Combined Final

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Chapter 1: Introduction

In this chapter, we will start by providing some relevant background information on stock

market, capital structure and firm‘s performance. We will then proceed to discuss on the

underlying problem derivate due to the lack of established capital structure studies.

1.1 BACKGROUND OF THE STUDY

1.1.1 Stock Market

Stock Market, also known as the equity market, allows investors to participate in the

financial achievements of the companies by holding the companies‘ shares. Malaysia

stock exchange is Bursa, previously known as Kuala Lumpur Stock Exchange (KLSE),

traced back to 1930.

Effective on 3 August 2009, the Board and Second Board are merged and now known as

the ―Main Market‖, while the MESDAQ Market as the ―ACE Market‖. As at 9 Dec

2013, a total of 913 companies are listed in Bursa, with 805 from the Main Market,

remaining 108 from the ACE market, as shown in Table 1.1.

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Table 1.1 Total Numbers of Listed Companies

Source: Bursa Malaysia

1.1.2 FBM KLCI

Introduced in 1986, Kuala Lumpur Composite Index (KLCI) is a stock market benchmark to for

indicating Malaysia stock market as well as the country‘s economy (Asmy et al, 2009). Effective

from 6 July 2009, KLCI is now known as FTSE Bursa Malaysia KLCI (FBM KLCI), and is

calculated by FTSE. Other changes include reducing the number of constituents from 100 to 30

companies, and the index is calculated every 15 seconds instead of 60 seconds (InsiderAsia,

2009).

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In 17 June 2013, FTSE (2013) announce Sapurakencana Petroleum and MISC to replace Bumi

Armada and YTL Power International. Table 1.2 shows the 30 companies within the FBM KLCI

as of 31 December 2013. Table 1.2 shows the constituents of FBM KLCI as of 31 December

2013.

Table 1.2 Constituents of FBM KLCI (as of 31 December 2013)

Constituent Name

AMMB HOLDINGS BERHAD

ASTRO MALAYSIA

AXIATA GROUP

BRITISH AMER TOBACCO

CIMB GROUP HOLDIN

DIGI.COM BERHAD

FELDA GLOB

GENTING BERHAD

HONG LEONG BANK BHD

HONG LEONG FIN

IHH HEALTHCARE

IOI CORPORATION BHD

KUALA LUMPUR KEPONG

MALAYAN BANKING BHD

MAXIS BHD

MISC BHD

PETRONAS CHEMICALS

PETRONAS DAGANGAN

PETRONAS GAS BERHAD

PPB GROUP BHD

PUBLIC BANK BHD

RESORTS WORLD BHD

RHB CAPITAL BERHAD

SAPURAKENCANA

SIME DARBY BHD

TELEKOM MALAYSIA BHD

TENAGA NASIONAL BHD

UEM SUNRISE

UMW HOLDINGS BERHAD

YTL CORPORATION BHD

Source: FTSE

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1.1.3 Capital Structure

In finance term, capital structure refers to how a company finances their assets through a

mixture of debt, equity and hybrid securities (Saad, 2010). It refers to how a firm use

diverse sources of funds to finances its overall operations and growth (Tsuji, 2011).

To determine the capital structure, the firm needs to consider many factors, some of

these factors include:

Company‘s business risk

Company‘s financial performance

Company‘s growth opportunities

Company‘s size

Company‘s financial flexibility or solvency

Company‘s tax position

Company‘s managerial attitude

Industry Performance

Market Environment

Ownership structure

Business risk refers to the uncertainty of the projections of future return if the firm uses

no debt. Financial performance refers to the firm‘s ability to generate profits or

profitability assessed by financial measures. Some of these financial measurements

include return on assets (ROA), return on equity (ROE), return on investment (ROI) and

Tobin‘s Q. If a company is very certain of the accuracy of projections of future return, it

gives the firm company confidence to apply loan without much concern on default risk.

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There are many theories related to capital structure, but perhaps the most commonly

discussed the agency cost model, which refer to an increase in leverage will makes the

firm more efficient .

Some study further elaborate that while agency cost theory is true, however if the

leverage continue to increase, excessive leverage will elevate the expected costs of

financial distress, bankruptcy, or liquidation may and may overwhelm the benefit gain as

in agency cost mode.

Other theories that are related with capital structure decision include pecking order

theory, MM theory, trade-off theory, signaling theory. Pecking theory refers to firms

prioritize their sources of financing with internal financing as the most favourable

financing, follow by debt, then external equity. MM theory, also known as Modigliani

and Miller's Capital-Structure Irrelevance Proposition, hypothesized that in perfect

markets, it does not matter what capital structure a company uses to finance its

operations (Investopedia). The signaling theory says that, in the presence of asymmetric

information, Signaling theory refers to when information asymmetric exist, decrease in

leverage signal overvalued stock and vice versa, therefore debts is expected to be

positively correlated to profitability.

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1.1.4 Equity Funding

Equity funding can be further divided into two groups, namely internal finance and

external finance. Internal finance is when the owner-manager of the firm finance the

company uses their own wealth. Example for internal equity are such as funding the

company using personal equity, such as savings or asset, or it may be in the form of

retailed earrings.

As an alternative, firms may also finance the company through external equity. Some

examples for external funds are rising include venture capital, initial public offerings

(IPOs) or crave out.

1.15 Liability Funding

Debt can take the form of private debt or corporate, examples are bank loan and

corporate bonds respectively (Ulph & Valentini, 2004). Investopedia refers liability as ―a

company's legal debts or obligations that arise during the course of business operations‖.

Liabilities can be in the form of loans, accounts payable, mortgages, deferred revenues

and accrued expenses.

1.1.6 Firms’ Performance

According to Investopedia, financial performances is a subjective measure of how well a

firm can generates revenues by using assets from its primary mode of business. It is also

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used as a general measure of a firm‘s financial health. Financial performance can be

measure in many different ways, but all measures should be taken in aggregation. In

their study on capital structure and firm performances, Salim & Yadav (2012) uses

return on equity (ROE), return on asset (ROA), Tobin s Q and earning per share (RPS)

to measure firm performances.

1.2 PROBLEM STATEMENT

Capital structure decision is critical for the continuation of business organization as well

as to maximize return to stakeholders (Akintoye, 2008). Unfortunely, while it is

important for the survival of business organization, previous researches are inconsistent

on the relationship between capital structure and performance (John, 2013), both

theoretically and empirically (Kebewar and Shah, 2012). For instance, the controversy is

shown when some research found debt has negative relationship on profitability

(Kebewar & Shah, 2012) ; Majumdar & Chhibber, 1999; Eriotis et al., 2002; Ngobo &

Capiez, 2004, Goddard et al., 2005; Rao et al., 2007; Zeitun & Tian, 2007; Nunes et

al.,2009), while other showed a positive influence (Baum et al., 2006, 2007; Berger &

Bonaccorsi, 2006; Margaritis & Psillaki, 2007, 2010). However, as Berger and

Bonaccorsi (2006), Margaritis and Psillaki (2007) and Kebewar (2012) found the

presence of non linear effect (inverse U-shaped relationship) of debt and profitability,

thus suggesting that it may not be suitable to find the relationship between debt and

profitability using linear test. As controversy widely appears in the context of capital

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structure, making it hard for corporate to apply capital structure related theory on firm‘s

financial management practice.

Over the years, different theories about capital structure composition was develop,

however no consensus developed for the optimal composition of capital structure (Raza

et al, 2013). We suggest that perhaps the reasons behind the difficulties to develop an

optimal capital structure lies within the fact that most research on capital structure

focuses on the effect of debt, and did little to provide important on the equity side of

capital structure, even though both debt and equity play important roles the capital

structure.

The purpose of capital structure decision is to utilizing various capital instruments to

maximize return for the organization while minimize the cost of financing. An

appropriate capital structure can helps the firm to generate greater profit, however if

inappropriately manage, it will incur more cost than profit, and may eventually lead to

the default, especially during industry downturn. If a firm is too conservative on

leverage, the firm will have to forego investment opportunity, possibly experience a

sluggish in its performance. On the other hand, excess leverage exposes the firm to

higher possibility lt, and the risk of the firm‘s its credit rating being downgraded.

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For the context of Malaysia, public listed company that is financially distressed, or does

not have a core business or has failed to meet minimum capital or equity (Less than 25%

of the paid up capital) will be classify as a Practice Note 17 (PN17) companies

(Mohammed, 2012). During the 1997 Asian economic crisis, Malaysia was hit hard.

The crisis also affected Malaysian companies and several affirms were in financial

distress. Those companies had to file under a bankruptcy protection plan, namely PN17,

which is similar to Chapter 11 in the United State, to seek protection and to undertake a

capital restructuring exercise (Baharin and Sentosa, 2013).

The most recent news about PN17 is regarding a steel manufacturer, Perwaja Holdings

Berhad (PHB), added to the list of PN17. As reported by The Edge Malaysia on 26

November 2013, PHB will not be able to pay off the Murabahah Medium Term notes of

RM50 million, and is now PN17 Issuer. (Ho, 2013). With that, Bursa Malaysia Stock

Exchange currently has 28 companies on their current PN17 list.

RAM (Rating Agency Malaysia Berhad) (2013) reported that Silver Bird Group Bhd, a

bread Manufacturer (High 5), default on its Commercial Paper/Medium Term Note

Programme (CP/MTN Programme) instrument in April 2012. On October 2013,

theSundaily (2013) reported that Silver Bird Group Bhd triggered the PN17 criteria, the

auditors expresses a disclaimer of opinion on the firm‘s audited accounts for the

financial year ended Oct 31, 2011 (FY11) and a default in payment by its major

subsidiaries.

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Figure 1.1: Annual Corporate Default Count and Volume

Source: RAM

1.3 OBJECTIVES

The main objective of this study is to examine the relationship of capital structure and

profits (performance) of public listed firms in Malaysia‘s stock exchange.

Specifically the study sets out to:

i. To examine the relationship of debt on firms‘ performance

ii. To examine the relationship of equity financing on firms‘ performance

iii. To test the agency cost model

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Following the main and specify objectives above, we wish to answer the following

research questions:

1. Does debt effects firm‘s performance?

2. Does equity effects firm‘s performance?

3. Does long-term debt effects firm‘s performance?

4. Does short-term debt effects firm‘s performance?

1.4 HYPOTHESIS

1. Does debt effects firm‘s performance?

H0: Debt will not affect firm‘s performance.

H1: Debt will affect firm‘s performance.

2. Does equity effects firm‘s performance?

H0: Equity will not affect firm‘s performance.

H1: Equity will affect firm‘s performance.

3. Does long-term debt effects firm‘s performance?

H0: Long-term debt will not affect firm‘s performance.

H1: Long-term will affect firm‘s performance.

4. Does short-term debt effects firm‘s performance?

H0: Short-term debt will not affect firm‘s performance.

H1: Short -term will affect firm‘s performance.

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1.5 SIGNIFICANCE OF THE STUDY

A sound capital structure is important for firms, as there are interrelationships between

capital structure and various other financial decisions variables. Hence, it is necessary to

acquire the skill to examine firm‘s capital structure and to understand its relationship to

risk, return and value (Nimalathasan and Brabete, 2011).

This research aims to discuss on multiple theories on capital structure and investigate

their similarity and controversy on a theoretical stand, and to provide more empirical

studies to aid solving the controversy of capital structure effects on firm‘s performance,

for firms to make the right choose of capital structure to better maximize their profit.

By the end of this study, we wish to be able to justify the relationship of both debt and

equity on firm performance, to assist us in providing constructive suggestion on how to

improve capital structure.

1.6 SUMMARY

In this chapter, we had discussed the fundamental concept of capital structure and the

purpose for this study. The next chapter shall discuss the multiple theory that associate

with capital structure, and provide empirical example for those theories. This paper aims

to investigate the relationship of capital structure, especially on debt, and its effects on

firm‘s profit performance, by collaborate within multiple literature theories, based on the

empirical result from Malaysia‘s public listed firms.

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Chapter 2: Literature Reviews

In this chapter, we will introduce the findings of previous studies on capital structure, equity

funding, and liability funding, both theoretical literature and empirically.

2.1 CAPITAL STRUCTURE

Modigliani & Miller (1958) was the first to start of the contemporary theory of capital structure.

Since then, many studies on capital structure had been carried out. (John, 2013)

Firm‘s performance is affected by various factors, and capital structure is one of the significant

factors (Salim & Yadav, 2012). Firms can raise capital from two main board categories, namely

equity or liability.

2.1.1 Optimum Capital Structure

Although many theories regarding the capital structure composition were develop over the year,

yet no consensus developed for the optimal composition of capital structure. Raza et al (2013)

suggested that the lack of particular methodology for the optimal composition of capital

structure is because each capital structure emphasizes on different aspects, giving example that

trade-off theory focuses on tax advantages, pecking order theory is based on information

asymmetry while free cash flow theory emphasizes on agency costs. One of the few studies we

found that do actually focus on optimal capital structure is Danis & Rettl (2011) study, where

they develop a simple methodology to identify firms that are at or close to their optimal capital

structure, using tradeoff theory, that is by finding the rebalancing points. As the study aims to

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focus on financially healthy firms, one of its filtering criteria for sample selection is to filter out

firms with negative net income are filter out. Prior to their study, Kim (1978) successfully

derived a simple method to approximate the optimal capital structure with linear bankruptcy

costs.

2.1.2 Capital Structure Theory Controversy

Huang & Song (2006) aim that two widely acknowledged models of capital structure was the

static tradeoff model and the pecking order hypothesis, believes that it is important to test which

hypothesis, tradeoff or pecking order, is more powerful in explaining firms' financing behavior.

However, they found that there is no conclusive test yet, as Shyam-Sunder & Myers (1999)

claim that the tradeoff model can be rejected, which study later rejected by Chirinko & Singha

(2000) by showing that the test conduced generates misleading inferences, and their empirical

evidence can neither evaluate both of the theories. Then, Fama & French (2002) find that both of

the theories cannot be rejected. Booth et al (2001) point out that it is difficult to distinguishing

between these two different models. In addition, Myers (2003) claims that all the capital

structure models are conditional and that ―there is no universal theory of capital structure and no

reason to expect one‖. Finally, Huang & Song themselves found that pecking order theory are

less suitable for China capital market, as China‘s listed companies favour towards external

equity financing over debt, probably due to favorable high stock price, equity financing not

binding or China‘s bond market still at infant stage. In addition, noted that they had group both

tax-based and agency-cost-based models as the subset of tradeoff models, as the theory says

firm‘s optimal capital structure involve the tradeoff among the effects of corporate and personal

taxes, bankruptcy costs and agency costs, etc.

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Even though Myers (2002) claims that there is no universal theory of capital structure, and no

reason to expect one. He does however clarify that ―There are useful conditional theories,

however… Each factor could be dominant for some firms or in some circumstances, yet

unimportant elsewhere‖. Therefore, Frank & Goyal (2003) added the effect of conditioning on

firm circumstances into their study, to address how different theories apply to firms under

different circumstance. Few years later, both Frank & Goyal (2009) collaborate again. In their

study, they argue and explain why the widely held impression on the defect of static trade-off

theory of capital structure was not true. They blame that that widespread is causes the literature

misinterpreted the data. In addition, they also found that more profitable firms experience an

increase in both book equity and the market value of equity, empirically, shows that firms react

as in the trade-off theory and in a trade-off model, financing decisions depend on market

conditions (`market timing').

2.2 EQUITY

Firms, who chose to use equity financing, can choose between internal equity and external

equity.

2.2.1 Internal Equity

Internal finance is an important source of funds. In fact, as much as 71.1% of sources of funds

for all manufacturing firms are from retained earnings account (Fazzari et al. , 1988).

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2.2.2 External Equity

Alternatively, firms may also choose external equity financing. Some examples of ways to raise

funds using external equity include venture capital, initial public offerings (IPOs) or Crave-Out.

However, noted that Asian venture capitals are unique compare to traditional venture capital in

five ways. Firstly, the diverse environment has resulted in the difference of degrees of venture

capital development within Asia. Second, Asian entrepreneurs‘ reluctant to relinquish any form

of control over their business, creates a less attractive environment for traditional venture capital.

Third, venture capital was seen as an economic development tool in Asian, and many

governments took various approaches to influence venture capital development (Lasserre &

Schutte, 1995), including setting up venture capital firms to promote and invest in promoted

specific industries. Thus, it seized the opportunity for traditional venture capital. In addition,

Asian country experience different phase of venture capital market‘s cyclical growth and venture

capital investment in Asia is not primarily based on innovation.

One of the many example how company can raise funds, is through carve-out, also known as a

partial spinoff, is a type of corporate reorganization where parent company sells a minority

(usually 20% or less) stake in a subsidiary for an IPO or rights offering. Allen (1998) examines

the innovative corporate structure of Thermo Electron Corporation. Following the carve-out

strategy, capital was raised to fund additional research and to retain developer of the product by

distributed options on 20,000 shares (less than 3%) of Thermedics. Following the crave-out

strategy, Thermo Electron transformed from a rather poorly-performing firm into an

organization that is proficient in utilizing capital markets, developing new technologies,

decentralizing control and sustaining growth over time. Although it cannot be answered

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definitively whether the firms‘ leaps in performance solely attributed solely to the carve-out

strategy, the approach implemented by the company has created a unique alternative to the

traditional corporate structure.

2.2.3 Inadequate Studies on Capital Structure from Equity Side

In studying about Capital Structure, most research tends to look at it from the liability side, while

paying little did to equity‘s capital influence on firm‘s capital structure. Some of the few capital

structure studies that does emphasize on equity side of capital structure are as below.

2.2.4 High Stock Return Firms, Favour Equity Issuance

One way to decide which financing method to choose is to look at the firms‘ market-to-book

ratio. Hovakimian et al., 2004 suggest that firms with high market-to-book ratio have good

growth opportunities, therefore low target debt ratios. They found that probability of issuing an

equity increase while the probability of issuing debt decreases with market-to-book. In addition,

high stock return are found to increase the probability of equity issuance, however does not

affect the probability of debt issuance.

2.2.5 Capital Structure and Equity Structure are Inverse U Shape Related

with Technical Efficiency

Through its empirical studies of China coal listed companies, Wang & Liu (2009) reveal that

both capital structure and equity structure have inverse U shape with the appraised technical

efficiency.

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2.2.6 Equity Financing Reduces the Risk of Foregoing Profitable Investments

and Accept Losses Inducing Investments

Jackson et al, 2013 suggest that if equity is the source of finance, it is less likely to make

decision errors of foregoing investment that increase firm value, or accept investment that

decrease firm value. These two decision errors are found in debt financing, because relative to

equity financing, debt financing makes managers reluctant to part with assets.

2.3 LIABILITY

Investopedia refers liability as ―a company's legal debts or obligations that arise during the

course of business operations‖.

2.3.1 Types of Liabilities

Debt can be in the form of either bank (private) debt or by public debt (corporate bonds) (Ulph

& Valentini, 2004). Example for interest bearing liabilities are equals short-term and long-term

mortgages, notes, and bonds payable (John & Towery, 2013).

2.3.2 Controversy in Relationship of Debt on Performance

Kebewar & Shah (2013) claims that the impact of debt on corporate profitability can be

explained by three essential theories: signaling theory, tax theory and the agency cost theory.

The signaling theory says that, in the presence of asymmetric information, debts are positively

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correlated to profitability. Next, the agency costs theory held two contradictory effects of debt on

profitability; in the case of agency costs of equity between shareholders and managers, the effect

is positive, however its effect is negative, resulting from the agency costs of debt between

shareholders and lenders. Finally, the influence of taxation is complex and difficult to predict as

it depends on the principles of tax deductibility of interest, income tax and non-debt tax shield.

To sum up, the relationship of debt on profitability is inconsistent in theoretical literature. In

addition, the relationships are inconsistent empirically as well. (Kebewar & Shah, 2012) .

Majumdar & Chhibber (1999), Eriotis et al. (2002), Ngobo & Capiez (2004), Goddard et al.

(2005), Rao et al. (2007), Zeitun & Tian (2007) & Nunes et al. (2009) confirmed a negative

effect of debt on profitability. On the other hand, positive influence was showed by Baum et al.

(2006) & (2007), Berger & Bonaccorsi (2006), Margaritis & Psillaki (2007) & (2010). Some

studies find both effects in their studies (Simerly & Li, 2000), (Mesquita & Lara, 2003) and

(Weill, 2008). Besides that, the presence of a non linear effect (inverse U-shaped relationship)

was found by Berger & Bonaccorsi (2006), Margaritis & Psillaki (2007) and Kebewar (2012).

Finally, a non significant effect was found in Baum et al. (2007) study.

Kebewar & Shah (2012) use panel data to study the relationship of debt ratio on profitability

ratio among 2240 French non listed companies of service sector during 1999-2006. Their result

shows that debt ratio has no effect on corporate profitability regardless of the company size,

using Generalized method of moments (GMM) econometric technique.

2.3.3 Agency Cost Model

In most agency relationships, the principal need to bear monitoring and bonding costs to ensure

that the agent will make optimal decisions from the principal‘s viewpoint. In addition, there will

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be some divergence between the agent‘s decisions and decisions that maximize the principal‘s

welfare; the divergence is then translated to ―residual loss‖. Agency costs are the sum of

monitoring expenditures by the principal, the bonding expenditures by the agent and residual

loss. (Jensen & Meckling, 1976)

Margaritis & Psillaki (2010) findings is consistent agency cost hypothesis, whereby higher

leverage reduces the agency costs, and increases firm value by constraining or encouraging

managers to act more for interests of shareholders. Using a sample of 12,240 New Zealand

firms, Margaritis & Psillaki (2007), added more evidence to support the theoretical predictions

of agency cost model.

In support of the agency cost model, Ofek (1993) results show that highly-leveraged firms are

more likely to respond to short-term decline in performance than do less-leveraged companies,

helping to avoid lengthy periods of losses with no response. He also suggests that this is because

high leverage subjects the firm to the discipline that debt provides.

However, when leverage becomes relatively high, the elevating in the expected costs of financial

distress, bankruptcy, or liquidation may overwhelm the agency costs of external equity. Berger

& Banaccorsi di Patti (2006) findings are consistent with agency cost hypothesis, however it is

not consistent with reversal of the relationship. While Campello (2006) studies across 115

industries for over 30 years, support both model. His results found that moderate debt taking

brings relatively sales gain compare to rivals, however high indebtedness cause product market

to underperform.

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2.3.4 Cost of Capital (Interest Rate)

Coincide with agency cost model and its reversal, Baxter (1967) also founds that initially

leverage increase performance, while high leverage may results in performance drop. However

Baxter analyzes the situation using cost of capital (interest rate), rather than using agency cost

model theory. He tries to explain how excessive leverage can be expected to raise the cost of

capital to the firm. When a firm leverage is very low, increases in debt unlikely exert significant

effects on probability of bankruptcy, thus firms can get loan with low cost. However, the cost of

capital is likely to have a greater effect with every increase in leverage. Firms with excessive

leverage may find themselves experience a sharp increase in interest rate, as the firm capital

structure becomes more risky. Moreover, he suggest that business with relatively stable income

streams (such as utilities) may find it desirable to rely relatively heavily on debt financing, as the

firms‘ low variance of net operating earnings contribute to relatively less cost of capital.

2.3.5 Causality

In agency cost model, we say high leverage increase effectiveness. However, does it work the

opposite way, where efferent firm tends to have higher leverage? Margaritis & Psillaki (2007)

test the reverse causality relationship using quantile regression analysis. They show that the

reverse causality effect is positive from low to mid leverage levels, but negative at high leverage

ratios. In addition, their results shows that firms in the low to middle range of leverage

distribution support of the predictions of the efficiency-risk hypothesis, more efficient firms may

choose higher debt to equity ratios because higher efficiency acts as a buffer for expected costs

of bankruptcy and financial distress.

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2.3.6 Financial Distress Costs

Highly leverage firms face indirect costs of financial distress, thus putting them at a greater

disadvantage as compare to competitors during industry downturns.

Using a large sample size of 10,375 firms in 39 countries, González (2013) ‘s studies indicate

that firms with greater leverage experience significant reduction in performance compared to

their competitors in industry downturns, thus supporting the importance of financial distress

costs. However, the effect of leverage on firm operating performance is not the same in all

countries as it varies with the legal origin and the financial structure and development of

countries.

Opler & Titman (1994)‘s studies also prove that highly leverage firms are in unfavorable

condition during industry downturns, as highly leverage firms may lose substantial market share

and experience lower operating profits than their competitors due to the indirect costs of

financial distress. The relation between leverage and performance tends to be more pronounced

for firms that engage significant research and development (R&D) expenditures and for those in

more concentrated industries.

Tih Koon Tan, study the relationship between financial distress and firm performance during the

Asian Financial Crisis of 1997-1998 using a sample of 277 firms from eight East Asian

economies. His result reaffirm that firms with low financial leverage tend to perform better than

firms with high financial leverage, and highlighted that high leverage firms experience worse

performance during a crisis.

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Gilson (1989) claims that financial distress may motivate managers to manage the firm more

efficiently. However, Gilson does not imply the rise in financial distress is cause by high

leverage. Thus Opler & Titman (1994) argues that financial distress in Gilson's sample may arise

from poor management as well as because of high leverage. Implying that the concept of ‗high

leverage firms motivates the managers to manage capital more effectively, due the financial

distress‘ was not established.

While financial distress can cause significant losses in some cases, but it may also motivate

value-maximizing choices in others. However, the overall costs and benefits of financial distress

are quite difficult to quantify (Opler & Titman (1994).

2.3.7 Comparing the benefits and cost of debts

Although debt increases efficiency as it prevents managers from financing unprofitable projects,

debt may also block some profitable investment opportunities. The optimal debt reflects the

trade-off between the disciplinary benefits of debt and the costs of financial distress. However,

question arises on how do we expect a manager to voluntarily increase the firm‘s leverage, as the

cost of his own discretion? The question was addressed by Harris & Raviv (1988), Stulz (1988),

& especially Zwiebel (1996), by showing how takeover threats prompt manager to increase

leverage.

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2.3.8 Debt drive short-run profit

Chevalier (1995) results shows that Leveraged Buyouts (LBOs) create incentives to raise prices

in order to drive short-run profit. However, this study does not give emphasize on the firms

profit in the long runs.

2.3.9 Relationship of debt and survival probability

Chung et al. (2013) claims that capital structure policy bears little relationship to survival

probability. Firms may increase leverage to support growth or to offset poor performance. While

firms with very high leverage in a year are more likely to fail or be acquired, it is due to the

firms‘ fundamental problem. Increase in leverage is a precursor of failure, and not the cause of

that failure.

2.3.10 Relationship between Leverage and Corporate Performance Varies

Across Countries

Weill (2008) measure performance of medium-sized firms from seven European countries, and

observe that the relationship between leverage and corporate performance varies across countries

across countries (positive in five countries, significantly negative in Italy and not significant in

Portugal). He suggests the access to bank credit for firms, and the efficiency of the legal system

may exert an influence.

Pathak (2011) found that the level of debt has significant negative with firm performance for

Asian countries, but not for Western country. One important reason for this conflict may be due

to the higher cost of borrowing in developing country (Salim & Yadav, 2012).

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2.3.11 Relationship between Leverage and Corporate Performance Varies

Across Industry Competitiveness

Some studies investigate the relationship between capital structure and firm performance, paying

particular attention to the degree of industry competition.

(Fuso, 2013) found that product market competition enhances the performance effect of

leverage. Using the Herfindahl–Hirschman Index and the Boone indicator on 257 South African

firms, he had proven that unconcentrated (competitive) industries significantly benefit from

leverage whilst those in concentrated (uncompetitive) industries are likely to suffer adverse

effects of leverage.

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Chapter 3: Methodology

The objective of this study is to study the relationship of capital structure on profit. This study

will investigate the effect of capital structure on different proxies of profit namely the ROE and

net income. We also investigate whether the different in period of debt will plays a different role

on earnings.

3.1 SAMPLE

Table 1.2 shows all the 30 constituents of FBMKLCI as of 31 December 2013, following the

changes on 17 June 2013 to replace Sapurakencana Petroleum and MISC for Bumi Armada and

YTL Power International.

3.2 MODELS AND VARIABLES

Using ROE (Return on Equity) as a profitability measures, Shubita and Alsawalhah (2012)

examined the relationship between capital structure and profitability among Industrial

Jordanian firms listed on Amman stock Exchange from 2004 to 2009.

Models (1) to (4) as shown in the next page, follow models follows Shubita and Alsawalhah

(2012) regression models with few modifications. We‘ve implemented their models into cross

sectional study to better focus on identifying the characteristics of firms with high performances.

As equity level is part of a firm‘s capital structure decision, we deem that it is important to add

27

equity as a variable in the model. Another changes is, instead of using firm‘s sales as a proxy for

size, we followed Niu (2008) in using natural logarithm of total assets as a proxy for firm‘s size.

However, we are doubtful for using ROE as a sole measurement for profitability, as ROE

measure the efficiency of profitability rather than the total income earned. To investigate how

corporate leverage depends on the structure of corporate assets, Norden and Kampen (2013)

control for profitability by including the logarithm of net income. Following their study to use

natural logarithm of net income as an alternative proxy of profitability, we‘ve constructed

models (4) and (5) based on model (1).

The following equations are our models for this study:

(1)

(2)

(3)

(4)

(5)

(6)

Where:

ROE = Return on Equity = net income / total shareholder equity

NETINCOME = natural logarithm of net inco

TDA = total debt / total asset

28

LDA = long-term debt / total asset

SDA = short-term debt / total asset

DEBT = natural logarithm of total debt

EA = total shareholder equity / total asset

EQUITY = natural logarithm of total shareholder equity

ASSET = natural logarithm of total asset

GROWTHPCT = Sales Growth Percentage = (sales 2013 – sales 2012) / sales 2012

GROWTH = Sales Growth = sales 2013 – sales 2012

ε = Error term

ROE is the amount of net income returned as a percentage of shareholders equity. ROE is useful

in measuring a corporate ability to generate earnings from the money invested.

Debt gives the borrowing party permission to borrow money with the condition of paying back

at a later date, usually with interest. Examples of debt includes bonds, loans, and commercial

paper.

Debt ratio measures the extent of a company‘s leverage. It also refers to the proportion of a

company‘s assets that are financed by debt. The higher a company‘s debt ratio is, the more

leveraged the company and thus greater financial risk. Debt ratios vary widely across industries.

29

Short-term debt refers to the firm‘s current liabilities. This account comprised of any debt

incurred by the company that are due within a year. It is usually made up of company‘s short-

term bank loans.

Long-term debt, known as long-term loans in the U.K., refers to loans and financial obligation

that due in greater than 12-month period.

Equity, is generally refers to the value ownership interest in any assets after all debts associated

with that assets are pay off in finance term. Common equity refers to the outstanding common

stock of a company, while shareholders equity is an account on the balance sheet.

Shareholder equity ratio is a ratio used to help determine how much shareholders would receive

in the event of a company-wide liquidation. This figure represents the amount of assets on which

shareholders have a residual claim.

3.3 OLS (LINEAR RELATIONSHIP)

In statistics, ordinary least squares (OLS) or linear least squares method is use to estimate the

unknown parameters in a linear regression model. In this study, parameters are obtained from

data; OLS is then run to capture the relationship of dependent and independent parameters, by

analyzing their regression and coefficient, as well as to test the significance level of the

relationship to answer our hypothesis in Chapter 1.4.

30

Chapter 4 Discussion

Our results in Table 4.1 show that debt and equity have a negative effect on firm‘s earning

efficiency, the ROE. However, they do not have significantly effect on firm‘s net income. The

sum of equity and leverage, namely total asset, has negative effect on ROE but positive effects

on net income.

4.1 OUTPUT

In this study, we apply cross sectional study to examine the relationship of capital structure and

profits (performance) of public listed firms in Malaysia‘s stock exchange and the output is

presented as Table 4.1. In addition, various diagnostic tests will be performed to check the

robustness of the model. Model (1) to (4) have ROE as dependent variable while Model (5) and

(6) used net income as a measurement of performance.

By applying cross sectional study, we are able to better observe the traits of good performances

firms and bad performances firms respectively. This is because cross sectional analysis rely on

existing differences (rather than changes) between units, to pinpoint the relationship between

parameters rather than looking at how something changes overtime or response to a specific

treatment.

31

Table 4.1 Output Summary

Model 1 2 3 4 5 6

Y

ROE ROE ROE ROE NET

INCOME

NET

INCOM

E

C

C 7.560*** 6.912*** 6.150*** 7.394*** 8.677*** 13.302**

X

TDA -1.000*

-0.546

LDA

-0.862*

-0.995*

SDA

0.356 -0.709

DEBT

-0.050

EA -1.494*** -1.338*** -1.189*** -1.453*** 0.145

EQUITY

0.032

ASSET -0.370*** -0.341*** -0.310*** -0.363*** 0.325*** 0.374*

GROWTHPC

T -0.260 -0.248 -0.280 -0.255 -.0412

GROWTH

-0.342

Basic

n 30 30 30 30 30 28

R2 0.652 0.648 0.600 0.653 0.495 0.524

R 2

Adjusted 0.597 0.592 0.536 0.581 0.415 0.442

SER 0.336 0.338 0.361 0.343 0.537 0.532

F-stat 11.723*** 11.530*** 9.372*** 9.041*** 6.136*** 6.336***

RESET

RESET(1) 92.370*** 29.241*** 28.554*** 95.165*** 1.031 3.601*

RESET(2) 141.76*** 14.352*** 14.682*** 174.60*** 0.832 2.297

Auto:

DW stat 2.371 2.430 2.217 2.396 2.014 1.966

BG LM 0.589 0.711 0.276 0.614 0.401 6.263

Hetero

BPG 4.177** 2.384* 3.118** 4.067*** 0.450 0.844

White 15.059*** 2.723** 27.294*** 222.440*** 0.610 1.190

Normal

JB prob 21.440*** 45.160*** 66.793*** 26.691*** 1.846 1.369

Multi

1.433 1.228 1.490 1.674 1.433 4.077

Notes: ***, **, and * denote significant at 1,5 and 10% respectively.

32

4.2 RESULTS AND DISCUSSION

First, let‘s focus on model (1) to model (4), where ROE is our dependent variables following

Shubita and Alsawalhah (2012) study. All models (1) to (4) have a positive constant. Among

these 4 models, all of the significant independent variables show a negative relationship on

performance. Statistically, both equity and debt have a significant negative effect on firm‘s

performance, with equity ratio at 1% significant, total debt ratio at 10% significant, with long

term debt ratio have a negative effect significant at 10% while short term debt ratio have no

significant effect on firm‘s performance. In addition, the negative effects of equity ratio are

larger than of debt ratio, as equity ratio has a larger negative coefficient. Last but not least, our

result shows no evidence to prove that growth is a factor that will determines performance.

The negative effect of debt on profit performance, is correspond to Majumdar & Chhibber

(1999), Eriotis et al. (2002), Ngobo & Capiez (2004), Goddard et al. (2005), Rao et al. (2007),

Zeitun & Tian (2007) & Nunes et al. (2009) findings, but rejected the positive influcne of debt

on profitability found by Baum et al. (2006) & (2007), Berger & Bonaccorsi (2006), Margaritis

& Psillaki (2007) & (2010).

Contracting to the agency cost theory, whereby higher debt will prompt managers to be more

efficient due to financial distress, our study shows otherwise. Our study shows that more debt

actually reduces firm performance efficiency. We suggest that perhaps ample capital incline to

waste of resources. We are also in support of Pathak (2011) findings that the level of debt has a

significant negative with firm performance for Asian countries. Salim & Yadav(2012) suggest

that this conflict may be due to the higher cost of borrowing in developing country.

33

Highly leverage firms face indirect costs of financial distress, putting them in an greater

disadvantage during economic downturns. In Chapter 2.3.6, we‘ve assessed several studies that

observed a negative relationship between debt and profit performance during economic

downturn. However for the year 2013, most of the sample companies were able to increase their

sales compare to year 2012, with only 8 companies experience a drop in sales. All companies

shows a positive net income for year 2013. In addition, according to the Department of Statistics

of Malaysia, the national Gross Domestic Product increase in 2013 compare to the previous year.

Thus, we conclude that financial distress cost will cause a reduction in performance even if it‘s

not in an economic downturn.

In addition to the effect of debt on performance, we observed that both total debt and long term

debt have a weak significant negative relationship on performance. However, there is no

significant effect of short term debt on firm‘s performances.

In Chapter 2.3.5, we mentioned that Margaritis & Psillaki (2007) test the reverse causality

relationship using quantile regression analysis, and they found that more efficient firms may

choose higher debt to equity ratios because higher efficiency acts as a buffer for expected costs

of bankruptcy and financial distress. However, from the scatter diagram of ROE and ‗total debt

to asset ratio‘ (TDA) in Figure 4.1, and from the scatter diagram of ROE and debt to equity‘

ratio in Figure 4.2, we failed to observe a positive relationship between ROE on either of the

debt ratio. Thus, we are sceptical to their result. However more statistics prove need to be

conducted to draw a more confidence conclusion on their findings.

34

.0

.1

.2

.3

.4

.5

.6

0.0 0.5 1.0 1.5 2.0 2.5 3.0

ROE

TD

A

14

15

16

17

18

19

20

21

0.0 0.5 1.0 1.5 2.0 2.5 3.0

ROE

AS

SE

T

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.5 1.0 1.5 2.0 2.5 3.0

ROE

EA

-.8

-.6

-.4

-.2

.0

.2

.4

.6

0.0 0.5 1.0 1.5 2.0 2.5 3.0

ROE

GR

OW

TH

0

1

2

3

4

5

6

7

8

0.0 0.5 1.0 1.5 2.0 2.5 3.0

ROE

DE

BT

OV

ER

EQ

UIT

Y

Figure 4.2: Scatter diagram of ROE and Debt to Shareholder Equity Ratio for the year 2013

Figure 4.1: Scatter diagram of ROE and its determinant in Model 1 for the year 2013

35

An intriguing pheromone shows in our study, whereby our models on ROE seem likely to be

downward sloping. In model (1) to (4), all significant independent variables shows a negative

sign while others independent variables were mostly negative sign as well, revealing that the

performance function as indicated by ROE may very much be download slopping. We use ROE

as a proxy of performances. ROE is an indicator use to measure how efficient is a firm‘s

managerial level. in using financial resources to generate revenue. We suggest that, perhaps the

reason as to why the performance function is downward slopping in out model is due to at least

one of the followings:

i. Our models left out some positive and important independent variables

ii. Our model highlighted that the key focus to maximize companies‘ performance

efficient are archive though company‘s ability to generate profit with as minimum as

possible of financial resources such as debt, equity and assets.

iii. The performances of sample companies are so sophisticated that they have gone past

the level for maximum efficiency, implying that financial resources may have an

inverted-U effects on performance‘ efficiency.

iv. There‘s a time differential in return, whereby the financial resources currently used

are expected to generate future revenue. Considering a handful of sample companies

are in real estate sector, financial sector and agriculture sector, it is not surprisingly

that these firms revenue come years later than the currently applied resources.

Next, we shift our focus to model (5) and (6), where we use net income as a measurement of

performance rather than ROE. The only independent variable that shows a significant

36

relationship is asset, with coefficient of 0.325 at significant 1% and 0.374 significant at 10% for

model (5) and (6) respectively. Model (5) reveals that debt ratio and equity ratio are insignificant

on net income, while model (6) shows that amount of debt and amount of equity are also

insignificant to net income. As asset is the sum of equity and liability, with liability highly

related to debt and often interchangeable, thus both our result suggest that to boost income,

perhaps the company could consider increasing asset, however, it doesn‘t matter whether the

funding is pool from equity or debt. Therefore, hinting that our study may be in support of MM

theory. However, more statistic prove is necessary to draw the conclusion whether debt and

equity is statistically the same.

Overall, all models from model (1) to model (6), have a positive constant at 1% significant,

ranging from 6.150 to 13.302. In all of our models, growth percentage or growth amount prove

to be insignificant on firm‘s performance.

4.3 DIAGNOSTIC TESTS

F-statistic determines that all of our models are significant. The probabilities of F-statistic are

significant at 1% for all the 6 models, thus there is enough evidence to reject the null hypothesis

that all of the slope coefficients in our zero. We therefore conclude that at least one independent

variable are significant to the dependent variable in each of the model.

Durbin Watson and Breusch-Godfrey Serial Correlation LM Test are applied to test whether

autocorrelation problem exist. Both the Durbin Watson and F-stat for Breusch-Godfrey Serial

Correlation LM Test indicate all 6 models have no autocorrelation problem.

37

All of the centered Variance Inflation Factors (VIF) in model (1) to (5) is less than 3, therefore

there trespass is no multicollinearity problem in the first 5 models. However, in model (6), two

of the values have centered VIF larger than 3, at 4.744 and 7.559. The criteria of VIF varies

across study, with some the model have multicollinearity if VIF more than 3, while other claims

the value to 5 or 10. Therefore we conclude that model (1) to model (5) is safe from

multicollinar problem, while model (6) have multicollinear problem but not serious.

However, our models are not perfect, especially model (1) to (4), as we‘ve noticed several

problems in the model, such misspecification, heteroskedasticity and non-normality. These

problems are not found in model (5) and (6), but model (5) and (6) has a lower R-squared.

Ramsey RESET test is a general specification test for the linear regression model. A drawback

about this test is that it does not tell exactly why the model is rejected. Noted that there is some

misunderstanding regarding RESET test where it is claim that RESET can be used to test for a

multitude of a specification problems, including omitted variables and heteroskedasticity,

however in fact RESET is actually generally a poor test for any of those problems (Wooldridge,

2010). RESET test is just a functional form test.

According to the RESET test, model (1) to (4) has functional mispeciafation error. By manually

dropping one variable at a time and test run, we were able to identify that the error seems to

cause by the asset variable. We‘ve tried changing the power for asset and roe, but still wasn‘t

able to remedy the functional error. We‘ve also tried switching proxy for asset or adding

variables, but none of them are able to pass the RESET test without suffer a drastic drop in R

38

squared. By far, the best remedy we‘ve detected is by substituting dependent variable, ROE to

other proxy such as net income, as shown in model (5).

As shown in Table 4.1 all of our models have heteroskedasticity problem. We use the F-stat of

Breusch-Pagan-Godfrey and White test to determine whether homoskedasticity problem exist in

our models. The results of both test, especially the White test shows that models (1) to (4) have

heteroskedasticity problem. Heteroskedasticity is commonly seen cross sectional and micro

variables (Each individual firm have different background, thus different behaviour/variance.)

Model with heteroskedasticity problem lose the B.L.U.E. feature as variance is incorrect,

however estimators are still unbiased.

The Jarque-Bera statistic reject the null hypothesis of residuals are normally distributed for

model (1) to (4), but fail to reject for model (5) and (6). Therefore, we say that model (1) to (4) is

not normally distributed, while model (5) and (6) are not normally distributed.

In running an OLS, it is sometimes additionally assumed that errors need to have a normal

distribution on the regressors. Buthmann (2010) addresses the six reasons that are frequently to

blame for non-normality, namely:

i. Extreme values,

ii. Overlap of two or more processes,

iii. Insufficient data discrimination,

iv. Sorted data,

v. Values close to zero or a natural limit, and

vi. Data follows a different distribution.

39

In our study, the ROE of most of the sample firm range from 0.032 to 0.293, except the ROE for

Astro Malaysia, British Amer Tabacco and Digi.com Berhad are as high as 0.810, 1.620 and

2.581 respectively. This study focus on all of the 30 companies in FBM KLCI, however in an

econometric point of view, the sample size may be inadequate. FBM KLCI is reviewed semi-

annually by the FTSE Bursa Malaysia Index Advisory Committee to undergo auto-corrective,

thus it may be considered as sorted data. Most of the variables in our models are in ratio or

natural logarithm, thus a lot of them are close to zero. All or some of the problems mention

above may have cause our models‘ residual to become non-normal.

4.4 CONTRIBUTIONS AND IMPLICATIONS

In the literature review, we pointed out several conflicts within various study. We also mention

about the difference climax in Asian venture capitals as compare to the traditional venture

capital in Chapter 2.2.2. Corresponding to that, we suggest Malaysia, or Asian as a whole, to be

more open and welcoming about venture capital by willingly forego a proportion of control over

their business if necessary, and to create a more innovative business. Instead of using venture

capital a tool to develop specific industry, we suggest that venture capital decision should be

based on innovative or the ability to generate profit rather than based on specific industry. Or

perhaps, government should prevent setting up venture capital firms for promoted specific

industries, and leave it up to the market for venture capitalist to invest in business that are most

appealing.

40

Our study does not support the agency cost theory, as we prove that an increase in debt will do

decrease the firm performance. However, we found that an increase in equity will also decrease

firm ability to generate revenue efficiently.

Our findings suggest that to increase profit performance, company should generate revenue

using as minimize as possible of financial resources such as long term debt, equity and asset.

Company should be carefully in planning the usage of financial resources to prevent wasteful.

On the other hand, if a company wishes to acquire more assets to increase its ability to generate

earnings, our result suggest that the decision on proportion of debt and equity does not have

significant effect on earnings.

Following the previous study, we use the increase in sales as compare to previous years as

measurement of grow. However, our results show that growth is not a significant factor on

performance.

Our finding also suggests that the relationship of financial resources on ROE is inverted U

shape. A rational firm will invest in most profitable activity, however as the firm grew larger, the

options to expand the business become more and more narrow, and firms are subject to project

that are not as profitable. Large firm that gone pass the optimum ROE will start to experience a

decreasing in ROE. Although it is possible to generate profit even though ROE is decreasing, the

firm earning ability is not as efficient as before. As firms within FBM KLCI are proven to have a

downward slopping ROE, we believe they should enter new industry or to open new market in

overseas, rather than continuing investing their current business locally.

41

Chapter 5 Conclusion

5.1 Conclusion While funds play an important role in firm, there are very few studies on findings focus on

findings the optimum capital structure. In addition, there is very few studies that focus on the

study of equity in capital structure, as most study only focus on debt. We also notice that there

are many conflicts within those studies.

In our model, we use ROE as an indicator of firm‘s performance, with 4 independent variables

namely debt, equity, asset and growth. We use debt to asset ratio as a proxy for debt, while

equity to asset ratio to represent equity, and we uses natural logarithm on total asset, finally we

measure the sales growth percentage as compare to previous year to indicate growth. For

comparison purposes, we‘ve also substitute net income with ROE to help us identify which

model is better.

Using all 30 companies within the FBM KLCI, this study aims to provide suggestion to improve

capital structure to help companies to generate revenue more efficiently. In order to achieve that,

we set out our objectives to examine the relation of both debt and equity on firms‘ performance

and provide more empirically result to help draw conclusion on agency cost model.

Our findings show that both total debt and equity shows a negative relationship on profit

performance, significant at 10% and 1% respectively. However, they does not seem to have any

significant effect on amount of income generated.

42

While agency cost model state that more debt will motivate managers to perform more

efficiently with additional pressure, our findings doesn‘t seems to be in support of the theory.

We found that more debt will decrease performance. It seems that financial distress is an

additional cost more than motivation for the manager level from the additional monitoring cause

by debt.

Our findings shows that total debt, equity and asset all have a significantly negative relationship

to firms ‗earnings efficiency, significant and 10%, 1% and 1% respectively. This suggest that

companies should generate profit with as minimum as financial resources as possible and avoid

raising funds as the only means to improve profit.

On the other hand, we‘ve also found that asset can increase net income. Thus if a company

wishes to increase its profit, then it should consider increase the firms‘ asset. As debt and equity

is insignificant to net income, it indicates that it does not matter as to whether the company raise

asset through debt or equity.

Our study also found that growth, are sales growth specially, have no significant effect on firm

profit performance efficiency.

Lastly, we‘ve observed a downward slopping performance, and we believe more investigation is

necessary to study why all independent variables in our model have a negative coefficient.

43

5.2 LIMITATIONS AND SUGGESTIONS FOR FURTHER STUDY

In this study, we‘ve used ROE as a proxy of profit performance. A decreasing ROE doesn‘t

necessary means that a firm profit is decreasing; it merely means that the firm is less efficient in

generating revenue. In addition, as pointed out by Gill (2012) in Forbes, ROE can be artificially

increase by company buying back shares or increase debt, thus making ROE a misleading

indicator. Therefore, we suggest using net income as a proxy of profit performance.

Some of the previous studies expect a non-linear relationship of debt on performance. However

we did not test the existence of non-linearity due to the limitation in our ability.

This study uses all companies in FBMKLCI as our sample size. As the FBMKLCI only contains

30 companies, thus our sample size are only limited to 30 companies. However, we are

determined to uses companies in FBM KLCI as our sample size to determine the key to

successful capital structure of big firms. Hence, if the focus of any upcoming study is to

investigate the relationship rather than contribute to the findings of optimum capital structure,

we strongly suggest expanding the sample size within all public listed companies. Alternatively,

if any further research wish to focus on findings the best capital structure approach, we suggest

filtering company with positive earnings for analysing.

Due to the limited sample size, we are reluctant to divide sample companies based on sectors. As

sectors is used as an parameters in many previous study, any further study that have enough

sample size should consider dividing companies into groups based on their respective sector

before further analysis.

44

Tax is uses as parameters in some study, especially for cross country analysis. For simplicity, as

our study sample size s constrain within just one country, therefore we did not include tax as a

variables within our study.

Our finding shows that, growth is not a significant determines of firm‘s performance. Therefore

further study should consider dropping this variable or to replace this variable with others proxy.

Some of the previous study raises investigate on the causality of debt and earnings ability, as it is

expected that the ability to borrow is based on the firms‘ earnings ability. However, for this

study, we fail to run a causality test due to time limitation.

Most of the previous study about capital structure focuses on debt, and seldom highlight on

equity. Although we do add in equity as a variable in our study, the lack of information from

previous studies making it hard for us to draw conclusion. There should be more study that

investigates about the role of equity in capital structure, observing the effects of internal and

external equities separately. In addition, there also very few studies that attempt to find the

optimum capital structure, thus we encourage more studies to contribute to the solving of

findings the optimum capital structure.

Initially, this study was set-foot to provide research for start-up or small and medium enterprises

(SMEs). However, we are forced to shift our focus to public listed company due to the lack of

45

data for start-up and SMEs. Hence, we recommend to research on start-up and SMEs for

national with ample data, or alternatively, to conduct qualitative survey instead of using

secondary data.

Last but not least, we notice many research focus on the research of the relationship rather than

suggestive measurement on ways to improve capital structure. We would like to take this

opportunity to urge further study to contribute to the solution of optimum capital structure. In

addition, we also notice most research uses public listed company as sample company. As we

strongly believe small firms need guidance and lacking the internal resources to the research, we

sincerely hope that more studies focus on depicting capital structure for small firms to guide and

enlighten them. This of course, should be supported by statistic department to made data about

small firms available.

46

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Appendices

APPENDIX 1: COMPANIES’ DATA

Source: Thomson Reuters datastream

(1) (2) (3) (4) (5) (6) (7) = (2)-(3)

NameNet income available to

common (RM)

Total Liabilities & Shareholdere

equity (RM)Total Liabilites (RM) Total Debt (RM) Long Term Debt (RM)

Short Term Debt &

Current Port (RM)

Total Shareholder Equity

(RM)

AMMB HOLDINGS BERHAD 1,635,146.00 126,857,046.00 113,723,816.00 15,578,947.00 4,205,232.00 11,373,715.00 13,133,230.00

ASTRO MALAYSIA 418,000.00 6,496,559.00 5,980,467.00 3,681,600.00 3,556,400.00 125,228.00 516,092.00

AXIATA GROUP 2,550,021.00 43,255,291.00 21,876,220.00 13,436,375.00 12,299,630.00 1,136,745.00 21,379,071.00

BRITISH AMER TOBACCO 823,440.00 1,360,259.00 851,927.00 510,000.00 - 510,000.00 508,332.00

CIMB GROUP HOLDIN 4,540,403.00 370,555,547.00 339,326,987.00 54,827,772.00 28,177,139.00 26,650,633.00 31,228,560.00

DIGI.COM BERHAD 1,705,878.00 3,752,190.00 3,091,191.00 749,326.00 445,869.00 303,457.00 660,999.00

FELDA GLOB 980,992.00 19,452,923.00 10,508,386.00 4,347,701.00 2,485,630.00 1,862,071.00 8,944,537.00

GENTING BERHAD 1,810,066.00 71,224,840.00 26,637,807.00 19,370,992.00 16,809,644.00 2,561,348.00 44,587,033.00

RESORTS WORLD BHD 1,602,995.00 19,677,410.00 4,199,809.00 1,679,920.00 1,482,608.00 197,312.00 15,477,601.00

HONG LEONG BANK BHD 1,856,272.00 163,585,697.00 150,549,073.00 20,401,345.00 6,284,774.00 14,116,571.00 13,036,624.00

HONG LEONG FIN 1,487,690.00 180,473,145.00 165,468,437.00 28,103,455.00 11,020,594.00 17,082,861.00 15,004,708.00

IHH HEALTHCARE 631,159.00 27,183,712.00 7,260,765.00 4,461,281.00 4,170,246.00 291,035.00 19,922,947.00

IOI CORPORATION BHD 1,970,100.00 23,844,400.00 9,892,400.00 7,324,300.00 7,104,900.00 219,400.00 13,952,000.00

KUALA LUMPUR KEPONG 917,743.00 11,644,601.00 3,691,361.00 2,335,352.00 1,558,227.00 777,125.00 7,953,240.00

MALAYAN BANKING BHD 6,552,391.00 558,781,295.00 511,038,696.00 74,392,606.00 25,966,381.00 48,426,225.00 47,742,599.00

MAXIS BHD 1,765,000.00 17,202,000.00 11,186,000.00 7,552,000.00 6,642,000.00 910,000.00 6,016,000.00

MISC BHD 2,085,377.00 40,166,812.00 14,409,441.00 10,218,828.00 6,826,205.00 3,392,623.00 25,757,371.00

PETRONAS CHEMICALS 3,146,000.00 27,273,000.00 3,884,000.00 - - - 23,389,000.00

PETRONAS DAGANGAN 811,753.00 10,159,669.00 5,330,187.00 582,638.00 139,580.00 443,058.00 4,829,482.00

PETRONAS GAS BERHAD 2,078,888.00 12,619,370.00 2,353,823.00 841,792.00 824,061.00 17,731.00 10,265,547.00

PPB GROUP BHD 994,219.00 17,073,379.00 869,836.00 419,553.00 89,698.00 329,855.00 16,203,543.00

PUBLIC BANK BHD 4,064,683.00 305,655,275.00 284,458,079.00 28,145,588.00 10,396,309.00 17,749,279.00 21,197,196.00

RHB CAPITAL BERHAD 1,831,190.00 191,058,682.00 174,115,955.00 27,325,703.00 9,728,993.00 17,596,710.00 16,942,727.00

SAPURAKENCANA 524,596.00 15,152,889.00 8,409,980.00 5,940,972.00 3,805,776.00 2,135,196.00 6,742,909.00

SIME DARBY BHD 3,700,600.00 47,534,100.00 19,553,000.00 10,249,900.00 8,151,200.00 2,098,700.00 27,981,100.00

TELEKOM MALAYSIA BHD 1,012,200.00 21,127,200.00 13,827,900.00 6,455,200.00 4,865,000.00 1,590,200.00 7,299,300.00

TENAGA NASIONAL BHD 4,614,200.00 99,025,700.00 63,634,800.00 29,482,400.00 27,648,200.00 1,834,200.00 35,390,900.00

UEM SUNRISE 579,141.00 9,675,007.00 3,205,391.00 1,940,049.00 1,722,066.00 217,983.00 6,469,616.00

UMW HOLDINGS BERHAD 681,237.00 14,754,058.00 5,777,436.00 3,019,567.00 1,602,246.00 1,417,321.00 8,976,622.00

YTL CORPORATION BHD 1,274,494.00 53,619,494.00 38,061,749.00 30,742,068.00 26,514,811.00 4,227,257.00 15,557,745.00

59

APPENDIX 1: Companies’ Data (Cont’d)

Source: Thomson Reuters datastream

(8) (9) (10) (11) = (10) - (9)

Name Total Assets (RM) Net Sales or Revenues in 2012 (RM) Net Sales or Revenues in 2013 (RM) SALES GROWTH (RM)

AMMB HOLDINGS BERHAD 126,857,046.00 6,356,040.00 7,908,130.00 1552090.00

ASTRO MALAYSIA 6,496,559.00 3,846,677.00 4,264,967.00 418290.00

AXIATA GROUP 43,255,291.00 17,651,617.00 18,370,841.00 719224.00

BRITISH AMER TOBACCO 1,360,259.00 4,364,786.00 4,517,222.00 152436.00

CIMB GROUP HOLDIN 370,555,547.00 19,676,149.00 20,869,787.00 1193638.00

DIGI.COM BERHAD 3,752,190.00 6,360,913.00 6,733,411.00 372498.00

FELDA GLOB 19,452,923.00 12,886,499.00 12,568,008.00 -318491.00

GENTING BERHAD 71,224,840.00 17,258,500.00 17,111,661.00 -146839.00

RESORTS WORLD BHD 19,677,410.00 7,892,900.00 8,327,537.00 434637.00

HONG LEONG BANK BHD 163,585,697.00 6,877,066.00 6,917,822.00 40756.00

HONG LEONG FIN 180,473,145.00 7,252,837.00 7,520,642.00 267805.00

IHH HEALTHCARE 27,183,712.00 6,981,942.00 6,756,451.00 -225491.00

IOI CORPORATION BHD 23,844,400.00 15,640,272.00 12,198,500.00 -3441772.00

KUALA LUMPUR KEPONG 11,644,601.00 10,067,249.00 9,147,325.00 -919924.00

MALAYAN BANKING BHD 558,781,295.00 27,971,308.00 25,259,551.00 -2711757.00

MAXIS BHD 17,202,000.00 8,966,828.00 9,084,000.00 117172.00

MISC BHD 40,166,812.00 9,484,003.00 8,971,805.00 -512198.00

PETRONAS CHEMICALS 27,273,000.00 16,599,000.00 15,202,000.00 -1397000.00

PETRONAS DAGANGAN 10,159,669.00 29,514,963.00 32,341,922.00 2826959.00

PETRONAS GAS BERHAD 12,619,370.00 3,576,771.00 3,892,139.00 315368.00

PPB GROUP BHD 17,073,379.00 3,017,926.00 3,312,917.00 294991.00

PUBLIC BANK BHD 305,655,275.00 12,865,954.00 13,899,449.00 1033495.00

RHB CAPITAL BERHAD 191,058,682.00 7,996,226.00 2,676,277.00 -5319949.00

SAPURAKENCANA 15,152,889.00 4,672,610.00 6,912,414.00 2239804.00

SIME DARBY BHD 47,534,100.00 47,602,300.00 46,812,300.00 -790000.00

TELEKOM MALAYSIA BHD 21,127,200.00 9,993,500.00 10,628,700.00 635200.00

TENAGA NASIONAL BHD 99,025,700.00 35,848,400.00 37,130,700.00 1282300.00

UEM SUNRISE 9,675,007.00 1,939,676.00 2,425,289.00 485613.00

UMW HOLDINGS BERHAD 14,754,058.00 15,863,617.00 14,206,870.00 -1656747.00

YTL CORPORATION BHD 53,619,494.00 20,195,789.00 19,972,948.00 -222841.00

60

APPENDIX 1: Companies’ Data (Cont’d)

Source: Thomson Reuters datastream

(12) = (1) / (7) (13)= ln(1) (14) = (4) / (9) (15) = (5) / (9) (16)= (6) / (9) (17)= In(4) (18) = (7) / (9) (19)=In(7) (20) = [ (10)-(9) ] / (9) (21) = In [ (11) + 5319949 ]

Name ROE NETINCOME TDA LDA SDA DEBT EA EQUITY GROWTHPCT GROWTH

AMMB HOLDINGS BERHAD 0.01 14.31 0.12 0.03 0.09 16.56 0.10 16.39 0.24 15.74

ASTRO MALAYSIA 0.01 12.94 0.57 0.55 0.02 15.12 0.08 13.15 0.11 15.56

AXIATA GROUP 0.03 14.75 0.31 0.28 0.03 16.41 0.49 16.88 0.04 15.61

BRITISH AMER TOBACCO 0.02 13.62 0.37 - 0.37 13.14 0.37 13.14 0.03 15.52

CIMB GROUP HOLDIN 0.01 15.33 0.15 0.08 0.07 17.82 0.08 17.26 0.06 15.69

DIGI.COM BERHAD 0.01 14.35 0.20 0.12 0.08 13.53 0.18 13.40 0.06 15.55

FELDA GLOB 0.03 13.80 0.22 0.13 0.10 15.29 0.46 16.01 -0.02 15.43

GENTING BERHAD 0.04 14.41 0.27 0.24 0.04 16.78 0.63 17.61 -0.01 15.46

RESORTS WORLD BHD 0.05 14.29 0.09 0.08 0.01 14.33 0.79 16.55 0.06 15.57

HONG LEONG BANK BHD 0.01 14.43 0.12 0.04 0.09 16.83 0.08 16.38 0.01 15.49

HONG LEONG FIN 0.01 14.21 0.16 0.06 0.09 17.15 0.08 16.52 0.04 15.54

IHH HEALTHCARE 0.05 13.36 0.16 0.15 0.01 15.31 0.73 16.81 -0.03 15.44

IOI CORPORATION BHD 0.04 14.49 0.31 0.30 0.01 15.81 0.59 16.45 -0.22 14.45

KUALA LUMPUR KEPONG 0.04 13.73 0.20 0.13 0.07 14.66 0.68 15.89 -0.09 15.30

MALAYAN BANKING BHD 0.01 15.70 0.13 0.05 0.09 18.12 0.09 17.68 -0.10 14.77

MAXIS BHD 0.02 14.38 0.44 0.39 0.05 15.84 0.35 15.61 0.01 15.51

MISC BHD 0.04 14.55 0.25 0.17 0.08 16.14 0.64 17.06 -0.05 15.39

PETRONAS CHEMICALS 0.06 14.96 - - - #NUM! 0.86 16.97 -0.08 15.18

PETRONAS DAGANGAN 0.03 13.61 0.06 0.01 0.04 13.28 0.48 15.39 0.10 15.91

PETRONAS GAS BERHAD 0.05 14.55 0.07 0.07 0.00 13.64 0.81 16.14 0.09 15.54

PPB GROUP BHD 0.06 13.81 0.02 0.01 0.02 12.95 0.95 16.60 0.10 15.54

PUBLIC BANK BHD 0.00 15.22 0.09 0.03 0.06 17.15 0.07 16.87 0.08 15.66

RHB CAPITAL BERHAD #NUM! 14.42 0.14 0.05 0.09 17.12 0.09 16.65 -0.67 #NUM!

SAPURAKENCANA 0.03 13.17 0.39 0.25 0.14 15.60 0.44 15.72 0.48 15.84

SIME DARBY BHD 0.04 15.12 0.22 0.17 0.04 16.14 0.59 17.15 -0.02 15.33

TELEKOM MALAYSIA BHD 0.02 13.83 0.31 0.23 0.08 15.68 0.35 15.80 0.06 15.60

TENAGA NASIONAL BHD 0.02 15.34 0.30 0.28 0.02 17.20 0.36 17.38 0.04 15.70

UEM SUNRISE 0.04 13.27 0.20 0.18 0.02 14.48 0.67 15.68 0.25 15.57

UMW HOLDINGS BERHAD 0.04 13.43 0.20 0.11 0.10 14.92 0.61 16.01 -0.10 15.11

YTL CORPORATION BHD 0.02 14.06 0.57 0.49 0.08 17.24 0.29 16.56 -0.01 15.44

61

APPENDIX 2: Diagnostic Checking

MODEL 1

Dependent Variable: ROE

Method: Least Squares

Date: 05/19/14 Time: 08:53

Sample: 1 30

Included observations: 30

Variable Coefficient Std. Error t-Statistic Prob.

C 7.559948 1.140774 6.627031 0.0000

TDA -0.999295 0.508046 -1.966937 0.0604

EA -1.493948 0.288605 -5.176442 0.0000

ASSET -0.369956 0.058000 -6.378603 0.0000

GROWTHPCT -0.260142 0.358666 -0.725303 0.4750

R-squared 0.652247 Mean dependent var 0.276257

Adjusted R-squared 0.596607 S.D. dependent var 0.529206

S.E. of regression 0.336116 Akaike info criterion 0.808290

Sum squared resid 2.824346 Schwarz criterion 1.041823

Log likelihood -7.124348 Hannan-Quinn criter. 0.882999

F-statistic 11.72255 Durbin-Watson stat 2.371055

Prob(F-statistic) 0.000017

Ramsey RESET Test

Equation: EQ1

Specification: ROE C TDA EA ASSET GROWTHPCT

Omitted Variables: Squares of fitted values

Value df Probability

t-statistic 9.610888 24 0.0000

F-statistic 92.36917 (1, 24) 0.0000

Likelihood ratio 47.36141 1 0.0000

Ramsey RESET Test

Equation: EQ1

Specification: ROE C TDA EA ASSET GROWTHPCT

Omitted Variables: Powers of fitted values from 2 to 3

Value df Probability

F-statistic 141.7564 (2, 23) 0.0000

Likelihood ratio 77.69296 2 0.0000

62

APPENDIX 2: Diagnostic Checking (Cont’d)

Breusch-Godfrey Serial Correlation LM Test:

F-statistic 0.588625 Prob. F(2,23) 0.5632

Obs*R-squared 1.460774 Prob. Chi-Square(2) 0.4817

Heteroskedasticity Test: Breusch-Pagan-Godfrey

F-statistic 4.176648 Prob. F(4,25) 0.0100

Obs*R-squared 12.01723 Prob. Chi-Square(4) 0.0172

Scaled explained SS 22.64021 Prob. Chi-Square(4) 0.0001

Heteroskedasticity Test: White

F-statistic 15.05870 Prob. F(14,15) 0.0000

Obs*R-squared 28.00728 Prob. Chi-Square(14) 0.0142

Scaled explained SS 52.76512 Prob. Chi-Square(14) 0.0000

Variance Inflation Factors

Date: 05/19/14 Time: 09:29

Sample: 1 30

Included observations: 30

Coefficient Uncentered Centered

Variable Variance VIF VIF

C 1.301366 345.5755 NA

TDA 0.258111 4.736337 1.362803

EA 0.083293 5.744013 1.602619

ASSET 0.003364 270.0076 1.694312

GROWTHPCT 0.128642 1.080169 1.072806

Covariance Matrix

C TDA EA ASSET GROWTHPCT

C 1.301366 -0.312682 -0.216279 -0.065404 -0.076913

TDA -0.312682 0.258111 0.065886 0.013098 -0.006768

EA -0.216279 0.065886 0.083293 0.009553 0.003884

ASSET -0.065404 0.013098 0.009553 0.003364 0.004319

GROWTHPCT -0.076913 -0.006768 0.003884 0.004319 0.128642

0

2

4

6

8

10

12

14

-0.75 -0.50 -0.25 0.00 0.25 0.50 0.75 1.00 1.25

Series: ResidualsSample 1 30Observations 30

Mean -1.44e-16Median 0.014129Maximum 1.099124Minimum -0.630634Std. Dev. 0.312076Skewness 1.163584Kurtosis 6.425859

Jarque-Bera 21.44028Probability 0.000022

63

APPENDIX 2: Diagnostic Checking (Cont’d)

MODEL 2

Dependent Variable: ROE

Method: Least Squares

Date: 05/19/14 Time: 08:59

Sample: 1 30

Included observations: 30

Variable Coefficient Std. Error t-Statistic Prob.

C 6.911510 1.010623 6.838860 0.0000

LDA -0.862177 0.457002 -1.886594 0.0709

EA -1.338177 0.264512 -5.059046 0.0000

ASSET -0.341491 0.053550 -6.377107 0.0000

GROWTHPCT -0.248038 0.360928 -0.687223 0.4983

R-squared 0.648478 Mean dependent var 0.276257

Adjusted R-squared 0.592234 S.D. dependent var 0.529206

S.E. of regression 0.337933 Akaike info criterion 0.819072

Sum squared resid 2.854964 Schwarz criterion 1.052605

Log likelihood -7.286085 Hannan-Quinn criter. 0.893782

F-statistic 11.52980 Durbin-Watson stat 2.429704

Prob(F-statistic) 0.000019

Ramsey RESET Test

Equation: EQ2

Specification: ROE C LDA EA ASSET GROWTHPCT

Omitted Variables: Squares of fitted values

Value df Probability

t-statistic 5.407509 24 0.0000

F-statistic 29.24115 (1, 24) 0.0000

Likelihood ratio 23.90334 1 0.0000

Ramsey RESET Test

Equation: EQ2

Specification: ROE C LDA EA ASSET GROWTHPCT

Omitted Variables: Powers of fitted values from 2 to 3

Value df Probability

F-statistic 14.35243 (2, 23) 0.0001

Likelihood ratio 24.30172 2 0.0000

64

APPENDIX 2: Diagnostic Checking (Cont’d)

Breusch-Godfrey Serial Correlation LM Test:

F-statistic 0.710906 Prob. F(2,23) 0.5017

Obs*R-squared 1.746568 Prob. Chi-Square(2) 0.4176

Heteroskedasticity Test: Breusch-Pagan-Godfrey

F-statistic 2.383631 Prob. F(4,25) 0.0784

Obs*R-squared 8.282603 Prob. Chi-Square(4) 0.0818

Scaled explained SS 20.38379 Prob. Chi-Square(4) 0.0004

Heteroskedasticity Test: White

F-statistic 2.722730 Prob. F(14,15) 0.0319

Obs*R-squared 21.52833 Prob. Chi-Square(14) 0.0888

Scaled explained SS 52.98201 Prob. Chi-Square(14) 0.0000

Variance Inflation Factors

Date: 05/19/14 Time: 09:31

Sample: 1 30

Included observations: 30

Coefficient Uncentered Centered

Variable Variance VIF VIF

C 1.021359 268.3113 NA

LDA 0.208851 2.404002 1.075319

EA 0.069966 4.773253 1.331770

ASSET 0.002868 227.6958 1.428804

GROWTHPCT 0.130269 1.082103 1.074727

Covariance Matrix

C LDA EA ASSET GROWTHPCT

C 1.021359 -0.136169 -0.153628 -0.053587 -0.079984

LDA -0.136169 0.208851 0.024057 0.005389 -0.009279

EA -0.153628 0.024057 0.069966 0.006898 0.004603

ASSET -0.053587 0.005389 0.006898 0.002868 0.004473

GROWTHPCT -0.079984 -0.009279 0.004603 0.004473 0.130269

0

2

4

6

8

10

12

14

16

-0.75 -0.50 -0.25 0.00 0.25 0.50 0.75 1.00 1.25

Series: ResidualsSample 1 30Observations 30

Mean 2.14e-16Median 0.040718Maximum 1.191401Minimum -0.562118Std. Dev. 0.313763Skewness 1.600146Kurtosis 8.087785

Jarque-Bera 45.15928Probability 0.000000

65

APPENDIX 2: Diagnostic Checking (Cont’d)

MODEL 3

Dependent Variable: ROE

Method: Least Squares

Date: 05/19/14 Time: 09:01

Sample: 1 30

Included observations: 30

Variable Coefficient Std. Error t-Statistic Prob.

C 6.150174 1.219342 5.043846 0.0000

SDA 0.356085 1.165883 0.305421 0.7626

EA -1.188988 0.321166 -3.702101 0.0011

ASSET -0.310362 0.062858 -4.937490 0.0000

GROWTHPCT -0.279859 0.385024 -0.726861 0.4741

R-squared 0.599924 Mean dependent var 0.276257

Adjusted R-squared 0.535912 S.D. dependent var 0.529206

S.E. of regression 0.360516 Akaike info criterion 0.948453

Sum squared resid 3.249300 Schwarz criterion 1.181985

Log likelihood -9.226788 Hannan-Quinn criter. 1.023162

F-statistic 9.372043 Durbin-Watson stat 2.216553

Prob(F-statistic) 0.000090

Ramsey RESET Test

Equation: EQ3

Specification: ROE C SDA EA ASSET GROWTHPCT

Omitted Variables: Squares of fitted values

Value df Probability

t-statistic 5.343596 24 0.0000

F-statistic 28.55402 (1, 24) 0.0000

Likelihood ratio 23.51363 1 0.0000

Ramsey RESET Test

Equation: EQ3

Specification: ROE C SDA EA ASSET GROWTHPCT

Omitted Variables: Powers of fitted values from 2 to 3

Value df Probability

F-statistic 14.68152 (2, 23) 0.0001

Likelihood ratio 24.68120 2 0.0000

66

APPENDIX 2: Diagnostic Checking (Cont’d)

Breusch-Godfrey Serial Correlation LM Test:

F-statistic 0.275409 Prob. F(2,23) 0.7617

Obs*R-squared 0.701654 Prob. Chi-Square(2) 0.7041

Heteroskedasticity Test: Breusch-Pagan-Godfrey

F-statistic 3.117925 Prob. F(4,25) 0.0328

Obs*R-squared 9.984896 Prob. Chi-Square(4) 0.0407

Scaled explained SS 28.28097 Prob. Chi-Square(4) 0.0000

Heteroskedasticity Test: White

F-statistic 27.29428 Prob. F(14,15) 0.0000

Obs*R-squared 28.86684 Prob. Chi-Square(14) 0.0109

Scaled explained SS 81.76170 Prob. Chi-Square(14) 0.0000

Variance Inflation Factors

Date: 05/19/14 Time: 09:31

Sample: 1 30

Included observations: 30

Coefficient Uncentered Centered

Variable Variance VIF VIF

C 1.486795 343.1806 NA

SDA 1.359283 2.806382 1.429936

EA 0.103147 6.182920 1.725077

ASSET 0.003951 275.6644 1.729809

GROWTHPCT 0.148244 1.081968 1.074593

Covariance Matrix

C SDA EA ASSET GROWTHPCT

C 1.486795 -0.760426 -0.263513 -0.075958 -0.111763

SDA -0.760426 1.359283 0.190402 0.033907 0.024750

EA -0.263513 0.190402 0.103147 0.011893 0.009923

ASSET -0.075958 0.033907 0.011893 0.003951 0.005981

GROWTHPCT -0.111763 0.024750 0.009923 0.005981 0.148244

0

2

4

6

8

10

12

14

-0.50 -0.25 0.00 0.25 0.50 0.75 1.00 1.25 1.50

Series: ResidualsSample 1 30Observations 30

Mean 5.96e-16Median -0.028235Maximum 1.325849Minimum -0.398253Std. Dev. 0.334731Skewness 1.969969Kurtosis 9.157238

Jarque-Bera 66.79336Probability 0.000000

67

APPENDIX 2: Diagnostic Checking (Cont’d)

MODEL 4

Dependent Variable: ROE

Method: Least Squares

Date: 05/19/14 Time: 09:03

Sample: 1 30

Included observations: 30

Variable Coefficient Std. Error t-Statistic Prob.

C 7.394647 1.327573 5.570050 0.0000

LDA -0.994891 0.518087 -1.920318 0.0668

SDA -0.708949 1.238924 -0.572229 0.5725

EA -1.452770 0.334670 -4.340908 0.0002

ASSET -0.362600 0.065632 -5.524710 0.0000

GROWTHPCT -0.255051 0.366088 -0.696692 0.4927

R-squared 0.653209 Mean dependent var 0.276257

Adjusted R-squared 0.580961 S.D. dependent var 0.529206

S.E. of regression 0.342572 Akaike info criterion 0.872188

Sum squared resid 2.816537 Schwarz criterion 1.152427

Log likelihood -7.082815 Hannan-Quinn criter. 0.961839

F-statistic 9.041190 Durbin-Watson stat 2.395856

Prob(F-statistic) 0.000062

Ramsey RESET Test

Equation: EQ4

Specification: ROE C LDA SDA EA ASSET GROWTHPCT

Omitted Variables: Squares of fitted values

Value df Probability

t-statistic 9.755234 23 0.0000

F-statistic 95.16460 (1, 23) 0.0000

Likelihood ratio 49.09753 1 0.0000

Ramsey RESET Test

Equation: EQ4

Specification: ROE C LDA SDA EA ASSET GROWTHPCT

Omitted Variables: Powers of fitted values from 2 to 3

Value df Probability

F-statistic 174.5949 (2, 22) 0.0000

Likelihood ratio 84.77013 2 0.0000

68

APPENDIX 2: Diagnostic Checking (Cont’d)

Breusch-Godfrey Serial Correlation LM Test:

F-statistic 0.615669 Prob. F(2,22) 0.5493

Obs*R-squared 1.590099 Prob. Chi-Square(2) 0.4516

Heteroskedasticity Test: Breusch-Pagan-Godfrey

F-statistic 4.067363 Prob. F(5,24) 0.0081

Obs*R-squared 13.76067 Prob. Chi-Square(5) 0.0172

Scaled explained SS 25.80337 Prob. Chi-Square(5) 0.0001

Heteroskedasticity Test: White

F-statistic 222.4398 Prob. F(20,9) 0.0000

Obs*R-squared 29.93943 Prob. Chi-Square(20) 0.0708

Scaled explained SS 56.14101 Prob. Chi-Square(20) 0.0000

Variance Inflation Factors

Date: 05/19/14 Time: 09:32

Sample: 1 30

Included observations: 30

Coefficient Uncentered Centered

Variable Variance VIF VIF

C 1.762450 450.5405 NA

LDA 0.268414 3.006493 1.344815

SDA 1.534933 3.509718 1.788307

EA 0.112004 7.435560 2.074572

ASSET 0.004308 332.8410 2.088595

GROWTHPCT 0.134021 1.083317 1.075933

Covariance Matrix

C LDA SDA EA ASSET GROWTHPCT

C 1.762450 -0.335749 -1.046032 -0.326954 -0.086213 -0.092542

LDA -0.335749 0.268414 0.287338 0.071166 0.014093 -0.006693

SDA -1.046032 0.287338 1.534933 0.248104 0.045702 0.015183

EA -0.326954 0.071166 0.248104 0.112004 0.014475 0.007185

ASSET -0.086213 0.014093 0.045702 0.014475 0.004308 0.005049

GROWTHPCT -0.092542 -0.006693 0.015183 0.007185 0.005049 0.134021

0

2

4

6

8

10

12

14

16

-0.75 -0.50 -0.25 0.00 0.25 0.50 0.75 1.00 1.25

Series: ResidualsSample 1 30Observations 30

Mean -1.07e-15Median 0.022239Maximum 1.121510Minimum -0.616802Std. Dev. 0.311644Skewness 1.270254Kurtosis 6.859853

Jarque-Bera 26.69081Probability 0.000002

69

APPENDIX 2: Diagnostic Checking (Cont’d)

MODEL 5

Dependent Variable: INCOME

Method: Least Squares

Date: 05/22/14 Time: 10:54

Sample: 1 30

Included observations: 30

Variable Coefficient Std. Error t-Statistic Prob.

C 8.677275 1.821400 4.764070 0.0001

TDA -0.546024 0.811164 -0.673136 0.5070

EA 0.144795 0.460797 0.314228 0.7560

ASSET 0.325155 0.092604 3.511242 0.0017

GROWTHPCT -0.416990 0.572659 -0.728164 0.4733

R-squared 0.495403 Mean dependent var 14.24795

Adjusted R-squared 0.414667 S.D. dependent var 0.701444

S.E. of regression 0.536654 Akaike info criterion 1.744086

Sum squared resid 7.199940 Schwarz criterion 1.977619

Log likelihood -21.16128 Hannan-Quinn criter. 1.818795

F-statistic 6.136116 Durbin-Watson stat 2.013547

Prob(F-statistic) 0.001392

Ramsey RESET Test

Equation: EQ5

Specification: INCOME C TDA EA ASSET GROWTHPCT

Omitted Variables: Squares of fitted values

Value df Probability

t-statistic 1.015615 24 0.3199

F-statistic 1.031475 (1, 24) 0.3199

Likelihood ratio 1.262406 1 0.2612

Ramsey RESET Test

Equation: EQ5

Specification: INCOME C TDA EA ASSET GROWTHPCT

Omitted Variables: Powers of fitted values from 2 to 3

Value df Probability

F-statistic 0.832130 (2, 23) 0.4478

Likelihood ratio 2.095831 2 0.3507

70

APPENDIX 2: Diagnostic Checking (Cont’d)

Breusch-Godfrey Serial Correlation LM Test:

F-statistic 0.401075 Prob. F(2,23) 0.6742

Obs*R-squared 1.011022 Prob. Chi-Square(2) 0.6032

Heteroskedasticity Test: Breusch-Pagan-Godfrey

F-statistic 0.449783 Prob. F(4,25) 0.7716

Obs*R-squared 2.014018 Prob. Chi-Square(4) 0.7332

Scaled explained SS 0.557047 Prob. Chi-Square(4) 0.9677

Heteroskedasticity Test: White

F-statistic 0.609153 Prob. F(14,15) 0.8196

Obs*R-squared 10.87396 Prob. Chi-Square(14) 0.6959

Scaled explained SS 3.007573 Prob. Chi-Square(14) 0.9991

Variance Inflation Factors

Date: 05/22/14 Time: 15:03

Sample: 1 30

Included observations: 30

Coefficient Uncentered Centered

Variable Variance VIF VIF

C 3.317497 345.5755 NA

TDA 0.657987 4.736337 1.362803

EA 0.212334 5.744013 1.602619

ASSET 0.008576 270.0076 1.694312

GROWTHPCT 0.327938 1.080169 1.072806

Covariance Matrix

C TDA EA ASSET GROWTHPCT

C 3.317497 -0.797101 -0.551348 -0.166730 -0.196069

TDA -0.797101 0.657987 0.167959 0.033391 -0.017252

EA -0.551348 0.167959 0.212334 0.024353 0.009901

ASSET -0.166730 0.033391 0.024353 0.008576 0.011009

GROWTHPCT -0.196069 -0.017252 0.009901 0.011009 0.327938

0

1

2

3

4

5

6

7

8

-1.00 -0.75 -0.50 -0.25 0.00 0.25 0.50 0.75 1.00

Series: ResidualsSample 1 30Observations 30

Mean -1.91e-15Median -0.116517Maximum 0.858118Minimum -0.917988Std. Dev. 0.498271Skewness 0.084097Kurtosis 1.796564

Jarque-Bera 1.845684Probability 0.397388

71

APPENDIX 2: Diagnostic Checking (Cont’d)

MODEL 6

Dependent Variable: INCOME

Method: Least Squares

Date: 05/22/14 Time: 21:42

Sample: 1 30

Included observations: 28

Variable Coefficient Std. Error t-Statistic Prob.

C 13.30189 6.074561 2.189770 0.0389

DEBT -0.049117 0.152555 -0.321966 0.7504

EQUITY 0.031699 0.146239 0.216760 0.8303

ASSET 0.373735 0.199630 1.872141 0.0740

GROWTH -0.341696 0.363348 -0.940409 0.3568

R-squared 0.524262 Mean dependent var 14.21630

Adjusted R-squared 0.441525 S.D. dependent var 0.712365

S.E. of regression 0.532359 Akaike info criterion 1.737434

Sum squared resid 6.518331 Schwarz criterion 1.975327

Log likelihood -19.32407 Hannan-Quinn criter. 1.810160

F-statistic 6.336488 Durbin-Watson stat 1.966240

Prob(F-statistic) 0.001370

Ramsey RESET Test

Equation: EQ6

Specification: INCOME C DEBT EQUITY ASSET GROWTH

Omitted Variables: Squares of fitted values

Value df Probability

t-statistic 1.897534 22 0.0710

F-statistic 3.600636 (1, 22) 0.0710

Likelihood ratio 4.244093 1 0.0394

Ramsey RESET Test

Equation: EQ6

Specification: INCOME C DEBT EQUITY ASSET GROWTH

Omitted Variables: Powers of fitted values from 2 to 3

Value df Probability

F-statistic 2.296725 (2, 21) 0.1253

Likelihood ratio 5.538792 2 0.0627

72

APPENDIX 2: Diagnostic Checking (Cont’d)

Breusch-Godfrey Serial Correlation LM Test:

F-statistic 0.263440 Prob. F(2,21) 0.7709

Obs*R-squared 0.685311 Prob. Chi-Square(2) 0.7099

Heteroskedasticity Test: Breusch-Pagan-Godfrey

F-statistic 0.843602 Prob. F(4,23) 0.5119

Obs*R-squared 3.582389 Prob. Chi-Square(4) 0.4655

Scaled explained SS 1.206722 Prob. Chi-Square(4) 0.8770

Heteroskedasticity Test: White

F-statistic 1.189843 Prob. F(14,13) 0.3798

Obs*R-squared 15.72667 Prob. Chi-Square(14) 0.3303

Scaled explained SS 5.297501 Prob. Chi-Square(14) 0.9812

Variance Inflation Factors

Date: 05/22/14 Time: 21:45

Sample: 1 30

Included observations: 28

Coefficient Uncentered Centered

Variable Variance VIF VIF

C 36.90029 3645.685 NA

DEBT 0.023273 565.1412 4.744379

EQUITY 0.021386 553.7984 2.905942

ASSET 0.039852 1182.757 7.559172

GROWTH 0.132022 3124.379 1.099300

Covariance Matrix

C DEBT EQUITY ASSET GROWTH

C 36.90029 -0.213234 -0.293701 0.254146 -2.146129

DEBT -0.213234 0.023273 0.005414 -0.024523 0.012030

-0.293701 0.005414 0.021386 -0.018928 0.012335

ASSET 0.254146 -0.024523 -0.018928 0.039852 -0.016426

GROWTH -2.146129 0.012030 0.012335 -0.016426 0.132022

0

1

2

3

4

5

6

-1.00 -0.75 -0.50 -0.25 0.00 0.25 0.50 0.75 1.00

Series: ResidualsSample 1 30Observations 28

Mean 4.84e-15Median -0.112820Maximum 0.944709Minimum -0.847927Std. Dev. 0.491345Skewness 0.206250Kurtosis 1.998446

Jarque-Bera 1.368811Probability 0.504390