CAPITAL STRUCTURE 1.0 INTRODUCTION The literature on determinants of capital structure is well known of the existence of three theories that are trade off, pecking order and free cash flow (managerial agency costs). Each theory presents a different explanation of corporate financing. The trade off theory is concerned with the trade off between debt tax shields or tax saving, and bankruptcy costs, according to which an optimal capital structure is assumed to exist. The pecking order theory assumes hierarchal financing decisions where firms depend first on internal sources of financing and, if these are less than the investment requirements, the firm seeks external financing from debt as a second source, then equity as the last resort. The free cash flow theory assumes that debt presents fixed obligations such as debt interests and principals to pay, that have to be met by the firm. These obligations are assumed to take over the firm's free cash flow (if exists), therefore prevents managers from over consuming the firm's financial resources. 1
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CAPITAL STRUCTURE
1.0 INTRODUCTION
The literature on determinants of capital structure is well known of the existence
of three theories that are trade off, pecking order and free cash flow (managerial agency
costs). Each theory presents a different explanation of corporate financing. The trade off
theory is concerned with the trade off between debt tax shields or tax saving, and
bankruptcy costs, according to which an optimal capital structure is assumed to exist. The
pecking order theory assumes hierarchal financing decisions where firms depend first on
internal sources of financing and, if these are less than the investment requirements, the
firm seeks external financing from debt as a second source, then equity as the last resort.
The free cash flow theory assumes that debt presents fixed obligations such as debt
interests and principals to pay, that have to be met by the firm. These obligations are
assumed to take over the firm's free cash flow (if exists), therefore prevents managers
from over consuming the firm's financial resources.
It was recognized that the three theories are "conditional" in a sense that each
works out under its own assumptions and propositions (Myers, 2001). That is, none of the
three theories can give a complete picture of the practice of capital structure. This means
that firms can pursue capital structure strategies that are conditional as well. That means
that when the business conditions change, the financing decisions and strategies may
change, moving from one theory to another. This is the main reason that the literature
does not include one theory or one explanation on the determinants of capital structure. In
fact, an interrelationship can be observed between and among the three theories of capital
structure.
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For example, the trade off theory assumes a higher use of debt as long as the debt
is associated with positive tax shields and less bankruptcy costs. This does not mean that
the firm can reach the maximum debt ratio if, under the assumptions of the pecking order
theory, the firm is profitable enough to replace debt with internal financing using the
accumulated retained earnings which is can be considered as a part of an equity
financing. According to the free cash flow theory, it is affected by the severity of the
agency costs associated with debt or equity financing. In fact, the agency theory presents
another explanation of debt financing. That is, as long as the agency problem arises from
the presence of information asymmetry, Ross (1977), Myers and Majluf (1984) and John
(1987) have shown that under asymmetric information, firms may prefer debt to equity
financing. Therefore, the interrelationships between and among the three theories of
capital structure call for further examination.
It was also found that studies on the determinants of capital structure include
selected determinants in a regression equation. This is what Fama and French (2002)
referred to as the two theories of capital structure that are trade off and pecking order
have share many common predictions about the determinants of leverage. In this
research, the study had used leverage (total debt to total asset) as dependent variable and
tangibility, size, profitability, growth, volatility and non debt tax shields as independent
variables. However, Myers (2001) had stated that each theory works out under its own
assumptions. Thus, for this study, the explanatory power and significance of each theory
that are represented by independent variables will show the extent of these variables can
explain the leverage.
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1.1 PROBLEM STATEMENT
Over the years numerous studies on capital structure theory have appeared.
Modigliani and Miller (1958) were the first who theorized the issue by illustrate that the
valuation of a firm will be independent from its financial structure under certain key
assumptions. Internal and external funds may be regarded as perfect substitutes in a world
where capital markets function perfectly, where there are no transaction or bankruptcy
costs and the firm cannot increase its value by changing its leverage.
However, based on the previous research made by Myers (2001), he stated that
each theory applied should be based on some certain circumstances. Due to that, the
theories are not designed to be general. They are conditional theories of capital structure.
Each emphasizes certain costs and benefits of alternative financing strategies. Because
the theories are not general, testing them on a broad, heterogeneous sample of firms can
be uninformative.
This study comprises of certain related questions to be known:
i) To what extent the three theories can give impact to the capital structure
decision on the firm’s leverage. Besides that, it also to examine which
factors is reliably important for predicting Malaysian firms.
1.2 OBJECTIVE OF THE STUDY
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The basic objective of any corporate finance study of capital structure is to
identify factors explaining the firm’s decision with respect to its financial leverage.
Starting with Miller and Modigliani (1958), the literature on capital structure has been
expanded by many theoretical and empirical contributions and due to that, much
emphasis has been placed on releasing the assumptions made by MM.
Thus, this study had followed another approach which is to examine each theory
independently. The argument here follows what is stated by Myers (2001) that each
theory works out under its own assumptions. This requires an examination of each theory
independently to avoid the highly likely overlap between results. Generally, the
explanatory power and significance of each theory represented by an independent
regression equation will show the extent to which the explanatory variables of each
theory explain variations in corporate leverage.
Therefore, the objective of this paper is to examine the extent to which capital
structure decisions are affected by the three common theories that are trade off, pecking
order and free cash flow. Besides that, it also to explore whether the main theories of firm
financing can explain the capital structure of these firms. The leverage ratio is used as a
proxy for firm’s capital structure. The type of industry especially, the firm specific
characteristics is used as a control variable that may have effects on changes of capital
structure.
2.0 LITERATURE REVIEW
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1) PROFITABILITY
One of the main theoretical controversies is the relationship between leverage and
profitability of the firm. Profitability is a measure of earning power of a firm. The earning
power of a firm is the basic concern of its shareholders. Based on the previous research in
the agency models of Jensen and Meckling (1976), Easterbrook (1984) and Jensen
(1986), higher leverage helps to control agency problem by forcing managers to pay out
more of the firm’s excess cash. So, the strong commitment to pay out a larger fraction of
their pre interest earnings to debt payments suggests a positive relationship between book
leverage and profitability. This result is also consistent with the signaling hypothesis by
Ross (1977), where higher levels of debt can be used by managers to signal an optimistic
future for the firm.
However, the sharp contrast results in the pecking order model, when higher earnings
should result in less book leverage. This is when firms prefer raising capital first from
retained earnings, second from debt and third from issuing new equity. This is due to the
cost associated with new equity issues in the presence of information asymmetries.
Accordingly, the study made by Fama and French (2000), the pecking order model
predicts a negative relationship between book leverage and profitability. Besides that,
Fama and French also arise an important question is whether these predictions for book
leverage carry over to market leverage. Due to that, this theory predicts that firms with a
lot of profits and few investments have little debt. Since the market value increases with
profitability, the negative relationship between book leverage and profitability also holds
for market leverage.
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Moreover, a study made by Rajan and Zingales (1995), under the same theory, reported
that they found a negative relationship between leverage and profitability. However,
another study made by Jensen, Solberg and Zorn (1992), had found a contrast result when
under the trade off theory it have a positive relationships.
Besides that, the study made by Wolfgang Drobetz and Roger Fix (2003) documented
that profitability is negatively correlated with leverage, both for book and market
leverage. Thus, result had reliably supports the predictions of the pecking order theory. In
addition, to the statistical significance, the economic significance of profitability on
leverage is also noteworthy. Due to that, this finding is consistent with the research made
by Rataporn Deesomsak et. al (2004). Moreover, based on the study made by Murray Z.
Frank and Vidhan K. Goyal (2004) had found that firms that have more profits tend to
have less leverage.
2) GROWTH
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The empirical evidence regarding the relationship between leverage and growth
opportunities is rather mixed. Based on the previous research made by Titman and
Wessels (1988) had found a negative relationship but Kester (1986) had a contrast result
when does not find any support for the predicted negative relationship between growth
opportunities and gearing.
Furthermore, based on the previous research made Rajan and Zingales (1995) also
uncovered evidence of negative correlations between market to book and gearing for all
G-7 countries. Thus, Rajan and Zingales had reported a positive relationship between
leverage and growth.
Moreover, the result is consistent with the theoretical predictions of Jensen and Mekling
(1976) based on agency theory and the work of Myers (1977), who argues that, due to
information asymmetric, companies with high gearing, would have a tendency to pass up
positive net present value investment opportunities (also known as growth options).
Myers therefore argues that companies with large amounts of investments opportunities
would tend to have low gearing ratios.
Besides that, another study made by Jensen’s (1986) under the free cash flow theory
which predicts that firms with more investment opportunities have less need for the
disciplining effect of debt payments to control free cash flows.
In addition, based on the study by Fama and French (2000), had explained how the
predictions for book leverage carry over to market leverage. According to the trade off
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theory, it predicts a negative relationship between leverage and investment opportunities.
Since the market value grows at least in proportion with investment outlays, the relation
between growth opportunities and market leverage is negative and also supported by
Rataporn Deesomsak et. al (2004).
According to Wolfgang Drobetz and Roger Fix (2003), they state that among all proxies
variables, they found the strongest and most reliable relationship between investment
opportunities and leverage. Specifically, companies with high market to book ratios have
significantly lower leverage than companies with low market to book ratio. Thus, this
result is consistent with both the trade off theory and the extended version of the pecking
order theory.
Moreover, from prior research made by Chingfu Chang et. al (2008), they found that
growth has a negative effect on leverage and this result is consistent with Booth et. al
(2001). This judgment is also consistent with Murray Z. Frank and Vidhan K. Goyal
(2004) in their research found that firms which have a high market to book ratio tend to
have low levels of leverage.
3) SIZE
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The effect of size on leverage is ambiguous. According to Warner (1977) and Ang, Chua
and McConnel (1982) indicate that bankruptcy costs are relatively higher for smaller
firms. This judgment is also supported by Titman and Wessels (1988) when they argue
that larger firms tend to be more diversified and fail less often.
On the other hands, under the trade off theory, it predicts an inverse relationship between
size and the probability of bankruptcy. Due to that, it showed a positive relationship
between size and leverage and this judgment is also supported by Rataporn Deesomsak
(2004). If diversification goes along with more stable cash flows, this prediction is also
consistent with the free cash flow theory that are studied by Jensen (1986) and
Easterbrook (1986). Thus, the result showed that size has a positive impact on the supply
of debt (leverage). However, under the pecking order theory of the capital structure, it
predicts a negative relationship between leverage and size, with larger firms exhibiting
increasing preference for equity relative to debt.
Furthermore, in the research made by Rajan and Zingales (1995), indicate that including
size in their cross sectional analysis, they found that the effect of size on equilibrium
leverage is more ambiguous. Thus, larger firms tend to be more diversified and because
of that, size may then be inversely related to the probability of bankruptcy.
However study made by Wolfgang Drobetz and Roger Fix (2003) had found a contrast
results with the Rajan and Zingales (1995), when size is positively related to leverage by
indicating that size is a proxy for a low probability of default. However, the estimated
coefficients on size are generally not significant. Again this result supports the trade off
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theory, suggesting that large firms exhibit lower probability of default while small firms
wary of debt. This judgment is also supported by Bouallegui (2004) when found that
large firms tend to use more debt than smaller firms. Consistent with this result, Murray
Z. Frank and Vidhan K. Goyal (2004) also indicate that larger firms tend to have high
leverage.
4) TANGIBILITY
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Tangibility is defined as the ratio of tangible (fixed) assets to total assets. Harris and
Raviv (1990) predicts that firm with higher liquidation value will have more debt. Thus,
firms with more tangible assets usually have a higher liquidation value. This judgment is
also supported by Bouallegui (2004) which showed that leverage is also closely related to
tangibility of assets.
On the other hand, based on the previous research by Titman and Wessels (1988), Rajan
and Zingales (1995) and Fama and French (2000) argue that the ratio of fixed to total
assets (tangibility) should be an important factor for leverage. The tangibility of assets
represents the effect of the collateral value of assets of the firm’s gearing level. As such,
firm’s with a higher proportion of tangible assets are more likely to be in a mature
industry thus less risky, which affords higher financial leverage.
Furthermore, based on the study by Galai Masulis (1976), Jensen and Meckling (1976)
and Myers (1977) argue stockholders of levered firms are prone to over invest, which
gives rise to the classical shareholder and bondholder conflict. However, if debt can be
secured against assets, the borrower is restricted to using debt funds for specific projects.
Creditors have an improved guarantee of repayment and the recovery rate is higher such
as assets retain more value in liquidation. Without collateralized assets, such as a
guarantee does not exist, for example the debt capacity should increase with the
proportion of tangible assets on the balance sheet. Hence, under the trade off theory, it
predicts a positive relationship between measures of leverage and the proportion of
tangible asstes.
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However study made by Grossman and Hart (1982) had found a contrast result with the
previous study when they are argue that the agency costs of managers consuming more
than the optimal level of perquisites is higher for firms with lower levels of assets that
can be used as a collateral. Managers of highly levered firms will be less able to consume
excessive perquisites, since bondholders more closely monitor such firms. The
monitoring costs of this agency relationship are higher for firms with less collateralizable
assets. Therefore, firms with less collateralizable assets might voluntarily choose higher
debt levels to limit consumption of perquisites. Thus, this agency model predicts a
negative relationship between tangibility of assets and leverage.
In addition, according to Wolfgang Drobetz and Roger Fix (2003) has mentioned that
tangibility is almost always positively correlated with leverage. The result showed that
regression coefficient on tangibility is significant in about half of all regression. This had
support the prediction of the trade off theory that the debt capacity increases with the
proportion of tangible assets on the balance sheet. This can be quantified by the size of
the changes in leverage ratios that are associated with changes in the ratio of fixed to total
assets. This finding is consistent with more recent research by Rataporn Deesomsak
(2004), where under agency theory the result showed a positive relationship between
tangibility of assets and leverage when anticipated.
5) NON DEBT TAX SHIELD
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Firms will exploit the tax deductability of interest to reduce their tax bill. Therefore, firms
with other tax shields, such as depreciation deductions, will have less need to exploit the
debt tax shield. According to Ross (1985) argues that if a firm in this position issues
excessive debt, it may become ‘tax exhausted’ in the sense that it is unable to use all its
potential tax shields. Thus, the incentive to use debt financing diminishes as non debt tax
shields increase. Accordingly, in the framework of the trade off theory, one hypothesizes
a negative relationship between leverage and non debt tax shields.
However, Scott and Moore (1977) had found a contrast result when argue that firms with
substantial non debt tax shield should also have considerable collateral assets which can
be used to secure debt. It has been argued above that secured debt is less risky than
unsecured debt. Therefore, from a theoretical point it showed a positive relationship
between leverage and non debt shield.
Furthermore, another study made by Shenoy and Koch (1996) had find a negative
relationship between leverage and non debt tax shield. Consistent with this result, the
judgment is also supported by Rataporn Deesomsak (2004) when they also found an
inversely related to leverage. In addition, study made by Bouallegui (2004) had also
stated that leverage is closely related to the ratio of non debt shield. However, Gardner
and Trcinka (1992) had got in contrast, when they found a positive one.
Besides that, another study made by Wolfgang Drobetz and Roger Fix (2003) had
mentioned that the non debt tax shield are generally insignificant. Only in one regression
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specification the estimated coefficient is significant but the sign is opposite to what the
trade off theory suggests means the result showed a positive relationship.
6.0 VOLATILITY
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Leverage increases the volatility of the net profit. Higher volatility of earnings increases
the probability of financial distress, since firms may not be able to fulfill their debt
servicing commitments. Thus, firm’s debt capacity decreases with increases in earnings
volatility leading to an expected inverse relation with leverage (Rataporn Deesomsak,
2004). On the research by Myers (1977), the importance of the type underinvestment
problem increases with the volatility of the firm’s cash flow.
Besides that, based on the study by DeAngelo and Masulis (1980), the two issues here
will be argue that are, for firms which have variability in their earnings, investors will
have little ability to accurately forecast future earnings based on publicly available
information. The market will see the firm as a ‘lemon’ and demand a premium to provide
debt. This drives up the cost of debt. The other one is, to lower the chance of issuing
new risky equity or being unable to realize profitable investments when cash flows are
low, firms with more volatile cash flows tend to keep low leverage. Due to that,
according to the pecking order model, it predicts a negative relationship between leverage
and the volatility of the firm’s cash flow.
Besides that, the trade off model allows the same prediction, but the reasoning is slightly
different. More volatile cash flows increase the probability of default, implying that a
negative relationship between leverage and volatility of cash flows.
However, in contrast, firms with stable cash flows should suffer from overinvestment
problems. Based on the research by Easterbrook and Jensen (1986), these firms
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supposedly have more leverage, which further strengthens the notion of a negative
relationship between leverage and volatility.
Furthermore, based on the study by Wolfgang Drobetz and Roger Fix (2003), had found
in their research that the relationships between leverage and volatility is negative. This
result also support both the trade off theory (more volatile cash flows increase the
probability of default) and the pecking order theory (issuing equity is more costly for
firms with volatile cash flows).
3.0 RESEARCH METHODOLOGY
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3.1 THEORETICAL FRAMEWORK
Theoretical framework is the network on how these variables are associated with each
other. It consists of dependent and independent variables that are believed to have
relationships with the research topic. The dependent variable is the leverage, while the
independent variables are profitability, growth, size, tangibility, non debt tax shield and
volatility.
The dependent variable can be defined as the phenomenon or characteristics
hypothesized to be the outcome, effect, consequent or output of some input variables. Its
occurrence depends on some other variables, which usually has come before the
dependent variables. The purpose of this variable is to identify the output or presumed
effect of one or more independent variables.
Independent variables can be defined as the characteristics hypothesized to be the input
previous variable. It is assumed to an effect the dependent variable and is manipulated,
measured or selected in order to measure the outcome of dependent variable.
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Leverage
Profitability
Growth
Size
Tangibility
Non DebtTax Shields
Volatility
INDEPENDENT VARIABLES DEPENDENT VARIABLE
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3.2 HYPOTHESES
Hypothesis 1
Trade Off Theory
H0: There is a negative relationship between profitability and leverage.
H1: There is a positive relationship between profitability and leverage.
Pecking Order Theory
H0: There is a positive relationship between profitability and leverage.
H1: There is a negative relationship between profitability and leverage.
Hypothesis 2
Trade Off Theory
H0: There is a positive relationship between growth and leverage.
H1: There is a negative relationship between growth and leverage.
Pecking Order Theory
H0: There is a negative relationship between growth and leverage.
H1: There is a positive relationship between growth and leverage.
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Agency Cost Theory
H0: There is a negative relationship between growth and leverage.
H1: There is a positive relationship between growth and leverage
Hypothesis 3
Trade Off Theory
H0: There is a negative relationship between size and leverage.
H1: There is a positive relationship between size and leverage.
Pecking Order Theory
H0: There is a positive relationship between size and leverage.
H1: There is a negative relationship between size and leverage
Agency Cost Theory
H0: There is a negative relationship between size and leverage.
H1: There is a positive relationship between size and leverage
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Hypothesis 4
Trade Off Theory
H0: There is a negative relationship between tangibility and leverage.
H1: There is a positive relationship between tangibility and leverage.
Agency Cost Theory
H0: There is a positive relationship between tangibility and leverage.
H1: There is a negative relationship between tangibility and leverage
Hypothesis 5
Trade Off Theory
H0: There is a positive relationship between non debt tax shields and leverage.
H1: There is a negative relationship between non debt tax shields and leverage
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Hypothesis 6
Trade Off Theory
H0: There is a positive relationship between volatility and leverage.
H1: There is a negative relationship between non volatility and leverage
Pecking Order Theory
H0: There is a positive relationship between volatility and leverage.
H1: There is a negative relationship between volatility and leverage
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3.3 EXPLANATORY VARIABLES
The explanatory variables consist of those that have commonly been documented in the
literature to affect the firm leverage. In this study, there are consists six independent
variables and defined as follows:
1) Profitability (PROF)
The measurement of profitability is by using the ratio of operating income over
total assets (ROA).
2) Growth (GROW)
The measurement of growth opportunities is using ratio of book to market equity.
3) Size (SIZE)
To test the effect of firm size on the optimal debt level, the natural logarithm of
net sales had been used as a measurement.
4) Tangibility (TANG)
That is defined as the ratio of fixed assets to total assets for empirical tests.
5) Non Debt Tax Shield (NDTS)
For empirical measurement, by using the total depreciation from the firm’s profit
and loss account divided by total assets.
6) Volatility (VOLA)
To test the volatility, the measurement is by using average value of the firm’s
total assets over time.
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3.4 RESEARCH DESIGN
3.4.1 INTRODUCTION
This chapter will discuss the procedure and methodology used for the purpose of this
research. The procedure for collecting and method in an attempt of analyzing data which
have relationship between dependent variable (leverage) and the independent variables
(profitability, growth, size, tangibility, non debt tax shield and volatility). The discussion
will provide in depth understanding on the relationship of variables. The study is
covering the period of 10 years from year 1998 until 2007 related to the evidence by
using data of companies listed on the Bursa Malaysia.
3.4.2 DATA COLLECTION
The data and information obtained regarding to this study are from secondary data. All
data is gathered from the year 1998 until 2007.The information sources are from data
stream, journals, books, magazines, newspapers and internet. All these sources were
collected from Bursa Malaysia library, UiTM library, UUM library, USM library and
Annual Report of various companies listed on the Bursa Malaysia from year 1998 until
2007.
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3.4.3 MULTIPLE REGRESSION ANALYSIS
A statistical technique will be used to attempt and establish a functional relationship
between the dependent and independent variables. For this study, only four statistical
techniques have been used in order to test the data. The techniques are Multiple
Regression Equation that is consists of T- statistic, F-statistic and R square. In using this
regression, the estimated regression model based on the some selected variables should
be developed.
General Form of Equation
LEVERAGE = f (PROF, GROW, SIZE, TANG, NDTS, VOLA)
Specific Form of Equation
X = a + β1 X1 + β2 X2 + β3 X3 + β4 X4 + β5 X5 + β6 X6 + e
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LEVERAGE = a + β1 PROF+ β2 GROW + β3 SIZE +
β4 TANG + β5 NDTS + β6 VOLA + e
Where,
LEV = Leverage
a = Constant
β1, β2, β3, β4, β5, β6 = Regression Coefficient
PROF = Profitability
GROW = Growth
SIZE = Size
TANG = Tangibility
NDTS = Non Debt Tax Shield
VOLA = Volatility
e = Error term
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3.4.4 COEFFICIENT OF DETERMINATION (R²)
R2 measures the proportion of the total variation or dispersion in the dependent variable
that is explained by regression equation. Therefore, R2 informs us about how good the
line is best fit and also measures the percentage of a change in the dependent variable that
can be measured or explained by the change in the dependent variables. The value of the
R2 is range from 0 -1.
Coefficient of determination can be divided into three main situations:
If R² = 0
This means none of the change in the dependent variable can be measured by the
change in the independent variables.
The estimated equation is useless ( wrong choice of variables)
The equation has no explanatory power.
If R² =1
This means 100% of the change in the dependent variable can be explained by the
change in the independent variables
The equation has full explanatory power
If R² = 0.85
85% of the change in the dependent variable can be explained by the change in
the independent variables.
Normally the higher the value of coefficient of determination, the higher the explanatory
power of the estimated equation and more accurate for forecasting purposes.
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3.4.5 HYPOTHESES TESTING
T- Statistic
T- Stat is used to determine whether there is a significant relationship between the
dependent and each of the independent variable. If the calculated t stat is greater than
critical t value, independent variable is significant to dependent variable at 95%
confidence level. However if the t-stat is less than critical t value the result is vice versa.
It can be said that the variable is not important and ought to be replaced.
Interpretation of T- Statistic
According to the t-distribution table,
T-stat = Value of coefficient Standard error of coefficient
Computed T- value > Critical F- value, reject H
Computed T- value < Critical F- value, accept H
F – Statistic
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F – Test is used to test the hypothesis that the variation in the independent variable
explained a significant portion of the variation in the dependent variable in the overall
model.
F – Test can be calculated as follows;
Explained variation / (k-1)
Unexplained variation / (n-k)
Where;
F = Critical value
k = No. of Independent Variables
n = No. of Observation
To conduct test
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F =
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The calculated F value must be compared with the critical value from the tabled F
value
If the calculated F value is higher than the tabled value, there is a significant
relationship between the independent variables and the dependent variable.
Therefore the overall model is said to be significant.
The computed F-value will be compared with F distribution table and the result will be
determined by:
Computed F-value > Critical F-value, reject H
Computed F-value < Critical F-value, accept H
RESEARCH SCHEDULE
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WEEK TASK
1 Collection of information for research
2 Discussion with supervisor about the specification draft
3 Develop a detailed specification for the design.
Create a work schedule.
4 - 5 Design proposal.
6 Discuss with supervisor regarding the proposal.
7 Proposal correction and amendment.
8 Complete design proposal.
9 Finding the data.
10 Hypotheses testing.
11 - 14 Work on thesis.
RESEARCH BUDGET
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REFERENCES
Ang, J., J. Chua, and McConnell (1982). ‘The Administrative Costs of Corporate
Bankruptcy: A Note, Journal of Finance, Vol. 37, pp. 219-226.
Booth, A., V. Aivazian, A. Demirguc Kunt, and V. Maksimovic (2001). ‘Capital
Structure in Developing Countries. The Journal of Finance, Vol.56, pp. 87-130
Chingfu Chang, Alice C. Lee and Cheng F. Lee (2008). ‘Determinants of Capital
Structure Choice: A Structural Equation Modeling Approach.
DeAngelo, A., and R. Masulis (1980). ‘Optimal Capital Structure under Corporate and
Personal Taxation, Journal of Financial Economics Vol. 8, pp.3-29.
NO ITEM COST (RM)1) Traveling Costs
Transportation
Accommodation
300
200
2) Stationary
Papers
Ink printer
Photocopying
150
300
150
3) Books and Journals 600
4) Software 300
TOTAL 2000
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Easterbrook, F. (1984). Two Agency Cost Explanations of Dividends, American
Economic Review Vol.74, pp. 650-659.
Fama, E., and K. French (2000). ‘Testing Tradeoff and Pecking Order Predictions about
Dividends and Debt’, working paper, University of Chicago and Sloan School of
Management (MIT).
Galai, D., and R. Masulis (1976). The Option Pricing Model and the Risk Factor of
Stock, Journal of Financial Economics Vol. 3, pp. 631-644.
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