averages of cross

sectional estimates. in the one year regressions, 249 firm years are used, in the three year regressions, 152 firm years are used, in the five year regressions, 69 firm years are used.

TABLE 4. Fama - MacBeth Regression Explaining Future Actual Debt Ratios ADRt+k

with Debt Ratios ADR, and Stock Return - Modified Debt Ratios IDRt,t+k

B. With Intercept A. Without intercept

Note. - The cross - sectional regression specifications are

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From Table 4 panel B, the regression uses an intercept as a proxy for a constant debt ratio target. Over one year horizons, the average firms show little tendency to revert to their starting debt ratios (ADRt); they count about 25.94 percent. On the other hand, with the existence of an intercept, starting debt ratio (ADRt) has less economic significance to influence the ending debt ratios, from 27.07 percent to 25.94 percent. The loss of starting debt ratio’s economic significance also happens over three year horizons and five year horizons from 13.15 percent to 1.71 percent and from 20.59 percent to 11.19 percent respectively. In competition with the influence of stock return through implied debt ratios (IDRt+1), starting debt ratios experience less economic significance. Over one year horizons the average firms allow their ending debt ratios (ADRt+1) to drift for about 69.16 percent with stock return, compared to 25.94 percent. The influence of stock return even becomes more important and significant over three year horizons for about 76.51 percent compared to 1.71 percent of starting debt ratios (ADRt+1). But then the firms began to readjust more towards target debt ratios; they account for 8.96 percent and 7.49 percent of intercept over three year and five year horizons respectively. Over five year horizons, the average firms experience slightly less stock return influence than over one year and three year horizons for about 65.27 percent.

Thus, it is concluded that observed corporate debt ratios at any fixed point in time series are largely transient, commoving with stock return performances. Any deliberate readjustment is slow and modest.

6.1 Change Regression

The regression can also be estimated in changes and or/with a restriction that the coefficients on IDRt+1 and ADRt add up to 1:

(15) t t k t k t IDR ADR ADR₊ =α₀ +α₁ ₊ +(1−α₁)⋅ +ε

Rearranging the estimated regression is:

t t k t t k

t ADR IDR ADR

ADR₊ − )=α +α ⋅( ₊ − )+ε ( ₀ ₁ % 78 . 40 ; ) ( % 53 . 76 % 1 ) ( 2 1 1− =− + ⋅ + − + =

+ ADR IDR ADR R

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% 15 . 41 ; ) ( % 95 . 88 % 2 ) ( 2 3 3− =− + ⋅ + − + =

ADR_t _t _t _t ε_t % 00 . 45 ; ) ( % 59 . 76 % 4 ) ( 2 5 5 − =− + ⋅ + − + =

ADR_t _t _t _t ε_t

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The coefficient estimates are highly statistical significant (99 percent level of confidence) and a first difference term in ADR adds no statistical or economic power. It can be concluded that over one year horizons stock returns induced by equity changes have a coefficient of 76.53 percent compared to 69.16 percent (in level) in influencing observed debt ratio changes. The stock returns induced by equity changes have a coefficient of 88.95 percent compared to 76.51 percent (in level) and a coefficient of 76.59 percent compared to 65.27 percent (in level) in influencing observed debt ratio changes over horizon three year and five year, respectively.

6.2 Variance Decomposition

One can isolate the dynamic components laid out in equation (8). That is, it is possible to predict ADRt+k not only with ADRt updated only for stock returns (IDRt+k), but also with ADRt updated for example, for corporate issuing activity between t and t+k (keeping other dynamic components at constant zero).

Level Difference Level Difference Level Difference

1 Past Debt Ratio, ADRt 81.75% 30.19% 18.70%

2 Implied Debt Ratio 85.65% 40.78% 51.98% 41.15% 41.32% 45.00%

3 All Issuing Activity 68.37% 39.55% 21.71% 45.96% 34.90% 44.37%

4 Net Equity Issuing Activity 63.28% 9.93% 1.56% 17.07% -1.91% 15.22%

5 Net Debt Issuing Activity 86.42% 32.12% 51.71% 31.60% 42.16% 34.59%

TABLE 5. Explanatory Power of Components of Debt Ratios and Debt Ratios Dynamic: Time - Series Average R2s from cross sectional Fama - MacBeth Regressions

Note. - in level, ADRt+k is explained by the regressor. In differences, ADRt+k - ADRt, is explained by

the regressor minus ADRt. The ratio are defined as follows: row 1: Dt / ( Dt + Et ) ; row 2 : Dt / [ Dt +

Et x (1 + rt,t+k) ] ; row 3 : ( Dt + TDNIt,t+k ) / ( Dt + TDNIt,t+k + Et + ENIt,t+k ) ; row 4 : Dt / ( Dt + Et +

ENIt,t+k ) ; row 5 : ( Dt + TDNIt,t+k ) / ( Dt + TDNIt,t+k + Et )

k = 5 - Year, AVG R2 No Description k = 1 - Year, AVG R2 k = 3 - Year, AVG R2

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ratios (starting ADRt), 30.19 percent by capital structure of 3 previous year’s capital structure, 18.70 percent by capital structure 5 years earlier. The table also shows that stock returns induced by changes in capital structure remain the most important component among starting debt ratios (starting ADRt), net issuing activity, debt issuing activity, and all issuing activity over one year horizons and three year horizons. Nevertheless, the interesting fact is that the influence of stock return through IDR or stock return induced by changes in capital structure over five year horizons is less important than corporate net debt issuing activity, although not much.

Over one year horizons, the influence of stock returns induced by capital structure accounts for about 40.78 percent of the change in debt ratios. Over three year horizons stock return’s influence on capital structure through IDR accounts for about 41.15 percent of debt ratio changes whereas over five year horizons stock return’s fluctuation accounts for about 45 percent of debt ratio changes. On the other hand, all net issuing activities over one year horizons, three year horizons, and five year horizons are responsible for 39.55 percent, 45.96 percent, and 44.37 percent of debt ratio changes respectively. It is also interesting to see that influence of stock return through IDR or stock return induced by changes in capital structure over three year horizons is less important than all corporate issuing activity, although not much. Note: the two of stock returns and all issuing activity induced by debt ratio changes do not need add up to 100 percent.

In principle, there is more than enough corporate issuing activity relating to capital structure relevant to counteract stock returns induced by equity growth. Firms are not inactive; they just do not choose to counteract their stock returns induced by their capital structure.

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7. Discussion of Empirical Result

7.1 Static Trade off Theory Analysis

Ivo Welch (2004) suggests using cost-benefit trade-off in analyzing the evidence that corporations are not inactive; they just do not use the issuing activity in a proper way to rebalance their capital structure. The benefit relies on how the hypothetically friction-free optimal debt ratio shifts with stock returns called Dynamic Optima. Here, Ivo Welch argues that if the optimal debt ratio changes one to one with stock returns, then there is no need for firms to rebalance toward their previous or static debt ratio targets. But he also points some drawbacks of this theory. First, the argument is less compelling for value firms with already low debt ratios. Second, firms experiencing the most immediate increases in profitability do not show any differences in their readjustment tendencies. On the other hand, another explanation is related to the transaction cost. The fact that there is more readjustment over longer horizons is also consistent with transaction costs playing role. But once again he points out the drawbacks of this theory. First, for large U.S. corporations, direct transaction costs are small and practitioners believe them to be small (Graham and Harvey 2001). Second, readjustment patterns are similar across firms in which transaction costs are very different. Third, firms do not seem to lack the inclination to be capital structure active. They just seem to lack the proper inclination to readjust for equity value change. The second explanation related to cost is the indirect costs, but once again this theory suffers from a problem similar to its cousin’s theory: direct transaction cost explanation, which can explain inertia better than lack of readjustment although firms are very active in real life.

7.2 Pecking Order Theory Analysis

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The implication for capital structure if a firm follows the Pecking Order theory is that its leverage ratio results from a series of incremental decisions. The firm does not attempt to reach a target; thus high cash flow will result in decreased leverage ratio, and low cash flow will lead to increased leverage ratio. Moreover, there may be good and bad times to issue equity depending on the degree of information asymmetry, and the rationale for hybrid instruments will be one of the managerial choices in issuing company’s securities. But this theory itself suffers from a problem in explaining why firms are reluctant to rebalance more toward debt when their stock prices increase.

7.3 Behavioral Theory Analysis

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8. Conclusion

This thesis investigates one of the most discussed issues in finance, namely the capital structure of the firm. It does so by using Welch (2004) approach in testing whether the managers are actively engaged in their optimal capital structure target or whether the managers passively allow their leverage level to be influenced by the fluctuation of stock price. The test is taken by implementing Fama-Macbeth regression in testing CAPM using data from companies listed in LQ 45 index of Jakarta Stock Exchange from the period of 2001 to 2006.

The important result from the evidence is that the corporate debt ratios at any fixed point in time are largely transient, commoving with stock returns. The influence of stock return’s fluctuations is the first order determinant of debt ratios and is an important determinant among net debt issuing activity, and net equity issuing activity, and all issuing activities. And from the variance decomposition test results, it can be concluded that the firms are actually not inactive concerning issuing activity (both for the debt issuing activity and equity issuing activity); they just do not choose to counteract their stock returns.

Several assumptions are made in building the whole process of this thesis and therefore become the limitation of the thesis as well. First, the calculation of debt is defined as the book value of the debt. It is assumed that the book value of debt is roughly equal to the market value of debt. Second, the thesis uses data only for the most actively traded Indonesian companies listed in LQ 45 index of Indonesia Stock Exchange.

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