Proceedings of 8th Asian Business Research Conference
1 - 2 April 2013, Bangkok, Thailand, ISBN: 978-1-922069-20-7
What Does Capital Structure Decision Depend On? Evidence
from S&P Cnx Nifty and Nifty Junior Firms
N R Parasuraman and P Janaki Ramudu
In this paper we attempted to analyze as to how Indian firms went about in
designing their capital structure during the period 2002 through 2011. The
findings of the study revealed that designing the capital structure depended on
profitability in general and on ROCE and RONW in particular. The hypotheses
testing revealed that, on an aggregate basis, capital structure decisions of
Indian firms were influenced by profitability, solvency, size, tax factor and
growth opportunities. The analysis pertaining to the impact of individual factor
on leverage decision revealed that there was no link between growth
opportunities and the way the firms designed their capital structure. Though
other factors impacted the leverage decision, ‘t’ values of beta co-efficients
indicated that the influence was not so high. Though the extent of reliance
varied among the years, the findings reveal that capital structure decision of
Indian firms depended mostly on profitability followed by short-term solvency,
size and tax laws in India. The study goes into in-depth analysis of capital
structure decisions by Indian firms and made its own contribution to the body of
knowledge on the topic.
Field of Research: Finance
I. Introduction, literature survey and gap in research:
Companies take into account several factors before deciding on an ideal capital
structure for themselves. Theoretically, a lot of postulates have been put forth to focus
on key areas which companies consider in this regard. The theories are based on
specific assumptions that they make. While none of the theories can be dismissed in
their entirety, it is also a fact that empirical evidence does not suggest a complete
validation of any theory. The famous Modigliani-Miller (1958) propositions and other
postulates including the signaling theory and the pecking order theory have their
supporters, but hardly can these be found suitable for explaining specific practices in
companies. Therefore in order to explain the capital structure practices in Indian context
we have undertaken this study on the constituent companies of NSE NIFTY and NIFTY
JUNIOR. The study is organized in a logical flow of (I) introduction, literature survey and
gap in research, (II) objectives and methodology, (III) hypotheses formulation, (IV)
model development, (V) results and discussions and (VI) conclusions.
For a long time, the theory of target capital structure also found favor with academics
and practitioners alike. The theory is elegant in that it suggests that companies have in
mind what they want in the long run and work towards it. However, experience tells us
that barring a few exceptions,
________________________________________________________________
N R Parasuraman, Sdm Institute for Management Development, India. E-mail:
nrparasuraman@sdmimd.ac.in
P P Janaki Ramudu, Alliance University School of Business, India. E-mail: pjanakiramudu@yahoo.co.in
1
Proceedings of 8th Asian Business Research Conference
1 - 2 April 2013, Bangkok, Thailand, ISBN: 978-1-922069-20-7
companies have been unable to consistently follow this principle. Also, it is not clear as
to how the target can be correctly determined and also, the time frame within which the
target needs to be accomplished. The theory of capital structure used to give a lot of
importance to targets so much so that the weighted average cost of capital was said to
be best taken with target weights in mind. Further, the decision regarding capital
structure is also influenced by the extent of present borrowings. A company that is
already under heavy borrowing may be reluctant to go in for new capital intensive
projects entailing debt, for the simple reason that it fears inability to service interest and
repayment schedules on time. Further, banks and lending institutions have their own
norms regarding this. Debt capacity depends not merely on the viability of new projects,
but also on the ability of the company as a whole to service existing debt. Other
considerations like financial distress and agency costs, signaling effects and the
pecking order theory also influence the determination of the target capital structure.
The pecking order theory looks at the tendency of companies to first utilize short term
debt facilities (even for longer term use), and other forms of debt in preference to equity
The principle is that equity raising would involve more questions which could be avoided
in a debt issue. While there is no unanimity, empirical studies have come to varied
results as regards the applicability of these theories in practical decision-making. The
results have been different across countries and over time.
Raghuram Rajan and Zingales (1995) in their research analyzed financing decisions of
the firm in the major industrialized countries. They found that the leverage decision of
the firms across G-7 countries was fairly similar and the factors identified by earlier
studies in the U S were similarly correlated with other countries. They however found
that the theoretical propositions of capital structure decisions were still unresolved. This
study has made a significant contribution to the body of knowledge in capital structure
theories.
John R. Graham (2000) estimated as to how big would be tax benefits of the debt
employment and the behavior of COMPUSTAT sample firms. The paper reveals that a
firm could continue to be issuing the debt till the point where marginal tax benefits start
declining. His research found that large, liquid and profitable firms with low expected
distress costs were conservative in using the debt component. The study brought out
the existence of debt conservative policy. This paper and its findings are of good
relevance to the present study in terms of providing enough support to the validity of
research.
Laurence and Varouj et al (2001) assessed if the capital structure theory is still
applicable across ten developing countries with variations in institutional structures.
They evidenced that capital structure decision of developing countries depended on
similar factors as in case of developed countries. They however, concluded that the
specific country factors would also influence and play a key role in capital structure
decision of the country concerned. Though the findings of their study suggested the
portability of modern finance theory, they felt that there is much to be done to
understand the capital structure decision.
2
Proceedings of 8th Asian Business Research Conference
1 - 2 April 2013, Bangkok, Thailand, ISBN: 978-1-922069-20-7
Jianjun Miao (2005) provided an equilibrium model to test the relationship between
optimal capital structure and industry dynamics. The study revealed that while the firms
make financing and investment decisions subject to idiosyncratic technology shocks, it
proved that the capital structure decisions reflect the tradeoff between tax benefits of
the debt component and associated bankruptcy and agency costs. The study also found
out that the industries with growth opportunities were low in leverage.
Ilya A. Strebulaev (2007) tried to find out if the tests of capital structures theory meant
what they say. Using a calibrated dynamic trade-off model to stimulate firms’ capital
structure paths, he found that the results of standard cross-sectional tests were
consistent with those reported in empirical literature.
Soku Byoun (2008) raised and tried to answer a research question “How and when do
firms adjust their capital structures toward target?” He developed and suggested a
financing needs-induced adjustment framework to examine the dynamic process by
which firms adjust their capital structures. The results of his research indicated that the
firms move towards target capital structure when they face a financial deficit/surplus, but
not in the manner hypothesized by the traditional pecking order theory. This paper
clearly evidences that the pecking order theory of financing need not be held good all
times.
Xin Chang and Sudipto Dasgupta (2009) in their paper attempted to find out if the firms
had a debt ratio target which is a primary determinant of financing behavior. Up on
addressing the issue, the authors found the evidence in support of the target debt ratio
based leverage decisions of the firms. However, on an overall basis they evidenced that
the existing tests of target behavior did not have any power to reject alternatives.
Murray Carlson and Ali Lazrak (2010) developed a model to predict if the leverage of
the U S based firms trades off the tax benefit of the debt against the utility cost of expost asset substitution. The authors found a positive relationship between cash to stock
and leverage ratios of the firms.
Erol (2011) develops what he calls the triangular relationship between firm size, capital
structure choice and performance. The paper looks at trade-off theory, pecking order
theory and irrelevance theory on a relative basis. The sample relates to the period 1994
to 2003 of 114 firms listed in the Istanbul Stock Exchange. Depending on how the size
expansion gets financed, the impact on performance will change. Asset expansions
with debt are seen to increase risk, which is what the trade-off theory says in any case.
Berumeu, Beyer and Dye (2011) look at a model for determining capital structure, along
with the extent of voluntary disclosure and cost of capital. They say “We establish a
hierarchy of optimal securities and disclosure policies that varies with the volatility of the
firm's cash flows. Debt securities are often optimal, with the form of debt—risk-free,
investment grade, or "junk"—varying with the firm's cash flow volatility. Though the
model predicts a negative association between firms' cost of capital and the extent of
information firms disclose, more expansive voluntary disclosure does not cause firms'
3
Proceedings of 8th Asian Business Research Conference
1 - 2 April 2013, Bangkok, Thailand, ISBN: 978-1-922069-20-7
cost of capital to decline. Mandatory disclosures alter firms' voluntary disclosures, their
capital structure choices, and their cost of capital”.
Brav (2009) analyses the data from public and private firms in the U.K, and finds that
private firms have a heavy dependence on debt financing and are more sensitive to
performance changes when it comes to deciding on the capital structure. The desire to
maintain control and possible information asymmetry have resulted in private equity
being costlier than public equity.
Jagadish (2011) examines the impact of financial risk in capital structuring decisions. A
sample of 59 companies listed in Indian stock exchanges has been examined for the
period 1997 to 2007. Different statistical tools of analysis have been used based on the
total asset value of the company. Financial risk in general and the risk of fluctuations in
ROE in particular are seen to have an impact on the extent of debt in the capital
structure. However, the study cannot be termed as comprehensive enough in the
sense that other factors would also influence and the testing may not have fully isolated
the financial risk angle completely.
A strategic side to capital structure decisions is brought out by Mats (2010). In his
paper, the author looks at ways of improving the bargaining position with organized
labor using leverage. The author’s argument is that since high cash flow liquidity would
result in higher demands from labor, firms would be better off having a higher level of
debt and consequently higher demands on debt service. This collective bargaining
power is a great influencer for the capital structure decision, according to the findings of
the author. However, this would largely depend on the country of study and also the
legislation prevalent.
Chang and Yu (2010) look at how capital structure choice would have an influence on
secondary market stock prices. The authors say “We show that the capital structure
decision affects traders' incentives to acquire information and subsequently, the
distribution of informed traders across debt and equity claims. When information is less
imperative for improving its operating decisions, a firm issues zero or negative debt (i.e.,
holding excess cash reserves) in order to reduce socially wasteful information
acquisition and the liquidity premium associated with it. When information is crucial for a
firm's operating decisions, the optimal debt level is one that achieves maximum
information revelation at the lowest possible liquidity cost. They say that their model can
explain why many firms consistently hold no debt. It also provides new implications for
financial system design and for the relationship among leverage, liquidity premium,
profitability, and the cost of information acquisition
Gill and Nahum (2011) argue that profitability is a crucial factor in capital structure
decisions. The sample is from American manufacturing and service companies. A
correlation and regression analysis is used to find the relationship of profitability with
capital structure parameters. The authors state that the findings of the study show a
positive relationship between i) short-term debt to total assets and profitability, ii) longterm debt to total assets and profitability, and iii) total debt to total assets and
4
Proceedings of 8th Asian Business Research Conference
1 - 2 April 2013, Bangkok, Thailand, ISBN: 978-1-922069-20-7
profitability in the manufacturing industry. This paper offers useful insights for the
owners/operators, managers, and lending institutions based on empirical evidence”
Mukherjee and Mahakud (2012) raise the question of whether the pecking order theory
and the tradeoff theories have to necessarily give diverse conclusions. The sample is
from Indian manufacturing companies for the period from 1993-94 to 2007-08. The
findings suggest that they are actually complimentary to each other. The authors also
state that the firms have not been showing a marked tendency to go towards the target
and the adjustment speed is around 40%. The testing method of finding target structure
adjustment is open to debate.
Specific aspects of manager contracts and their influence on capital structure are
examined by Bhagat, Bolton and Subramanian (2011). They find that long term debt
has a tendency to decline with long term risk; and short term risk shows a declining
trend with short term risk. The analysis is comprehensive enough, but the testing
methodology is open to question.
Fan, Titman and Twite (2012) give an international comparison of capital structure, with
emphasis on the time of maturity of debt. The sample is taken from 39 developed and
developing countries. The authors find that the legal and tax system in individual
companies dictate the preferences of capital structure in many countries. So much so,
they find that companies in countries with greater levels of corruption use more of debt
and particularly short-term debt. In countries having greater tax deductions from
leverage, more debt is seen to be used.
Specifically in India, several factors have been influential in capital structure decisions.
These factors include volatility in interest rates, changes in legislation on taxation of
dividends and differences in planned retention ratios. Following the liberalization policy
of the Government in early 1990s companies have become more capital intensive.
More emphasis is being laid by companies on shareholder value creation and market
value added. The stock markets have also demonstrated great sensitivity to changes in
debt patterns and borrowing. Empirical studies have shown that growth companies and
companies with high market capitalization have not shown any specific theoretical
framework in their capital structure decisions.
Academic studies on Capital Structure the world over have looked into many diverse
angles. Studies have reached conflicting conclusions on what drives capital structure
policy. Most papers have sought to test basic postulates, the Pecking Order theory, the
trade-off theory and the theory of Asymmetric Information. Most of these studies pertain
to companies in the United States and Europe. The conclusions have been sharply
different and have depended on the type of sample and the country from where these
are taken. What we have endeavored to do in this paper is to show the broad
relationship between actual capital structure and other key parameters like profitability
and asset growth etc. Although the model is straightforward, we feel that this will go a
long way in showing a basic trend in this policy.
5
Proceedings of 8th Asian Business Research Conference
1 - 2 April 2013, Bangkok, Thailand, ISBN: 978-1-922069-20-7
II.
Objectives and methodology:
Keeping in view the significance of leverage decision, as it has been highlighted in
literature review, the study aims at:
1. Investigating into as to how Indian firms went about in raising the funds to meet
with the growing financial needs of the business over the last five years (2007
through 2011).
2. Identifying the factors that influenced Indian firms’ financing decisions and
3. Testing statistically as to which specific factors exercised influence, and to what
extent, on Indian firms’ capital structure decisions.
While there are many companies constituting corporate India, the researchers have
taken the companies constituting S&P CNX NIFTY and CNX NIFTY JUNIOR numbering
a total of hundred companies. In order to make the study and its findings more
meaningful, banking and financing companies have been eliminated from the sample
size as capital structure does not mean much in these firms. Also the companies for
which there was no sufficiency of the data were eliminated. Thus after such elimination
process, data of seventy three companies have used for analysis purpose. The
rationale behind in choosing these companies is that as it has been historically
observed, the market capitalization of these indices (popularly known as NIFTY and
NIFTY JUNIOR) happens to be high in Indian stock market. Also the companies
constituting these two indices have been found to be reasonably large in size. And it is a
well known fact that the size of the firm matters a lot while deciding on the capital
structure pattern. While it is argued that only seventy three companies may not be
sufficient to arrive at the conclusions, the researchers feel that it is a good
representative figure to provide a meaningful lead to understand as to how Indian
companies went about capital structure decisions.
The study depends fully on primary data of the companies for the years 2007 through
2011. While most of the companies reported the results on fiscal basis, the researchers
found some companies reporting on calendar basis in some years. In order to ensure
the inclusion of the data for all the companies and all the years chosen, the researchers
have considered both for the purpose of analysis. It may however be noted that, if a
company reported the results both in March and December in a year, the latest data, i.e
that of December has been taken into account. This procedure has been followed for all
such companies having two reporting figures in a year to eliminate any bias in data
consideration. Though we find many corporate databases, we depended on Capitaline
database (www.capitaline.com), a leading corporate database in India and therefore we
sincerely acknowledge the support of this database in terms of providing required data
for our study.
III.
Hypotheses formulation:
Designing of capital structure is a function of many factors like the profitability, liquidity,
size, growth and tax proportion of the firm. However there may be differences in the
ways that these factors would impact the decision. In order to provide meaningful
6
Proceedings of 8th Asian Business Research Conference
1 - 2 April 2013, Bangkok, Thailand, ISBN: 978-1-922069-20-7
guidance and to answer the research question, the following hypotheses have been
formulated and tested.
H1: Capital structure design of the Indian firms does not depend on profitability,
solvency, size, tax proportion and growth factors.
H2: Increasing profitability of the Indian companies does not influence the capital
structure pattern.
H3: The Indian firms that are solvent in the short-term do not depend on debt
financing.
H4: The capital structure pattern of Indian firms does not depend on size
H5: The capital structure position of Indian firms does not depend on tax proportion.
H6: The Indian firms growing significantly in short-term do not depend on debt
financing.
H7: Time factor does not have any impact on the capital structure decision of Indian
firms.
IV.
Model development:
For the purpose of testing hypotheses and as a part of analysis, multiple regression
model has been used to test the results statistically. The following is the description of
the model fit used in the study.
Model 1: Relationship between leverage as criterion variable and profitability, solvency,
size, tax proportion and growth as predictor variables.
Leverage = α + β (ROCE) + β (RONW) + β (NPM) + β (CR) + β (Size) + β (TP)
+ β (Growth) + µ
(1)
Model 2: Relationship between leverage and profitability.
Leverage = α + β (ROCE) + β (RONW) + β (NPM) + µ
(2)
Model 3: Relationship between leverage and solvency.
Leverage = α + β (CR) + µ
(3)
Model 4: Relationship between leverage and size.
Leverage = α + β (Size) + µ
(4)
Model 5: Relationship between leverage and tax proportion.
Leverage = α + β (TP) + µ
(5)
Model 6: Relationship between leverage and growth.
Leverage = α + β (Growth) + µ
(6)
In the above models Degree of Leverage (here after Leverage or capital structure) is the
dependent variable or also known as criterion variable measured as debt up on equity.
The following are the Independent variables or also known as predictor variables.
ROCE
=
RONW
=
NPM
CR
=
=
Return On Capital Employed measured as Profit Before Interest
and Tax
(PBIT) up on Capital Employed (CE).
Return On Net Worth measured as Profit After Tax (PAT) up on Net
Worth (NW).
Net Profit Margin measured as Profit After Tax (PAT) upon Sales.
Current Ratio measured as Current Assets (CAs) up on Current
Liabilities (CLs).
7
Proceedings of 8th Asian Business Research Conference
1 - 2 April 2013, Bangkok, Thailand, ISBN: 978-1-922069-20-7
Size
TP
=
=
Growth
=
Size measured as Logarithm (base 10) of Total Assets.
Tax Proportion measured as Tax Expenditure up on Profit Before
Interest and Tax (PBIT).
Growth Rate in assets measured as Total Assets in the year ‘t’ up
on the Total Assets in the year ‘t-1‘
Apart from the above connotations, ‘α’ is the intercept and ‘β’s are the coefficients of the
predictor variable concerned which indicate the variance in the criterion variable (i.e
leverage) caused by predictor variables and ‘µ’ is the error term of the model
concerned. These abbreviations and connotations have been used in the study
frequently to denote the variables and their influence. The following table indicates the
proxies for various parameters in the above models.
Parameter

Profitability
Solvency
Size
Tax Factor
Growth






Proxies
ROCE i.e Return On Capital
Employed
RONW i.e Return On Net Worth
NPM i.e Net Profit Margin
CR i.e Current Ratio
Log 10 of Assets
TP i.e tax as the percentage of
PBIT
Year on Year Growth Rate in total
assets
The above models have been run in Statistical Package for the Social Sciences (SPSS)
for windows version 15.0. The regression has been run using ENTER and STEP
methods. While ENTER method considers all the predictor variables in the equation,
STEP method considers predictor variables best correlated with the criterion variable in
the equation as the first stage. Followed by this, the remaining predictor variables
having the highest partial correlation with the criterion variable, controlling for predictor
variables are entered and those variables having lowest partial correlation are
eliminated. This process in STEP method is repeated at each stage “partialising” for
previously entered predictor variables until the addition of a remaining predictor variable
does not increase R2 by a significant amount.
The linear regression model has been run separately for each year using criterion and
predictor variables of all seventy three sample companies. While there may be some
arguments that either ENTER method or STEP method would suffice, the idea behind
running the models through both the methods is to extract the results and interpret the
same in all possible ways. This would help to analyze if the pattern of capital structure
depended on any one or more predictor variables significantly throughout the study
period. Followed by this ANOVA also has been run across all the years to test H7 and
see if the leverage of sample companies varied over a period of time. The strength of
the linear relationship between criterion and predictor variables has been analyzed
using Co-efficient of Multiple Determination, widely known as R2 the value of which must
technically lie between ‘0’ and ‘1’. The value of R2 indicates as to what extent variance
8
Proceedings of 8th Asian Business Research Conference
1 - 2 April 2013, Bangkok, Thailand, ISBN: 978-1-922069-20-7
in criterion variable is explained by predictor variables. Therefore, higher the value of
R2, higher is the total variation in criterion variable explained by predictor variables. This
in turn indicates that the higher level of significance is greater is the strength of linear
regression model.
However in order to test the statistical significance of the model and the hypothesis, Sig.
F (also known as ‘p’ value) has been used. This statistic would help us know if the
equation on an overall basis is significantly fit enough. The ‘p’ value generated in SPSS
analysis helps us to accept or reject the null hypotheses. In the present study null
hypotheses have been tested at 5% significance level and therefore, if the ‘p’ value is
less than 0.05, then the null hypothesis is rejected and concluded that the model holds
significant. In other words, this in the present study means that capital structure pattern
would depend on profitability, solvency, growth, size and tax factors wherever relevant.
As it has been mentioned earlier, the hypotheses are tested across all the companies
for every year. Apart from testing the significance of overall model, relative influence of
each predictor variable on criterion variable has been tested through ‘t-test’ the values
of which are generated in SPSS analysis. This would help us to know as to which
predictor variables contribute and do not contribute significantly to the regression model.
Testing this at 5% significance level, if the ‘p’ value for the ‘t-test’ is less than 0.05, then
the null hypothesis is rejected and concluded that the predictor variable concerned has
significant influence on the overall model. In order to study the special contribution of
each predictor variable to the equation, beta coefficients (β) of the un-standardized
variables are taken into account. Apart from these statistics, Standard Error term (or
simply S.E) and Durbin Watson’s coefficient (or simply D.W) have been used to analyze
the results. Standard error indicates the variance in the mean values and therefore
higher the standard error, more erroneous and less reliable is the model fit and vice
versa. Durbin Watson’s coefficient indicates the extent of auto correlation among the
error terms of the models of various samples. As a general rule, Durbin Watson’s
coefficient closer to 2 indicates that there is no correlation among the error terms which
is good sign to justify the model. Although adjusted R2 is captured in the tables, it is not
interpreted as the sample size of the firms is not very high.
V.
Results and discussions:
The analysis and interpretation has been carried out in the order of testing the impact of
all predictor variables, followed by profitability predictors, solvency predictor, size
predictor, tax proportion predictor and growth predictor on leverage position of the
sample firms. It may also be noted that STEP method of multiple regression analysis
has been carried out in case of overall model (Model 1) and profitability model (Model 2)
as there are more than one predictor variable in those cases. As there is only one
predictor variable in case of solvency, size, tax proportion and growth analyses only
ENTER method has been used. Since it is space consuming activity, the researchers
preferred to capture SPSS output and results in form of tables which are mostly self
explanatory in nature. However, a few comments and observations are made out of the
results for the purpose of better understanding and implications of regression results.
9
Proceedings of 8th Asian Business Research Conference
1 - 2 April 2013, Bangkok, Thailand, ISBN: 978-1-922069-20-7
Table 1: Summary statistics of the model 1 pertaining to overall analysis between leverage as the
criterion variable and profitability, solvency, size, tax and growth as predictor variables
(ENTER method)
Year
α
R
R2
Adj. R2
Std. Error
Sig. F
D.W
2007
0.048
0.872
0.76
0.734
0.522
0
2.04
2008
0.915
0.683
0.466
0.409
0.417
0
1.91
2009
0.76
0.665
0.442
0.382
0.407
0
0.19
2010
1.075
0.715
0.511
0.458
0.401
0
2.17
2011
0.916
0.649
0.421
0.359
0.413
0
2
Un-standardized beta Coefficients and ‘t’ statistic
Predictor
ROCE
RONW
CR
NPM
Size
TP
Growth
‘β’ and
‘β’ and
‘β’ and
‘β’ and
‘β’ and
‘β’ and
‘β’ and
Year
(‘t’ value) (‘t’ value) (‘t’ value) (‘t’ value) (‘t’ value) (‘t’ value)
(‘t’ value)
-9.15*
0.10*
-0.12
0
-0.06 (1.24
2007
0.48 (1.87)
(-11.92)
(11.87)
(-1.35)
(-0.19)
0.53)
(1.80)
-2.87*
0.027*
-0.16*
0.00
0.04
-1.25*
2008
0.053 (0.35)
(-3.16)
(2.56)
(2.92)
(-1.18)
(0.36)
(-2.03)
-2.27
0.02
-0.09*
0.00
0.09
-1.19*
-0.05
2009
(-1.96)
(1.25)
(-2.99)
(-1.32)
(0.87)
(-2.05)
(-0.20)
-4.77*
0.04
-0.14*
0.00
0.05
-0.47
-0.21
2010
(-3.48)
(2.89)
(-2.81)
(-1.48)
(0.45)
(-0.64)
(-1.06)
-0.87
0.00
-0.11*
0.00
0.15
-1.9*
-0.29
2011
(-1.72)
(0.78)
(-2.45)
(-1.56)
(1.33)
(3.09)
(-1.17)
* Significant at 5% level
10
Proceedings of 8th Asian Business Research Conference
1 - 2 April 2013, Bangkok, Thailand, ISBN: 978-1-922069-20-7
Table 2: Summary statistics of the model 1 pertaining to overall analysis between leverage as the
criterion variable and profitability, solvency, size, tax and growth as predictor variables
(STEP method)
Adj.
Std.
R2
Sig.
Predi
‘β’ and
Year Model
R
R2
D.W
2
R
Error Change
F
ctors
(‘t’ value)
1
0.397
0.158
0.146
0.936
0.158
0.001
2
0.851
0.723
0.715
0.54
0.566
0
2007
2008
3
0.862
0.743
0.732
0.524
0.02
0.025
1
0.417
0.174
0.162
0.496
0.174
0
2
0.533
0.284
0.263
0.465
0.11
0.002
3
0.62
0.385
0.358
0.434
0.101
0.001
4
0.671
0.45
0.417
0.413
0.065
0.006
1
0.472
0.223
0.212
0.458
0.223
0
2
0.561
0.315
0.296
0.433
0.092
0.003
2009
3
0.629
0.396
0.37
0.409
0.081
0.003
1
0.449
0.202
0.191
0.489
0.202
0
2
0.621
0.386
0.369
0.432
0.184
0
2010
3
0.698
0.487
0.465
0.398
0.101
0
1
0.466
0.217
0.207
0.458
0.217
0
2
0.552
0.305
0.285
0.434
0.087
0.004
2011
3
0.61
0.372
0.345
0.416
0.068
0.008
ROCE
ROCE
RONW
ROCE
RONW
TP
TP
TP
CR
TP
CR
ROCE
TP
CR
ROCE
RONW
ROCE
ROCE
RONW
ROCE
RONW
CR
ROCE
ROCE
RONW
ROCE
RONW
CR
ROCE
ROCE
CR
ROCE
CR
TP
1.35 (6.03)
-8.72 (-13.27)
0.10 (11.96)
-9.47 (-13.21)
0.10 (12.49)
1.49 (2.29)
-2.18 (-3.86)
-2.58 (-4.75)
-0.17 (-3.28)
-1.96 (-3.63)
-0.19 (-3.83)
-0.79 (-3.36)
-1.11 (-1.86)
-0.16 (-3.23)
-3.11 (-3.66)
0.03 (2.83)
-1.17 (-4.54)
-3.99 (-4.36)
0.036 (3.09)
-3.91 (-4.36)
0.03 (3.06)
-0.08 (-3.05)
-1.17 (-4.27)
-5.48 (-5.67)
0.05 (4.61)
-5 (-5.56)
0.04 (4.19)
-0.17 (-3.71)
-1.12 (-4.47)
-1.24 (-4.87)
-0.14 (-2.98)
-0.91 (-3.35)
-0.13 (-2.83)
-1.61 (-2.74)
1.97
1.91
1.98
2.22
1.90
11
Proceedings of 8th Asian Business Research Conference
1 - 2 April 2013, Bangkok, Thailand, ISBN: 978-1-922069-20-7
Table one above contains the details pertaining to regression model, through ENTER
method that reveal if the leverage decision of the firms depended significantly on all
predictor variable together. Significance ‘F’ in all the years was below 0.05 and hence
we understand that leverage decision of Indian firms depended significantly on
profitability, solvency, size, tax proportion and growth. At the outset, we therefore reject
null hypothesis, H1 and prove that capital structure design of Indian firms depended on
the profitability, solvency, size, tax proportion and growth factors together in every year.
However, while ‘R’ is found to be reasonably high in all the years, ‘R 2’ is found to be low
in most of the years. Second part of the table 1 also contains un-standardized beta coefficients and ‘t’ values of predictor variables. The values indicate that the capital
structure design of the firms did not depend significantly on size and growth of the firms
while it significantly depended on profitability, solvency and tax proportion in most of the
years. From the same table we also observe that of all the predictor variables, ROCE
had very high influence on capital structure as revealed by its beta co-efficient (B) and ‘t’
statistic while the other predictors’ influence was relatively lower in almost all the years.
Table 2 contains the details pertaining to step-wise multiple regression of model 1. As
we see, size and growth variables never appeared as the determinants of the capital
structure throughout the study period which was a similar observation even in ENTER
method. This is therefore an important finding of the study that Indian firms did not
consider the size and growth opportunities while designing their capital structure
position. This finding is not in line with the theoretical proposition that size and growth
factors play a vital role in capital structure decision of the firms. While tax proportion
was the most influencing factor in the year 2008, two proxies of profitability (i.e. ROCE
and RONW) were the most dominant determinants of capital structure in the rest of the
individual years. On the other hand solvency (CR) and another proxy of profitability (i.e.
NPM) had very least influence on capital structure decision of the firms. In fact NPM
never appeared as the determinant variable throughout out the study period. The Durbin
Watson statistic in both tables 1 and 2 in every year indicates that auto correlation
among the error terms was within the limits. This in turn indicates that the models are
justified to explain as to what factors have influenced the leverage of Indian firms and to
what extent also.
12
Proceedings of 8th Asian Business Research Conference
1 - 2 April 2013, Bangkok, Thailand, ISBN: 978-1-922069-20-7
Table 3: Summary statistics of the regression model 2 between leverage and
profitability (ENTER Method).
Adj. Std.
Sig. Predictor ‘β’ and
D.W
Year
Α
R
R2
(‘t’ value)
R2
Error
F
ROCE
-8.74* (-13.31)
RONW
0.10* (12)
2007 0.43 0.85 0.72 0.71 0.53
0
2.05
NPM
-0.001 (-1.1)
ROCE
-4.15* (-5.62)
RONW
0.04* (4.5)
2008 0.62 0.61 0.37 0.34 0.43
0
1.74
NPM
-0.001 (-1.16)
ROCE
-3.96* (-4.13)
RONW
0.03* (3.01)
2009 0.70 0.57 0.32 0.29 0.43
0
1.83
NPM
0 (-1.11)
ROCE
-5.58* (-5.78)
RONW
0.05* (4.71)
2010 0.66 0.63 0.40 0.38 0.42
0
2.04
NPM
-0.001 (-1.63)
ROCE
-1.47* (-3.21)
RONW
0.003 (0.75)
2011 0.73 0.48 0.23 0.20 0.46
0
1.78
NPM
0 (-0.90)
* Significant at 5% level
Table 4: Summary statistics of the regression model 2 between
leverage and profitability (STEP Method).
2
Year Model R
R
Adj. Std. R2
Sig. Predictors ‘β’ and
(‘t’ value)
R2 Error Change F
-2.61 (-3.64)
1
0.39 0.15 0.14 0.93
0.15
0 ROCE
ROCE
-8.72 (-13.27)
2007
2
0.85 0.72 0.71 0.54
0.56
0
RONW
0.10 (11.96)
-0.97 (-3.88)
1
0.41 0.17 0.16 0.49
0.17
0 ROCE
ROCE
-4.14 (-5.59)
2008
2
0.6 0.36 0.34 0.44
0.18
0
RONW
0.04 (4.49)
-1.17 (-4.55)
1
0.47 0.22 0.21 0.45
0.22
0 ROCE
ROCE
-3.93 (-4.10)
2009
2
0.56 0.31 0.29 0.43
0.08
0
RONW
0.03 (2.97)
-1.18 (-4.30)
1
0.45 0.20 0.19 0.48
0.20
0 ROCE
ROCE
-5.43 (-5.58)
2010
2
0.62 0.38 0.36 0.43
0.17
0
RONW
0.05 (4.51)
-1.21 (-4.48)
2011
1
0.47 0.22 0.21 0.45
0.22
0 ROCE
D.W
2.04
1.76
1.86
2.04
1.85
Tables 3 and 4 contain model summary statistics and beta co-efficients along with ‘t’
values of the regression model between leverage and profitability. While table 3
contains the details of model through ENTER method, table 4 contains that of STEP
13
Proceedings of 8th Asian Business Research Conference
1 - 2 April 2013, Bangkok, Thailand, ISBN: 978-1-922069-20-7
method. A few important observations about details through ENTER method (in table 3)
are as follows. The strength of the model, as revealed by ‘R2’ was reasonably high in
the years 2007, 2008 and 2010 while in the rest of the years it was very weak. This
indicates, that the relationship explained between the leverage (criterion variable) and
the profitability (predictor variables being ROCE, RONW and NPM) was very low in
these years. However, the ‘Sig. ‘F’ in all the years was less than 0.05 and hence we
reject null hypothesis, H2 and prove that in case of Indian firms, the increasing
profitability influenced leverage decision significantly as a whole during the period under
review. But beta coefficients and their ’t’ values indicate that ROCE and RONW were
the two predictor variables of profitability that influenced leverage significantly
throughout the study period except RONW being insignificant only in the year 2011.
This is in fact in line with the observation made in the overall analysis as we can see in
tables 1 and 2. It is therefore understood that Indian firms did take NPM into account as
a factor to be concerned about, while designing their capital structure position. The ‘t’
values of beta co-efficients indicate that the impact of ROCE on leverage was the
highest when compared to that of RONW. A few important observations from STEP
method as captured in table 4 are as follows. Like in the case of ENTER method, in
STEP method also, the strength of the model, as aptly revealed by R2, was found to be
very weak during the entire period of study barring the second model in the year 2007.
The highest portion of the relationship explained by the independent variables was as
low as 0.15. The standard error was also very high which indicates that the model was
highly erroneous throughout the study period. When it comes to the question of
predictor variables, like in the case of ENTER method, only two proxies of profitability
(i.e ROCE and RONW) had significant impact on leverage throughout the study period.
Based up on these observations, we understand that the leverage decision of Indian
firms depended on ROCE and RONW in the years 2007 through 2011. However, the
beta co-efficients and their ‘t’ values indicate that the impact of profitability variables was
relatively low. The Durbin Watson’s co-efficient in both the methods (i.e ENTER and
STEP methods in tables 3 and 4) was close to two and hence we conclude that there
was no auto co-relation among the error terms of the models during the study period
and hence the models being justified. This in turn means that the models were found to
be fit enough to explain and prove that the leverage depended on profitability variables.
Thus from all angles point of view, as revealed by the statistical analysis we understand
that the capital structure design of Indian firms very much depended on ROCE and
RONW significantly.
14
Proceedings of 8th Asian Business Research Conference
1 - 2 April 2013, Bangkok, Thailand, ISBN: 978-1-922069-20-7
Table 5: Summary details of regression model 3 between leverage and solvency
Std.
‘β’ and
Year
α
R
R2
Adj. R2
Sig. F
Predictor
(‘t’ value)
Error
-0.05
2007 0.71 0.04 0.00
-0.02
1.01
0.73
CR
(-0.33)
-0.12
2008 0.68 0.23 0.05
0.04
0.53
0.05
CR
(-1.99)
-0.07*
2009 0.59 0.24 0.06
0.04
0.50
0.03
CR
(-2.14)
-0.16*
2010 0.75 0.30 0.09
0.08
0.52
0.00
CR
(-2.71)
-0.12*
2011 0.64 0.26 0.07
0.05
0.50
0.02
CR
(-2.30)
*Significant at 5% level.
Table 5 contains the details pertaining to the relationship between leverage, the criterion
variable and the solvency (i.e. CR) the predictor variable. Values of R and R2 reveal
that the strength of the model in every year has been very weak. However as Sig. ‘F’ in
the years 2009, 2010, 2011 was less than 0.05, we reject null hypothesis, H3 and prove
that Indian firms that are solvent in the short-run depended on debt financing in these
years. However, Sig. ‘F’ indicate that the leverage did not depend on solvency in the
years 2007 and 2008. Though the solvency influenced the leverage significantly in a few
years, the coefficient and their beta values of CR (proxy for solvency) indicate that the
impact was very low. This in turn reveals that the solvency indeed did not have any
major impact on capital structure decision of Indian firms during the study period.
Table 6: Summary details of regression model 4 between leverage and
size
Adj.
Std.
Sig.
‘β’ and
Year
α
R
R2
Predictor
(‘t’ value)
R2
Error
F
0.23
2007 -0.21 0.12 0.01 0.00
1.01 0.29
Size
(1.04)
0.27*
2008 -0.52 0.27 0.07 0.06
0.52 0.02
Size
(2.37)
0.34*
2009 -0.84 0.35 0.12 0.11
0.48 0.00
Size
(3.24)
0.37*
2010 -0.97 0.36 0.13 0.12
0.51 0.00
Size
(3.28)
0.36*
2011
-1
0.36 0.13 0.12
0.48 0.00
Size
(3.35)
*Significant at 5% level.
Table 6 contains the details pertaining to the relationship between leverage and size of
sample firms. As in the case of relationship between leverage and solvency, the
strength of the model between leverage and size of the firms has been found to be very
15
Proceedings of 8th Asian Business Research Conference
1 - 2 April 2013, Bangkok, Thailand, ISBN: 978-1-922069-20-7
weak as revealed by R2. Since Sig. ‘F’ is less than 0.05 in all the years except in the
year 2007, we reject the null hypothesis, H4 and prove that leverage depended on size
significantly during the study period except in the year 2007. The beta co-efficients and
their ‘t’ values of CR also indicate that the impact of size on leverage was significant in
all the years except in the year 2007. It is also worth noting that the impact of size on
the leverage was very high as revealed by beta co-efficients. Thus we conclude that,
though the strength of the model is lesser, the capital structure design of Indian firms
depended significantly on size.
Table 7: Summary details of regression model 5 between leverage and tax
proportion
Sig.
‘β’ and
Year
α
R
R2 Adj. R2 Std.
Predictor
F
(‘t’ value)
Error
2007
1.03
0.22
0.04
0.03
0.99
0.06
TP
2008
0.89
0.42
0.17
0.16
0.49
0.00
TP
2009
0.79
0.39
0.15
0.14
0.48
0.00
TP
2010
0.90
0.37
0.14
0.13
0.50
0.00
TP
2011
0.93
0.45
0.21
0.19
0.46
0.00
TP
-2.06
(-1.91)
-2.18*
(-3.89)
-1.84*
(-3.56)
-2.24*
(-3.44)
-2.55*
(-4.35)
* Significant at 5% level
Table 7 contains the details pertaining to the relationship between leverage and tax
proportion (TP) of Indian companies. The values of R2 reveal that the model in all the
years has been very weak as the explained portion of the relation was very low.
However as the Sig. ‘F’ is less than 0.05 in all the years except in the year 2007 we
reject the null hypothesis, H5 and prove that leverage decision of Indian firms depended
on tax proportion in all the years except in the year 2007. This is also confirmed by the
beta co-efficients of tax proportion being significant throughout the study period except
in the year 2007. This finding is exactly in line with the findings pertaining to the impact
of solvency and size. We also notice that the co-efficients of TP throughout the study
period have been found to be reasonably higher than those of solvency and size factors
indicating greater influence of tax factor on leverage when compared to that of solvency
and size.
16
Proceedings of 8th Asian Business Research Conference
1 - 2 April 2013, Bangkok, Thailand, ISBN: 978-1-922069-20-7
Table 8: Summary details of regression model 6 between leverage and
growth
Adj.
Std.
Sig.
‘β’ and
Year
α
R
R2
Predictor
(‘t’ value)
R2
Error
F
1.02*
2007
-70 0.27 0.07 0.06
0.97 0.01
Growth
(2.45)
0.13
2008 0.31 0.09 0.00 -0.00 0.54 0.44
Growth
(0.77)
0.34
2009 0.03 0.13 0.01 0.00
0.51 0.26
Growth
(1.11)
-0.18
2010 0.70 0.08 0.00 -0.00 0.54 0.45
Growth
(-0.75)
-0.27
2011 0.76 0.11 0.01 -0.00 0.51 0.34
Growth
(-0.94)
Details pertaining to the relationship between the leverage and the growth have been
summarized in table 8. The strength of the model between the leverage and growth is
very weak throughout the study period as revealed by R2. The change in the leverage
could not be explained properly by the growth factor. The standard error term is also
high in all the years indicating that the model was unreliable in terms of studying the
extent of change in leverage due to change in growth. However, Sig. ‘F’ indicates that
the impact of growth on leverage was significant in the year 2007 only. Based on these
observations, we reject null hypothesis, H6 and prove that the leverage depended on
growth in the years 2007 only but not in the rest of the years. While this being worth
noting observation, the coefficients and their ‘t’ values indicate that the percentage of
impact exercised by the growth on leverage has been very low. Thus, we conclude that
the capital structure design of Indian firms did not depend much on growth factor.
Table 9: ANOVA results pertaining to leverage position of Indian
firms during the years 2007 through 2011.
Sum of
Mean
Squares
df
Square
F
Sig.
Between
1.844
4
.461
1.070
.371
Groups
Within
155.125
360
.431
Groups
Total
156.969
364
Table 9 captures the details pertaining to ANOVA of leverage position of Indian firms
during the years 2007 through 2011. Since Sig. ‘F’ is greater than 0.05, we accept the
null hypothesis, H7 and conclude that the leverage position of Indian firms did not vary
significantly over the years 2007 through 2011. This in turn implies that the time period
did not have any impact on the leverage position of Indian firms.
17
Proceedings of 8th Asian Business Research Conference
1 - 2 April 2013, Bangkok, Thailand, ISBN: 978-1-922069-20-7
Table 10: Descriptive statistics (mean and S.D) pertaining to
criterion variable (DER) and predictor variables (ROCE,
RONW, CR, NPM, SIZE, TP and Growth)
Variable
2007
2008
2009 2010
DER Mean 0.64
0.48
0.47
0.48
S.D
1.01
0.54
0.52
0.55
Mean 27.20 27.90 24.90 25.00
ROCE
S.D
15.30 23.40 21.00 21.00
Mean 25.86 24.84 21.80 21.97
RONW
S.D
12.03 18.61 17.25 17.05
Mean 1.53
1.63
1.76
1.71
CR
S.D
0.81
1.04
1.70
1.04
Mean 29.72 29.65 30.33 28.27
NPM
S.D 116.17 104.46 120.06 98.46
Mean 3.63
3.75
3.85
3.92
SIZE
S.D
0.53
0.55
0.55
0.54
Mean 19.00 19.00 18.00 19.00
TP
S.D
11.00 10.00 11.00 09.00
Mean 31.00 33.40 27.00 20.00
Growth
S.D
27.60 38.30 19.80 26.10
2011
0.44
0.52
22.70
20.00
18.00
25.54
1.72
1.10
29.20
117.19
3.99
0.53
20.00
09.00
18.30
20.60
Table 10 contains the mean and the standard deviation values of both criterion and
predictor variables in every individual year. On an average basis, the leverage position
of Indian firms did not vary much rather remained almost same in a range of 0.436
times being the lowest in the year 2011 and 0.639 times being the highest in the year
2007. The mean values of predictor variables like CR, NPM, Size and TP did not vary
much but remained almost same in all the years. However, other predictor variables like
ROCE, RONW and Growth decreased slightly in the years 2010 and 2011. The
standard deviation of DER, ROCE, CR, NPM, Size and TP remained almost same in all
the years and that of RONW and Growth factors varied slightly in the later years of the
study period. Of the standard deviations of all variables, that of NPM was exceptionally
high in all the years indicating higher volatility in reported NPM of Indian firms.
VI.
Conclusions:
In this paper we demonstrated as to how Indian firms went about in designing their
capital structure positions. While there are a host of studies available on the topic, we
came across, through literature survey, that the research on this topic has been focused
primarily on the U S and European firms providing a scope for research on Indian firms.
Also, the topic of capital structuring has been at a forefront of corporate finance
manager in the light of changing economic environment. Using multiple regression
models, we tried to demonstrate if there were any specific factors that influenced the
capital structure decision of Indian firms during the years 2007 through 2011. The
analysis revealed that the capital structure decisions of Indian firms depended largely
18
Proceedings of 8th Asian Business Research Conference
1 - 2 April 2013, Bangkok, Thailand, ISBN: 978-1-922069-20-7
on profitability in general and ROCE and RONW in specific in most of the years. A
deeper insight into statistical results indicates that the impact of various factors on
leverage position of Indian firms varied across the years though it was not much
significant. To be specific with respect to testing of hypotheses, the findings of the study
reveal that:
 H1 can’t be accepted and therefore it is concluded that the leverage position of
Indian firms depended on profitability, solvency, tax factor and growth
opportunities together.
 H2 can’t be accepted and therefore it is concluded that increasing profitability
influenced the leverage position of Indian firms.
 H3 can’t be accepted and therefore is concluded that leverage position of Indian
firms depended on their short-term solvency position.
 H4 can’t be accepted and therefore it is concluded that capital structure design of
Indian firms depended on their size.
 H5 can’t be accepted and therefore it is concluded that tax factor had an impact
on the leverage position of Indian firms.
 H6 can’t be rejected (except in the year 2007) and therefore it is concluded that
there was no link between the way Indian firms designed their capital structure
and their growth opportunities.
 H7 can’t be rejected and therefore it is understood that the time factor did not
have any significant impact on the changing leverage position of the firms during
the study period.
To sum up, the study revealed that capital structure design in Indian context is still a
matter of concern as it depends on a host of factors specific to the firm and specific to
the market. Despite certain limitations that we faced, we strongly feel that this study
bought out significant observations which provide meaningful implications to the
manager, lender and any stakeholder concerned with respect to financing pattern of
Indian firms. We also feel that the study adds up significantly to the body of knowledge
on capital structure practices as it has provided empirical evidence as to how Indian
firms design their capital structure position. While there would have been scope for
further improvement of the paper, we strongly feel and recommend that there is a need
to extend the research on this topic and therefore we welcome any researcher to
furthering the research on this topic in any possible and value adding manner.
References:
Bertomeu and Anne etal (2011), Capital Structure, Cost of Capital, and Voluntary
Disclosures, Accounting Review, Apr2011, Vol. 86 Issue 3, Pp.857-886.
Bolton and Brian etal (2011), Manager Characteristics and Capital Structure: Theory
and Evidence, Journal of Financial & Quantitative Analysis, Dec2011, Vol. 46
Issue 6, p1581-1627, 47p.
Brav (2009), Access to Capital, Capital Structure, and the Funding of the Firm, Journal
of Finance, Feb2009, Vol. 64 Issue 1, p263-308, 46p.
19
Proceedings of 8th Asian Business Research Conference
1 - 2 April 2013, Bangkok, Thailand, ISBN: 978-1-922069-20-7
Chang and Xiaoyun (2010), Informational Efficiency and Liquidity Premium as the
Determinants of Capital Structure, Journal of Financial & Quantitative Analysis,
Apr2010, Vol. 45 Issue 2, p401-440, 40p.
Erol (2011), Triangle Relationship among Firm Size, Capital Structure Choice and
Financial Performance, Journal of Management Research (09725814), Aug2011,
Vol. 11 Issue 2, p87-98.
Fan, Joseph P. H. etal (2012), Twite, Garry, An International Comparison of Capital
Structure and Debt Maturity Choices, Journal of Financial & Quantitative
Analysis, Mar2012, Vol. 47 Issue 1, p23-56, 34p.
Franco Modigliani and Merton H Miller, The cost of Capital, Corporation Finance and
Theory of Investment, American review, Vol. 48, June 1948.
Gill and Biger etal, (2011), The Effect of Capital Structure on Profitability: Evidence from
the United States, International Journal of Management, Dec2011, Vol. 28 Issue
4, p3-15, 13p.
Ilya A. Srebulaev (2007), Do Tests of Capital Structure theory Mean What they say?,
Journal of Finance, Vol. LXII, No. 4, August 2008, Pp. 1747-1787.
Jagdish (2011), The Impact of Financial Risk on Capital Structure Decisions in Selected
Indian Industries: A Descriptive Analysis, Advances in Management, Nov2011,
Vol. 4 Issue 11, p24-30, 7p.
Jianjun Miao (2005), Optimal capital Structure and Industry Dynamics, Journal of
Finance, Vol. LX, No. 6, December 2005, Pp. 2621-2659.
John R. Graham (2000), How Big Are the Tax Benefits of Debt, Journal of Finance, Vol.
LV, No. 5, October 2000, Pp. 1901-1941.
Laurence Booth and Varouj Aivazian et al (2001), Capital structures in Developing
Countries, Journal of Finance, Vol. LVI, No. 1, February 2001, Pp. 87-130.
Matsa (2010), Capital Structure as a Strategic Variable: Evidence from Collective
Bargaining, Journal of Finance, Jun2010, Vol. 65 Issue 3, p1197-1232, 36p.
Mukherjee and Mahakud et al, (2012), Are Trade-off and Pecking Order Theories of
Capital Structure Mutually Exclusive? Journal of Management Research
(09725814), Apr2012, Vol. 12 Issue 1, p41-55, 15p.
Murray Carlson and Ali lazrak (2010), Leverage Choice and Credit Spreads when
Managers Risk Shift, Journal of Finance, vol. LXV, No. 6, December 2010,
Pp.2323-2362.
20
Proceedings of 8th Asian Business Research Conference
1 - 2 April 2013, Bangkok, Thailand, ISBN: 978-1-922069-20-7
Raghuram G. Rajan and Luigi Zingales (1995), What Do We know about Capital
Structure? Some evidence from international Data, Journal of Finance, Vol. L,
No. 5, December 1995, Pp. 1421-1460.
Soku Byoun (2008), How and When Do Firms Adjust Their Capital Structures toward
Target, Journal of Finance, Vol. LXIII, No.6, December 2008, Pp. 3069-3096.
Xin Chang and Sudipto Dasgupta (2009), Target Behavior and Financing: How
Conclusive Is the Evidence, Journal of Finance, Vol. LXIV, No.4, august 2009,
Pp.1767-1796.
21