1. 1
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.

2. 2
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.

5. 5
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.

6. 6
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.

9. 9
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

14. 14
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).

16. 16
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

17. 17
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

19. 19
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. 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. 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. 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. 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. 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. 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. 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. 34
.0
.1
.2
.3
.4
.5
.6
0.0 0.5 1.0 1.5 2.0 2.5 3.0
ROE
TDA
14
15
16
17
18
19
20
21
0.0 0.5 1.0 1.5 2.0 2.5 3.0
ROE
ASSET
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
GROWTH
0
1
2
3
4
5
6
7
8
0.0 0.5 1.0 1.5 2.0 2.5 3.0
ROE
DEBTOVEREQUITY
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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 46
References
Akintoye, I. R. (2009). Sensitivity of Performance to Capital Structure. Banking &
Finance Letters, 1(2).
Allen, J. W. (1998). Capital markets and corporate structure: the equity carve-outs of
Thermo Electron. Journal of Financial Economics, 48(1), 99–124.
doi:10.1016/S0304-405X(98)00005-1
Asmy, M., Rohilina, W., Hassama, A., & Fouad, M. (2009). Effects of macroeconomic
variables on stock prices in Malaysia: An approach of error correction model.
Baxter, N. D. (1967). Leverage, Risk of Ruin and the Cost of Capital. The Journal of
Finance, 22(3), 395–403. doi:10.2307/2978892
Baharin, I., & Sentosa, I. Capital Structure and the Post Performance Factors of
Malaysian PN 17 Firms.
Baum, C. F., Schafer, D., & Talavera, O. (2006). The Effects of Short-Term Liabilities
on Profitability: A Comparison of German and US Firms (Vol. 636). Boston
College Working Papers in Economics.
Baum C. F., Schafer D. and Talavera O. (2007): «The Effects of Short-Term Liabilities
on Profitability: The Case of Germany», Money Macro and Finance (MMF)
Research Group Conference 2006 61, Money Macro and Finance Research
Group.
Berger, A. N., & Bonaccorsi di Patti, E. (2006). Capital structure and firm performance:

47. 47
A new approach to testing agency theory and an application to the banking
industry. Journal of Banking & Finance, 30(4), 1065–1102.
doi:10.1016/j.jbankfin.2005.05.015
Campello, M. (2006). Debt financing: Does it boost or hurt firm performance in product
markets? Journal of Financial Economics, 82(1), 135–172.
doi:10.1016/j.jfineco.2005.04.001
Chevalier, J. A. (1995). Do LBO Supermarkets Charge More? An Empirical Analysis of
the Effects of LBOs on Supermarket Pricing. The Journal of Finance, 50(4),
1095–1112. doi:10.2307/2329345
Chirinko, R. S., & Singha, A. R. (2000). Testing static tradeoff against pecking order
models of capital structure: a critical comment. Journal of Financial Economics,
58(3), 417–425. doi:10.1016/S0304-405X(00)00078-7
Chung, Y. P., Na, H. S., & Smith, R. (2013). How important is capital structure policy to
firm survival? Journal of Corporate Finance, 22, 83–103.
doi:10.1016/j.jcorpfin.2013.04.002
Cohn, J. B., Mills, L. F., & Towery, E. M. (n.d.). The evolution of capital structure and
operating performance after leveraged buyouts: Evidence from U.S. corporate
tax returns. Journal of Financial Economics. doi:10.1016/j.jfineco.2013.11.007
Corporate history | bursa malaysia corporate. (n.d.). Retrieved from

48. 48
http://www.bursamalaysia.com/corporate/about-us/corporate-history/
Corporate finance - the mm capital structure vs. the tradeoff theory of leverage. (n.d.).
Retrieved from http://www.investopedia.com/exam-guide/cfa-level-1/corporate-
finance/mm-capital-structure-versus-tradeoff-leverage.asp
Danis, A., Rettl, D. A., & Whited, T. M. (2011). Refinancing, Profitability, and Capital
Structure Retrieved from http://papers.ssrn.com/abstract=2170671
http://www.danielrettl.com/DanisRettl_TradeoffTheory_2011.pdf
Datastream. (2012) Thomson Reuters Datastream. [Online]. Available at: Subscription
Service (Accessed: April and May 2013)
de Mesquita, J. M. C., & Lara, J. E. (2003). Capital structure and profitability: the
Brazilian case.
Department of Statistics, Malaysia. (n.d.). NATIONAL ACCOUNTS GROSS
DOMESTIC PRODUCT. . Retrieved , from
http://www.statistics.gov.my/portal/index.php?option=com_content&view=articl
e&id=1414&Itemid=111&lang=en
Eriotis N. P., Franguoli Z. and Neokosmides Z. V. (2002): Profit Margin and Capital
Structure: An Empirical Relationship». The Journal of Applied Business
Research (18), pp. 85-89.
Fama, E. F., & French, K. R. (2002). Testing Trade-Off and Pecking Order Predictions
About Dividends and Debt. Review of Financial Studies, 15(1), 1–33.
Fazzari, S. M., Hubbard, R. G., Petersen, B. C., Blinder, A. S., & Poterba, J. M. (1988).

49. 49
Financing Constraints and Corporate Investment. Brookings Papers on Economic
Activity, 1988(1), 141–206. doi:10.2307/2534426
Financial Performance Definition. (n.d.). Investopedia. Retrieved December 18, 2013,
from http://www.investopedia.com/terms/f/financialperformance.asp
Fosu, S. (2013). Capital structure, product market competition and firm performance:
Evidence from South Africa. The Quarterly Review of Economics and Finance,
53(2), 140–151. doi:10.1016/j.qref.2013.02.004
Frank, M. Z., & Goyal, V. K. (2003). Capital Structure Decisions (SSRN Scholarly
Paper No. ID 396020). Rochester, NY: Social Science Research Network.
Retrieved from http://papers.ssrn.com/abstract=396020
FTSE. (2013, June 13). FTSE Bursa Malaysia Index Series Semi-Annual Review June
2013 - Update. . Retrieved , from
http://www.ftse.com/tech_notices/2013/Q2/78339_20130617_FBM_Review_Jun
e_2013_update.jsp
Gill, D. (2012). Why Return-on-Equity Often Misleads Investors These Days: Dell
Better than Apple?. Forbes.
Goddard, J., Tavakoli, M., & Wilson, J. O. S. (2005). Determinants of profitability in
European manufacturing and services: evidence from a dynamic panel model.
Applied Financial Economics, 15(18), 1269–1282.
doi:10.1080/09603100500387139

50. 50
González, V. M. (2013). Leverage and corporate performance: International evidence.
International Review of Economics & Finance, 25, 169–184.
doi:10.1016/j.iref.2012.07.005
Harris, M., & Raviv, A. (1988). Corporate control contests and capital structure. Journal
of Financial Economics, 20, 55–86. doi:10.1016/0304-405X(88)90040-2
Hovakimian, A., Hovakimian, G., & Tehranian, H. (2004). Determinants of target
capital structure: The case of dual debt and equity issues. Journal of Financial
Economics, 71(3), 517–540. doi:10.1016/S0304-405X(03)00181-8
Huang, G., & Song, F. M. (2006). The determinants of capital structure: Evidence from
China. China Economic Review, 17(1), 14–36. doi:10.1016/j.chieco.2005.02.007
Hasan, N. A., Shaari, N. A., Palanimally, Y. R., & Haji-Mohamed, R. K. M. (2013). The
Impact of Macroeconomic and Bank Specific Components on the Return of
Equity in Malaysia. Interdisciplinary Journal of Contemporary Research in
Business, 5(2), 106-126.
Insider Asia (2009, June 30). Introducing the FTSE Bursa Malaysia KLCI. The Edge
Malaysia.
Jackson, S. B., Keune, T. M., & Salzsieder, L. (2013). Debt, equity, and capital
investment. Journal of Accounting and Economics, 56(2–3), 291–310.
doi:10.1016/j.jacceco.2013.09.001
Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior,

51. 51
agency costs and ownership structure. Journal of Financial Economics, 3(4),
305–360. doi:10.1016/0304-405X(76)90026-X
John, A. O. Effect Of Capital Structure On Firm Performance: Evidence From Nigerian
Manufacturing Industry.
Kallunki, J.-P., Laitinen, E. K., & Silvola, H. (2011). Impact of enterprise resource
planning systems on management control systems and firm performance.
International Journal of Accounting Information Systems, 12(1), 20–39.
doi:10.1016/j.accinf.2010.02.001
Kebewar, mazen, & SHAH, S. M. N. A. (2012, December 10). The effect of debt on
corporate profitability: Evidence from French service sector. MPRA Paper.
Retrieved December 18, 2013, from http://mpra.ub.uni-muenchen.de/43304/
Kebewar, M. (2012). L‘impact De L‘endettement Sur La Profitabilité Une Étude
Empirique Sur Données Françaises En Panel. Retrieved From
http://Halshs.Archives-Ouvertes.Fr/Halshs-00749685
Kim, E. H. (1978). A Mean-Variance Theory of Optimal Capital Structure and
Corporate Debt Capacity. The Journal of Finance, 33(1), 45–63.
doi:10.2307/2326349
Liability Definition. (n.d.). Investopedia. Retrieved December 18, 2013, from
http://www.investopedia.com/terms/l/liability.asp
Listing statistics | bursa malaysia market. (n.d.). Retrieved from

52. 52
http://www.bursamalaysia.com/market/listed-companies/initial-public-
offerings/listing-statistics/
Majumdar, S. K., & Chhibber, P. (1999). Capital structure and performance: Evidence
from a transition economy on an aspect of corporate governance.Public
Choice, 98(3-4), 287-305
Margaritis, D., & Psillaki, M. (2010). Capital structure, equity ownership and firm
performance. Journal of Banking & Finance, 34(3), 621–632.
doi:10.1016/j.jbankfin.2009.08.023
Margaritis, D., & Psillaki, M. (2007). Capital Structure and Firm Efficiency. Journal of
Business Finance & Accounting, 34(9-10), 1447–1469. doi:10.1111/j.1468-
5957.2007.02056.x
Modigliani, F., & Miller, M. H. (1958). The Cost of Capital, Corporation Finance and
the Theory of Investment. The American Economic Review, 48(3), 261–297.
Mohammed, A. A. E. (2012). Financial situation of PN17 companies listed in the
Malaysian stock exchange (masters). Universiti Tun Hussein Onn Malaysia.
Retrieved from http://eprints.uthm.edu.my/2911/
Naqi, S. A., & Hettihewa, S. (2007). Venture capital or private equity? The Asian
experience. Business Horizons, 50(4), 335–344.
doi:10.1016/j.bushor.2007.03.001

53. 53
Ngobo, P. V. et Capiez A.(2004):«Structure du capital et performance de l‘entreprise: le
rôle modérateur des différences culturelles». Congrès de l’Association
Internationale de Management Stratégique (AIMS), Le Havre.
Nimalathasan, D. B., Lecturer, S., & Jaffna, S. L. Capital Structure Patterns: A Study Of
Companies Listed on The Colombo Stock Exchange In Sri Lanka. Chief Patron
Chief Patron.
Niu, X. (2008). Theoretical and practical review of capital structure and its
determinants. International Journal of Business and Management, 3(3), P133.
Norden, L., & van Kampen, S. (2013). Corporate leverage and the collateral channel.
Journal of Banking & Finance, 37(12), 5062-5072.
Novaes, W. (2003). Capital Structure Choice When Managers Are in Control:
Entrenchment versus Efficiency*. The Journal of Business, 76(1), 49–82.
doi:10.1086/344113
Nunes, P. J. M., Serrasqueiro, Z. M., & Sequeira, T. N. (2009). Profitability in
Portuguese service industries: a panel data approach. The Service Industries
Journal, 29(5), 693–707. doi:10.1080/02642060902720188
Ofek, E. (1993). Capital structure and firm response to poor performance. Journal of
Financial Economics, 34(1), 3–30. doi:10.1016/0304-405X(93)90038-D
Opler, T. C., & Titman, S. (1994). Financial Distress and Corporate Performance. The
Journal of Finance, 49(3), 1015–1040. doi:10.2307/2329214

54. 54
Perwaja 3Q loss widens to RM227m; defaults on debts, sinks into PN17 slot. (n.d.). The
Edge Malaysia. Retrieved December 18, 2013, from
http://www.theedgemalaysia.com/business-news/264866-perwaja-3q-loss-
widens-to-rm227m-defaults-on-debts-sinks-into-pn17-slot.html
Pn17 companies | bursa malaysia market. (n.d.). Retrieved from
http://www.bursamalaysia.com/market/listed-companies/list-of-companies/pn17-
companies/
Rao N. V., Al-Yahyaee K. H. M. and Syed L. A. M. (2007): «Capital structure and
financial performance: evidence from Oman», Indian Journal of Economics and
Business (7), pp. 1-14.
Rating Agency Malaysia Berhad, (2013). 2012 corporate default and rating transition
study. Retrieved from website:
http://www.ram.com.my/publicationview.aspx?sid=7f9195a203fa14c972baff49f
7f36d52&id=bc4de037-7e5c-4cf5-ba73-3dc4feb79d62
Rating Agency Malaysia Berhad, (2013). 2012 structured finance ratings migration
study. Retrieved from website:
http://www.ram.com.my/publication_view.aspx?catid=43a06d8d-9a70-4b9f-
9cd5-8c101dab028d&ddlid=63232e96-f72f-41e2-847f-
23ba77a6f478&sid=c18af75c6dfa775aa827f5edc32b169e&ID=b15c4373-0c68-
4b5e-b28c-6b356616551d
Raza, H., Aslam, S., & Farooq, U. (2013). Financing Pattern in Developing Nations
Empirical Evidence from Pakistan. World Applied Sciences Journal,22(9), 1279-
1285.

55. 55
Saad, N. M. (2010). Corporate governance compliance and the effects to capital
structure in Malaysia. International journal of economics and finance, 2(1), 105-
114.
Salim, M., & Yadav, R. (2012). Capital Structure and Firm Performance: Evidence from
Malaysian Listed Companies. Procedia - Social and Behavioral Sciences, 65,
156–166. doi:10.1016/j.sbspro.2012.11.105
Sc & bursa malaysia launch new fund-raising framework & board structure – more
efficient access to capital & investments | securities commission. (n.d.).
Retrieved from http://www.sc.com.my/post_archive/sc-bursa-malaysia-launch-
new-fund-raising-framework-board-structure-more-efficient-access-to-capital-
investments/
Shyam-Sunder, L., & C. Myers, S. (1999). Testing static tradeoff against pecking order
models of capital structure. Journal of Financial Economics, 51(2), 219–244.
doi:10.1016/S0304-405X(98)00051-8
Shubita, M. F., & Alsawalhah, J. M. (2012). The Relationship between Capital Structure
and Profitability. International Journal of Business and Social Science, 3(16),
104-112.
Simerly, R. L., & Li, M. (2000). Environmental dynamism, capital structure and
performance: a theoretical integration and an empirical test. Strategic
Management Journal, 21(1), 31–49. doi:10.1002/(SICI)1097-
0266(200001)21:1<31::AID-SMJ76>3.0.CO;2-T

56. 56
Silver Bird announces regularisation plan | theSundaily. (2013). Retrieved December 18,
2013, from http://www.thesundaily.my/news/855641
Stulz, R. (1988). Managerial control of voting rights: Financing policies and the market
for corporate control. Journal of Financial Economics, 20, 25–54.
doi:10.1016/0304-405X(88)90039-6
Tan, T. K. Financial Distress and Firm Performance: Evidence from the Asian Financial
Crisis. Retrieved from http://www.aabri.com/manuscripts/121199.pdf
Tsuji, C. (2011). Recent development of the agency theory and capital
structure. Economic and Finance Review, 1(6), 94-99.
Ulph, A., & Valentini, L. (2004). Environmental liability and the capital structure of
firms. Resource and Energy Economics, 26(4), 393–410.
doi:10.1016/j.reseneeco.2004.03.002
Wang, Y., & Liu, C. (2009). Capital structure, equity structure, and technical efficiency
— empirical study based on China coal listed companies. Procedia Earth and
Planetary Science, 1(1), 1635–1640. doi:10.1016/j.proeps.2009.09.251
Weill, L. (2008). Leverage and corporate performance: does institutional environment
matter?. Small Business Economics, 30(3), 251-265.
Weill, L. (2011, December 27). Leverage and Corporate Performance: Does Institutional

57. 57
Environment Matter? research-article. Retrieved December 18, 2013, from
http://ezproxy.upm.edu.my:2135/stable/info/40650910
Welch, I. (2010). Common Problems in Capital Structure Research: The Financial-
Debt-To-Asset Ratio, and Issuing Activity vs. Leverage Changes (SSRN
Scholarly Paper No. ID 931675). Rochester, NY: Social Science Research
Network. Retrieved from http://papers.ssrn.com/abstract=931675.
Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. MIT
press
Zeitun, R., & Tian, G. (2007). Capital structure and corporate performance: evidence
from Jordan. Australasian Accounting Business and Finance Journal, 1(4).
Retrieved from http://ro.uow.edu.au/aabfj/vol1/iss4/3
Zwiebel, J. (1996). Dynamic Capital Structure under Managerial Entrenchment. The
American Economic Review, 86(5), 1197–1215.

58. 58
Appendices
APPENDIX 1: COMPANIES’ DATA
Source: Thomson Reuters datastream
(1) (2) (3) (4) (5) (6) (7) =(2)-(3)
Name
Net 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)
AMMBHOLDINGS 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
BRITISHAMERTOBACCO 823,440.00 1,360,259.00 851,927.00 510,000.00 - 510,000.00 508,332.00
CIMBGROUP 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 WORLDBHD 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
IHHHEALTHCARE 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 LUMPURKEPONG 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
PPBGROUP 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
RHBCAPITAL 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

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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)
AMMBHOLDINGS 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
BRITISHAMERTOBACCO 1,360,259.00 4,364,786.00 4,517,222.00 152436.00
CIMBGROUP 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 WORLDBHD 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
IHHHEALTHCARE 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 LUMPURKEPONG 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
PPBGROUP 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
RHBCAPITAL 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

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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
AMMBHOLDINGS 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
BRITISHAMERTOBACCO 0.02 13.62 0.37 - 0.37 13.14 0.37 13.14 0.03 15.52
CIMBGROUP 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 WORLDBHD 0.05 14.29 0.09 0.08 0.01 14.33 0.79 16.55 0.06 15.57
HONG LEONG BANKBHD 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
IHHHEALTHCARE 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 LUMPURKEPONG 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
PPBGROUP BHD 0.06 13.81 0.02 0.01 0.02 12.95 0.95 16.60 0.10 15.54
PUBLIC BANKBHD 0.00 15.22 0.09 0.03 0.06 17.15 0.07 16.87 0.08 15.66
RHBCAPITALBERHAD #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
SIMEDARBY 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 NASIONALBHD 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
YTLCORPORATION BHD 0.02 14.06 0.57 0.49 0.08 17.24 0.29 16.56 -0.01 15.44

61. 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. 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: Residuals
Sample 1 30
Observations 30
Mean -1.44e-16
Median 0.014129
Maximum 1.099124
Minimum -0.630634
Std. Dev. 0.312076
Skewness 1.163584
Kurtosis 6.425859
Jarque-Bera 21.44028
Probability 0.000022

63. 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. 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: Residuals
Sample 1 30
Observations 30
Mean 2.14e-16
Median 0.040718
Maximum 1.191401
Minimum -0.562118
Std. Dev. 0.313763
Skewness 1.600146
Kurtosis 8.087785
Jarque-Bera 45.15928
Probability 0.000000

65. 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. 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: Residuals
Sample 1 30
Observations 30
Mean 5.96e-16
Median -0.028235
Maximum 1.325849
Minimum -0.398253
Std. Dev. 0.334731
Skewness 1.969969
Kurtosis 9.157238
Jarque-Bera 66.79336
Probability 0.000000

67. 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. 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: Residuals
Sample 1 30
Observations 30
Mean -1.07e-15
Median 0.022239
Maximum 1.121510
Minimum -0.616802
Std. Dev. 0.311644
Skewness 1.270254
Kurtosis 6.859853
Jarque-Bera 26.69081
Probability 0.000002

69. 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. 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: Residuals
Sample 1 30
Observations 30
Mean -1.91e-15
Median -0.116517
Maximum 0.858118
Minimum -0.917988
Std. Dev. 0.498271
Skewness 0.084097
Kurtosis 1.796564
Jarque-Bera 1.845684
Probability 0.397388

71. 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