Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
The Capital Structure And Firm’s Financial Leverage In Indonesian
Publicly Listed Cement Industry
Widya Ningsih* and Neneng Djuaeriah**
This is discussing the correlation of firms’ capital structure towards its
financial leverage and focuses on static tradeoff framework and pecking
order framework. This used secondary data of publicly listed cement industry
in IDX30 in ten year period (2003-2012). There are seven ratios being used
in this which are ROA, ROE, EPS, SER, BMR, TG and QR. This use MS.
Excel and E-Views 6. Descriptive statistic, linear regression test using
pooled least square analysis, and hypothesis testing using F-test and T-test,
are the method being used in data analysis. The result shows that capital
structure indicators correlated significantly on financial leverage
simultaneously. Meanwhile, the T-test result shows that only ROA, ROE and
SER correlated significantly with financial leverage.
Keywords: Capital Structures, Leverage, Static Tradeoff Framework, Pecking
Order Framework, Pooled Least Square Analysis.
JEL Codes: G32 and G39
1. Introduction
The cement sales in Indonesia, along with property and car sales, are leading indicators of the
economy. Sales of the material, used mainly in property and construction-related businesses,
jumped 18% to 12.5 million tons in the first quarter of 2012 from the same period last
year(Unditu, 2012).
It is predicted that there will be 10% growth in the cement industry in 2013 (Teresia, 2013).
According to data from Cushman & Wakefield, a worldwide property consulting company, in
2011 around 90% of 294,000 new houses were sold in the Greater Jakarta area, an increase
from 87% in the second quarter of 2010 (Unditu, 2012).
Tariq and Hijazi (2006) explained that the cement industry is a capital-intensive industry and it
requires a much bigger commitment of funds to setup a new business and to expand it in the
future. Capital structure is the first financial decision for in cement industry management.
According to Rahul Kumar (2007), companies may raise money from internal and external
sources. Firms can plow back part of their profits, as the internal source for money, which would
otherwise have been distributed as dividend to shareholders; or by an issue of debt or equity, as
the external source of money. Then if profits rise, the debt holders continue to receive a fixed
interest payment, so that all the gains go to the shareholders. On the contrary, when the reverse
happens and profits fall, shareholders bear all the pain. If times are sufficiently hard, a company
that has borrowed heavily may not be able to repay its debt, which may then become bankrupt
_________________________________________________________________
*Widya Ningsih, School of Accounting and Finance, Swiss German University, Indonesia.
Email: widya.ningsih07@gmail.com
**Ir.Neneng Djuaeriah, MCom, School of Accounting and Finance, Swiss German University, Indonesia.
Email: nenengd@gmail.com
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Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
and shareholders lose their entire investment. Because debt increases returns to shareholders
in good times and reduces them in bad times, it creates financial leverage.
Subramanyam and Wild (2009) stated that the analysis of financial leverage involves several
key elements, one of it is capital structure analysis, which is often measured in terms of the
relative magnitude of the various financing sources, debt and equity. This will focus on the
correlational study of firm’s capital structure and its financial leverage.
The problem statement to be analyzed in this study is Does Capital structure Correlate with
Firm’s Financial Leverage?. To analyze this problem statement, the objective is developed
which hopefully contributes towards a very important aspect of capital structure and firm
financial leverage in Indonesian cement industry.
This is focusing on capital struture and its effect on firm’s financial leverage according to static
tradeoff framework and pecking order framework. The main objectives are as follows. The first
is to analyze and measure the correlation between capital structure and firm’s financial
leverage. The second is to scrutinize the correlation between each chosen ratios of capital
structure and financial leverage in cement industry firms listed on IDX30.
To achieve these objectives, this is organized as follows. Section two reviews the literatures for
relevant theoritical and emphirical work on capital structure and financial leverage focusing on
static tradeoff and pecking order frameworks. Section three presents the methodology and
framework which includes sample and the variable used in the emphirical analysis. Section four
potrays and discusses the data analysis, discussion and statistical results. Section five
presents the conclusion.
2. Literature Review
Since 1958 at least eight theories and theoretical frameworks have been developed related to a
firms’ financial leverage. These are, Irrelevance theory by Modigliani and Miller in 1958, Static
Trade-Off theory by Myers and Majluf in 1984, Asymmetric Information Signaling framework by
Ross in 1977, Leland and Pyle in 1977, Models based on Agency Cost by Jensen and Meckling
in 1976, Pecking Order framework by Majluf and Myers in 1984, the Legal Environment
framework of Capital Structure by La Porta et. al. in 1997, Target Leverage framework or Mean
Reversion theory by Fischer et al. in 1989, and Transaction Cost framework by Williamson in
1988 (Kumar, 2007).
This of capital structure initialing the ers studies of capital structure and financial leverage. As
the nature of knowledge that is always dynamic, ers found that the theory of Modigliani and
Miller is not always appropriate. One er that also studied this capital structure puzzle is Myers
and Majluf, which together they initialing the Static Tradeoff theory and Pecking Order theory
are used in this as the theoretical basis of financial leverage towards capital structure. In his
paper titled The Capital Structure Puzzle, Myers (1984) divides the contemporary thinking on
capital structure into two theories, Static Tradeoff Theory and The Pecking Oder Theory The
First is the Static Tradeoff Theory (STT), which is explains that a firm follows a target debtequity ratio and then behaves accordingly. The benefits and costs associated with the debt
option sets this target ratio. These include taxes, cost of financial distress and agency cost.
Tariq & Hijazi (2006) explained that interest payments are a tax-deductible expense, and
decrease the tax liability thus providing cash savings. Therefore firms will use a higher level of
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Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
debt to take advantage of tax benefits if the tax rates are higher. If the firms incur losses, this tax
benefit will fade away. So if the operating earnings are enough to meet the interest expense
then firms will get the benefit of tax deductibility of interest expenses. The chance of default
increases as the level of debt increases, so there exists an optimal level of debt. If the firm goes
beyond this optimal point, it is more likely that the firm will default on the repayment of the loan.
Bancel and Mittoo (2004) explained that the financial flexibility and earning per share dilution
are the primary concern of managers in 16 Europeans countries in issuing debt and common
stock. Besides managers also value hedging considerations when raising capital. They find that
although a country’s legal environment is an important determinant of debt policy, it plays a
minimal role in common stock policy and the firms financing policies are influenced by both their
institutional environment and their international operations. In conclusion, firms determined their
optimal capital structure by trading off cost and benefits of financing.
The theory of business finance in modern sense starts with the Modigliani and Miller (1958)
capital structure irrelevance propositions. Before Modigliani and Miller, there was no generally
accepted theory of capital structure. They start by assuming that the firm has a particular set of
expected cash flows. When the firm chooses a certain proportion of debt and equity to finance
its assets, all that it does is to divide up the cash flows among investors. Investors and firms are
assumed to have equal access to financial markets, which allows for homemade leverage. The
investor can create any leverage that was wanted but not offered, or investor can get rid of any
leverage that the firm took on but was not wanted. As a result the leverage of the firm has no
effect on the market value of the firm. As the matter of fact their paper led subsequently to both
clarity and controversy, this theory can be proved only under a range of circumstances (Frank
&Goyal, 2005).
Bancel and Mittoo (2004) found in their sample survey of managers from 16 European countries
that over 40% of the managers issued debt when interest rates are low or when the firm’s equity
is undervalued by the market. These findings suggest the managers use windows of opportunity
to raise capital. They further reasoned that managers issue convertible debt because it is less
expensive than straight debt, or to attract investors who are unsure about the riskiness of the
firm.
In short, static trade off theory offers a partial explanation of the factors determining a firm's
choice of leverage as illustrated on table 2.1.
Table 2.1. Determinants of leverage: Static tradeoff framework
Variable
Hypothesis (impact
on leverage)
Positive
Effective marginal tax rate on
firm
Tangibility
Positive
Investment Flexibility
Positive
Profitability
Positive
Sources: Tariq & Hijazi, 2006
Author (Year of
publication)
Frank and Goyal (2005);
Tariq and Hijazi (2006)
Brierley and Bunn (2005)
Bancel and Mittoo (2004)
Frank and Goyal (2002)
The second theory is the Pecking Order Theory (POT) from Myers (1984) and also Myers and
Majluf (1984). In further , Myers (2001) stated that in the Pecking Order Theory the firm will
borrow, rather than issue equity, when internal cash flow is not sufficient to fund capital
expenditures. Thus the amount of debt will reflect the firm’s cumulative need to external funds.
This is nicely summed up by Bartos and Ramalho (2010). They argue that Pecking Order
Theory due to information asymmetries between firms’ managers and potential outside
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Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
financiers, firms tend to adopt a perfect hierarchical order of financing: first, internal funds
(retained earnings) are used; next, in cases where external financing is needed, low-risk debt is
issued; and only as a last resort, when firms are no longer able to issue safe debt, are new
shares issued.
Be accordance with this explanation of funding sources hierarchy, Fama and French (2002) also
explained the reason why management will acted toward the pattern. Equity is subject to
serious adverse selection problems while debt has only a minor adverse selection problem.
The lack of collateral also raises the agency costs of debt relative to equity finance, so such
firms should also have relatively low leverage under the pecking order approach. This is borne
out by UK company accounts data for the quoted sector, studied by Brierly and Bunn (2005),
which confirm that financial leverage has generally been positively related to capital intensity
(the ratio of fixed to current assets), and therefore inversely related to the importance of
intangible assets, since the mid-1980s. This is reassuring for financial stability as it suggests
that it is principally the firms with most collateral available to secure their debt that have raised
leverage to historically high levels in recent years.
Frank and Goyal (2002) also find that under the pecking order theory, one might expect that
firms with few tangible assets would have greater asymmetric information problems. Thus, firms
with few tangible assets will tend to accumulate more debt over time and become more highly
levered. A more common idea is based on the hypothesis that collateral supports debt. It is
often suggested that tangible assets naturally serve as collateral. Hence, collateral (tangible
assets) is associated with increased leverage. It seems that a positive correlation between
levels of tangible assets with leverage is correct, Read (2012) in his study of real options, taxes
and leverage find that large firms ought to borrow more; they are presumably safer and more
likely to pay taxes. Firms with more tangible assets are less likely to be damaged in financial
distress and should therefore have higher target debt ratios.
In short, Myers (1984) presents the pecking order model as a theory both about how firms
finance themselves and about the capital structures that result from pecking order financing. As
it illustrated on table 2.2.
Table 2.2 Determinants of leverage: Pecking order framework
Hypothesis (impact on
Author (Year of
Variable
leverage)
publication)
Profitability
Negative
Level of tangible assets
Fama and French
(2002)
Read, Jr. (2012); Brierly
and Bunn (2005)
Positive
Sources: Bancel & Mittoo, 2004
In his of financial leverage on corporate performance in Nigerian firms, Ojo (2012) opined that
financial leverage causes variability in the returns of shareholders, thus, adds financial risk;
consequently, beta (risk) of a levered firm’s equity will increase as debt is introduced in the
firm’s capital structure. Going by his words on the other hand, financial leverage is seen as the
existence of debt in a firm’s capital structure. Hence, a levered firm is the one that has debt in its
capital structure.
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Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
Firms can finance their assets through a combination of debt and equity. According to Zubairi
(2010), the higher the proportion of debt in the capital structure of a firm, the higher is its default
risk because debt carries a fixed cost which has to be paid irrespective of its operating
performance. Thus, a high proportion of debt makes a firm more vulnerable to default with a
slight decline in operating performance. It is therefore important to be clear on what figures are
being taken from a firm's financial statements for computing this correlation.
Rajan and Zingales (1995) believe that firms with high market-to-book ratios have higher costs
of financial distress which is why they expect a negative correlation. But there may be other
potential reasons for why the market-to-book ratio is negatively correlated with leverage. For
instance, the shares of firms in financial distress (high leverage) may be discounted at a higher
rate because distress risk is priced (as suggested by Fama and French (2002). If this is the
dominant explanation, the negative correlation should be driven largely by firms with low
market-to-book ratios. But in fact, the negative correlation appears to be driven by firms with
high market-to-book ratios rather than by firms with low market-to-book ratios. It is unlikely that
financial distress is responsible for the observed correlation.
Read, Jr. and Myers (2012) explained, by following the trade-off theory the tangibility of asset
have a positive sign towards debt ratios in the cross-sectional test and this result seem
reasonable. Large firms ought to borrow more; they are presumably safer and more likely to pay
taxes. Firms with more tangible assets are less likely to be damaged in financial distress and
should therefore have higher target debt ratios.
3. The Methodology and Model
The primary purpose of this is to study the correlational between capital structure and financial
leverage. Firms are ought to figure out the risk of its source of funding. The correlation between
the capital structures to financial leverage is shown by calculating selected capital structure
ratios of debt and equity.
3.1 Data Set & Sample
The data used in this was acquired from Indonesian Stock Exchange(IDX), which represents the
30 leading stocks in the bourse (The Jakarta Post, 2012) and limited to the ten years company
performance (2003 – 2012). The reason for restricting to this period was that the latest data for
investigation was available for this period. The sample is based on financial statement of two
cement companies listed in IDX30. Finally, the chosen ratios of equity and debt as capital
structure and its correlation to financial leverage. The leverage variables used in this are ROA,
ROE, QR, SER, EPS, BMR and TG.
3.2 Variables
There are seven independent variables being used in this that are ROA, ROE, QR, SER, EPS,
BMR and TG, all this independent variables used together with financial leverage as dependent
variable. The detail of variables is illustrated on table 3.1.
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Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
No.
1. Leverage Ratio
Table 3.2 Variables Formula
Ratio
Formula
Total Debt
Total Equity
2.
Return on Assets
Earnings before Taxes
Return on Equity
Earnings before Taxes
Total Asset
3.
4.
Quick Ratio
Total Shareholder’s Equity
Current Assets – Inventories
Current Liabilities
5.
Shareholders Equity Ratio
Total Shareholder’s Equity
Total Assets
6.
Book-to-Market Ratio
Book Value of Firm
Market Value of Firm
7.
Tangibility of Assets Ratio
Fixed Assets
Total Assets
8.
Basic Earnings per Share Ratio
Net Income – Dividen on Preferred
Stock Average Outstanding Shares
Sources: Subramanyam & Wild, 2009 with modification
3.3 Hypotheses Testing
Since the objective of this is to examine the relationship between capital structure and firm
financial leverage, the makes a set of testable hyphothesis (the Null Hyphothesis H0 versus the
Alternative ones H1).
Hypothesis 1
The first hyphothesis of this is as follows:
H0: Capital structure has negative correlation on firm’s financial leverage
H1: Capital structure has positive correlation on firm’s financial leverage
Hypothesis 2
The second hyphothesis of this is as follows:
H0: ROA, ROE, Quick Ratio, Shareholders Equity Ratio, EPS, Book-to-Market Ratio and
Tangibility of Asset Ratio have insignificant correlation to firm’s financial leverage
H1: ROA, ROE, Quick Ratio, Shareholders Equity Ratio, EPS, Book-to-Market Ratio and
Tangibility of Asset Ratio have significant correlation to firm’s financial leverage
3.4 Model Specification
This used regression model in order to analyze the correlation between capital structures
towards financial leverage. Regression is statistic method to measure thecorrelation between
independent variable with many dependent variables (Field, 2009). The regression model will
be:
Lev = β0 + β1(ROA) + β2(ROE) + β3(QR) + β4(TG) + β5(EPS) + β6(SER) +
β7(BMR) + ε
................................................................................... [ 3.9 ]
Where:
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Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
Lev
TG
ROA
ROE
QR
EPS
SER
BMR
β0-7
ε
=
=
=
=
=
=
=
=
=
=
Leverage
Tangibility of Asset Ratio
Return on Asset
Return on Equity
Quick Ratio
Earnings per Share Ratio
Shareholders of Equity Ratio
Book-to-Market Ratio
Constanta
Error term
3.5 Analysis used in this Study
In this, there are two types of data analysis: descriptive and quantitative analysis. Decriptive
analysis is to describe relevant aspect of phenomena of variable of investigation. The quantitive
analysis applied regression analysis. The correlation between the selected ratios of capital
structure, debt and equity, and financial leverage of cement industry firms listed in IDX30, uses
normal linear regression test, common effect of pooled least square for regressions analysis (Ttest and F-test). There are two tests that should be passed by the regression model, normality
and autocorrelation prior to regression analysis. Good regression models should not have
autocorrelation among its variables. Goodness of fit statisticsare available to test how well the
sample regression function fits the data; that is, how close the fitted regression line is to all of
the data points taken together (Brooks, 2008). For this purpose of analysis the E-View software
used to analyse financial data.
4. The Findings
The two types of analysis have been performed, descriptive and quantitative. The results of
these two types analysis are discussed in this section.
4.1 Descriptive Statistic
Descriptive statistics shows the average, and standard deviation of the different variables of
interest in this. It also presents the minimum and maximum values a variable can achive.
Table 4.3 presents decriptive statistics for two firms of Indonesian cement industry for the period
of 2003 to 2012 for total 20 observations. The mean value of return on equity(ROE) and return
on assets(ROA) is 36.62% and 25.90% and standard deviation is 6.2% and 7.28%. It means
that the value of the ROE can deviate from mean to both side by 6.2% and 7.28%. The
maximum value for ROE is 47.92% and 36.85% while the minimum is 26.27% and 12.59%.
The mean value SER is 69.33% and standar deviation 13.05% The maximum value of 86.68%
while the minimum value is 44.68%. The mean value of BMR is 19.76 with standard deviation is
18.42%. The maximum value is 68.56% and minimum value is 2.04%
The avearge value of quick ratio (QR) is 38.62% and standard deviation is 30.35%. The
maximum value is 97.08% and minimum value is 10.06%. It shows that there is a large gap
between minimum and maximum value of QR.
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Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
The most sgnificant difference is in the earning per share as the mean value is 699.75% with a
large deviation of 535%. The maximum value is 2184% and the minimum value is 31.520%.
This is due to the currency value fluatuation within 10 year investigation.
Table 4.3 Descriptive Statistic
ROA?
ROE?
QR?
TG?
EPS?
SER?
BMR?
Mean
Median
Maximum
Minimum
Std. Dev.
Skewnes
s
Kurtosis
0.3901
0.3106
0.8928
0.1098
0.2460
0.2590
0.2743
0.3685
0.1259
0.0728
0.3692
0.3738
0.4792
0.2627
0.0620
0.3862
0.2459
0.9708
0.1006
0.3035
0.5587
0.5683
0.8024
0.3099
0.1665
699.75
637.50
2184.1
31.520
535.00
0.6933
0.7384
0.8668
0.4468
0.1305
0.1976
0.1323
0.6856
0.0204
0.1842
0.9066
2.6337
-0.3930
2.1227
-0.0372
2.0892
0.9496
2.4079
0.0462
1.7721
1.2628 -0.4818
1.5625
4.3487 RESID_INTP
1.9876
4.3248
Obs.
Cross
sections
20
20
20
20
20
2
2
2
2
2
Quantiles of Normal
LEV?
.4
.2
20
20
20
2
2
2
Sources: E-Views 6 (Descriptive Statistic_file)
4.2 Normality
.0
This used normal probability plot (NPP) to indicate the normality of data distribution, since this
-.2
use small sample data (20 observations). If the variable is in fact from the normal population,
the NPP will be approximately a straight line or in other words if the fitted line in the NPP is
approximately a straight line, it concludes that the variable
of interest is normally distributed
-.4
-.4
-.2
.0
.2
.4
(Gujarati, 2004).
Quantiles of RESID_INTP
Table 4.1: Normality Test Result
RESID_INTP
RESID_SMGR
.4
.15
Quantiles of Normal
Quantiles of Normal
.10
.2
.0
-.2
.05
.00
-.05
-.10
-.4
-.4
-.2
.0
.2
Quantiles of RESID_INTP
.4
-.15
-.10
-.05
.00
.05
.10
.15
Quantiles of RESID_SMGR
RESID_SMGR
From the figure above the
fitted line in the NPP is approximately a straight line. It is concluded
.15
that the variables
of interest are normally distributed.
.10
Quantiles of Normal
4.3 Auto correlation
.05
The result of the autocorrelation is as depicted on the following table:
.00
-.05
-.10
-.15
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Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
Table 4.2 Durbin-Watson statistics of Pooled Least Square
R-squared
0.679413
Adjusted R-squared 0.492404
S.E. of regression 0.175266
Sum squared resid 0.368620
Log likelihood
F-statistic
Prob(F-statistic)
11.55845
3.633044
0.024378
Mean dependent var 0.390141
S.D. dependent var 0.246002
Akaike info criterion 0.355845
Schwarz criterion
0.042448
Hannan-Quinn criter. 0.278094
Durbin-Watson stat 1.924948
Sources: E-Views 6 (PLS Common_file)
From the table above the Durbin-Watson value is 1.924948 which fulfills the second criteria of
the above explanation. So that in conclusion there is no autocorrelation on the explanatory
variables being used.
4.4 Goodness of Fit
The result of the test is illutrated on the following table:
Table 4.4 Multiple Coefficient of Determination R2
R-squared
0.679413
Adjusted
Rsquared
0.492404
Sources: E-Views 6 (PLS Common_file)
Table above shows the value of R2 is 0.492404. In conclusion, the proportion of the variance in
leverage (Y) explained by ROA, ROE, QR, TG, SER, EPS and BMR is about 49%. However,
the rest of multiple coefficient of determination R2 can be explained by other variables that were
not examined in this .
4.5 Regression Analysis
The result of F-Test and T-Test are as follows:
Table 4.5. F-statistic
F-statistic
Prob(F-statistic)
3.633044
0.024378
Sources: E-Views 6 (PLS Common_file)
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Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
Table 4.6: T-statistic
Dependent Variable: LEV?
Method: Pooled Least Squares
Date: 06/23/13 Time: 10:51
Sample: 2003 2012
Included observations: 10
Cross-sections included: 2
Total pool (balanced) observations: 20
Variable
C
ROA?
ROE?
QR?
TG?
EPS?
SER?
BMR?
Coefficient Std. Error
t-Statistic
-4.560075 2.071027 -2.201843
-24.70484 7.461870 -3.310812
17.63922 5.424304 3.251886
-0.162266 0.193981 -0.836505
-0.172299 0.613036 -0.281059
-0.000118 0.000115 -1.029455
7.291141 2.617875 2.785137
0.118748 0.353612 0.335814
Sources: E-Views 6 (PLS Common_file)
Prob.
0.0480
0.0062
0.0069
0.4192
0.7835
0.3236
0.0165
0.7428
Table above shows the value of F-test is 0.024378 which lower than 0.05 (the significant level
chosen by author). So this indicates that joint hypothesis can be supported which conclude all
the independent variables, ROA, ROE, QR, TG, SER, EPS and BMR, together significantly
affected leverage as dependent variable.
From the table above the regression line is:
Lev = – 24.7 (ROA) + 17.64 (ROE) – 0.16 (QR) – 0.17 (TG) + 7.29 (SER) –
0.0001(EPS) + 0.12 (BMR)
..…….…………………………. [ 4.1 ]
Variable
Correlation
ROA
Negative
ROE
Positive
QR
Negative
TG
Negative
SER
Positive
EPS
Negative
BMR
Positive
Sources: Author’s
Variable
T-test
ROA
0.0062
ROE
0.0069
QR
0.4192
TG
0.7835
EPS
0.3236
SER
0.0165
BMR
0.7428
Sources: Author’s
Table 4.7 Correlation result
Increase/Decrease
Unit
24.7
Percent
17.64
Percent
0.16
Times
0.17
Times
7.29
Times
0.0001
Rupiah
0.12
Rupiah
Table 4.7 T-test result
Significant Value
0.05
0.05
0.05
0.05
0.05
0.05
0.05
H1Q1 Accept/Reject
Rejected
Accepted
Rejected
Rejected
Accepted
Rejected
Accepted
H1Q2 Accept/Reject
Accepted
Accepted
Rejected
Rejected
Rejected
Accepted
Rejected
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Proceedings of World Business and Social Science Research Conference
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From the first T-test result above only three variables that have significant effect on leverage
that are ROA, ROE, and SER. Then this result required the second run of T-test which is consist
of these three significant variables only.
Table 4.8 Final T-test
Dependent Variable: LEV?
Method: Pooled Least Squares
Date: 06/27/13 Time: 19:17
Sample: 2003 2012
Included observations: 10
Cross-sections included: 2
Total pool (balanced) observations: 20
Variable
Coefficient Std. Error
t-Statistic
Prob.
C
ROA?
ROE?
SER?
-3.850764
-20.32654
14.79809
5.830823
-2.846161
-3.498506
3.707068
2.892574
0.0117
0.0030
0.0019
0.0106
1.352968
5.810063
3.991858
2.015790
Sources: E-Views 6 (PLS Common_file)
Based on the final T-test result, the model of regression becomes:
Lev = – 3.850764 – 20.32654 (ROA) + 14.79809 (ROE) + 5.830823 (SER) ... [ 4.2 ]
Variable
ROA
ROE
SER
Correlation
Negative
Positive
Positive
Table 4.9: Regression result
Unit
Increase/Decrease
Percent
20.32
Percent
14.79
Times
5.83
H1Q2 Accept/Reject
Accepted
Accepted
Accepted
4.6 Analysis
Based on the above result, hyphothesis one have answered by ROE, SER, BMR that stated
capital structure have positive effect on financial leverage so that this accepted H1 and rejected
H0. This is corresponding to the pecking order theory, especially one that explained by Fama
and French (2002). As the return on assets goes up give a signal that firm is generating more
profit compare to its total assets, in other words firm is likely have sufficient internal funding to
postpone the choice of external funding (debt or equity) as described by pecking order theory.
Then for hyphothesis two, based on the result of F-test statistic it shows us that all the seven
ratios; ROA, ROE, QR, TG, EPS, SER, BMR, together significantly affected financial leverage.
That author can state to reject H0 and accept H1.
This also supported by the final T-test statistical result, individually it proved that there are three
ratios significantly affected financial leverage that are ROA, ROE, SER. This result supported
by the theory of pecking order and static trade off being used. Which Harris and Raviv (1991)
under Pecking Order theory explained that firms with low levels of fixed assets would have more
problems of asymmetric information, making them issue more debt, since equity issues would
only be possible by underpricing them meanwhile firms with higher levels of asset tangibility are
generally larger firms, which can issue equity at fair prices, so they do not need to issue debt to
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Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
finance new investment, so the expected relationship between asset tangibility and debt should
then be negative.
This is also correlate with study of Bancel and Mittoo (2004), explained that return on equity
ratio has significant effect on leverage. The increase of return on equity ratio becomes a signal
that the company generates higher earnings from previous which is becomes a good signal also
for chance of new external financing sources, as explained by the static tradeoff theory.
As a consequence control of the firm will shift from shareholders to bondholders who will try to
recover their investments by liquidating the firm. Because of this threat, a firm may face two
types of bankruptcy costs: direct and indirect costs. Direct costs include the administrative costs
of the bankruptcy process. If the firm is large in size, these costs constitute only a small
percentage for the firm. However, for a small firm, these fixed costs constitute a higher
percentage and are considered an active variable in deciding the level of debt. The indirect
costs arise because of change in investment policies of the firm in case the firm foresees
possible financial distress. To avoid possible bankruptcy, the firm will cut down expenditures on
and development, training and education of employees, advertisements etc. As a result, the
customer begins to doubt the firm’s ability to maintain the same level of quality in goods and
services. This doubt appears in the form of a drop in sales and eventually results in a drop of
the market share price of the firm. This implies that the potential benefits from leverage are
shadowed by the potential costs of bankruptcy(Tariq & Hijazi, 2006).
5. Summary and Conclusion
The hypothesis one is based on the result of F-test, this rejects H0 and accepts H1. The F-test
result shows all the independent variable that are return on assets (ROA), return on equity
(ROE), quick ratio (QR), tangibility (TG), share earnings ratio (SER), earnings per share (EPS)
and book-to-market ratio (BMR) together significantly affected leverage as dependent variable.
The hypothesis two is based on T-test, it was concluded that return on assets (ROA), return on
equity (ROE) and share earnings ratio (SER) has significant correlation on leverage. Meanwhile,
quick ratio (QR), tangibility (TG), earnings per share (EPS) and book-to-market ratio (BMR) has
insignificant correlation on leverage. So this thesis accepts H1 and rejects H0.
Then from the significance of the variables, the regression model becomes:
Lev = – 3.850764 – 20.32654 (ROA) + 14.79809 (ROE) + 5.830823 (SER)
The result indicates that reference theory (pecking order framework and static trade of
framework) is proved to be correlated to the study of capital structure towards financial
leverage.
The conclusions are in confirmation with Fama and French (2002), Harris and Raviv (1991),
Bancel and Mittoo (2004),read, Jr and Myers(2012), Tariq and Hijazi(2006), Frank and
Goyal(2002) and Brieley and Bun (2005) that the capital stucture significantly correlated on
firm’s financial leverage.
It is adviced that the future to broaden the scope of the ’s object to be analyzed, regarding the
correlation of capital structure towards leverage in other industries. Since, different types of
industries may lead to different variables which may significantly correlates with leverage, such
as profitability ratio, company sizes, growth opportunity and gross domestic product (GDP).
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Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
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