Determinants of capital structure evidence from listed companies on Hochiminh stock exchange.pdf

MINISTRY OF EDUCATION AND TRAINING
UNIVERSITY OF ECONOMICS HOCHIMINH CITY
----------o0o---------
VĂNG NGUYỄN PHƯƠNG THẢO
DETERMINANTS OF CAPITAL STRUCTURE
EVIDENCE FROM LISTED COMPANIES ON
HOCHIMINH STOCK EXCHANGE
MASTER THESIS
Ho Chi Minh City - 2011

MINISTRY OF EDUCATION AND TRAINING
UNIVERSITY OF ECONOMICS HOCHIMINH CITY
----------o0o---------
VĂNG NGUYỄN PHƯƠNG THẢO
DETERMINANTS OF CAPITAL STRUCTURE
EVIDENCE FROM LISTED COMPANIES ON
HOCHIMINH STOCK EXCHANGE
MAJOR: BANKING AND FINANCE
MAJOR CODE : 60.31.12
MASTER THESIS
INSTRUCTOR : ASSOC. PROF. – DR. PHẠM VĂN NĂNG
Ho Chi Minh City - 2011

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ACKNOWLEDGEMENT
I would like to express my deepest gratitude to my research Instructor,
Associate Professor – Doctor Pham Van Nang for his intensive support, valuable
suggestions, guidance and encouragement during the course of my study.
My sincere gratitude are also due to Doctor Vo Xuan Vinh for his valuable
feedback on the problems of the study.
I would like to express my thanks to all of my lecturers at Banking and
Finance Faculty, University of Economics Hochiminh City for their teaching and
guidance during my Master of Banking and Finance course.
Moreover, I would like to specially express my thanks to all of my
classmates, my friends for their support and encouragement.
My final and greatest thanks are sent to my family including my parents, my
brothers, my husband and my baby who are the greatest encouragement for me to
overcome all difficulties in my life.

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ABSTRACT
This thesis research the explanatory power of some of the literary theories
that have been propounded to explain variations in capital structures across firms.
In specific, this thesis investigates capital structure determinants of firms listed on
Hochiminh Stock Exchange based on a panel data set from 2006 to 2010
comprising 77 companies. Main characteristic of Vietnamese firms, including
firms listed on Hochiminh Stock Exchange, is short-term debt comprises a
considerable part of firms’ total debt. An analysis of determinants of leverage
based on total debt ratios may hide significant differences in the determinants of
long and short-term forms of debt. Therefore, this thesis studies determinants of
total debt ratios as well as determinants of short-term and long-term debt ratios.
The thesis consider the impact of those ratios on capital structure during period
2006-2010 to consider whether there was any different from before, in and after
the financial crisis. The thesis also tests the different choice of capital structure of
eleven groups of industries. And the last answer should be find out is the
difference of capital structure of firms with different size.
Keywords: Capital structure, Vietnam, HOSE.

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CONTENTS
Acknowledgement .........................................................................................i
Abstract ................................................................................................ ii
Contents .......................................................................................................iii
List of Tables ...............................................................................................vi
Abbreviations ....................................................................................................vii
CHAPTER 1: INTRODUCTION ............................................................................1
1.1. Introduction......................................................................................................1
1.2. Research objectives ..........................................................................................2
1.3. Research methodology......................................................................................2
1.4. The structure of the research.............................................................................3
CHAPTER 2: LITERATURE REVIEW..................................................................5
2.1. Introduction......................................................................................................5
2.2. Theoretical and Empirical Findings ..................................................................5
2.3. Potential determinants of capital structure.........................................................7
2.3.1. Profitability (PROF) ......................................................................................8
2.3.2. Firm size (SIZE)............................................................................................9
2.3.3. Assets tangibility (TANG)...........................................................................10
2.3.4. Growth opportunities (GRO) .......................................................................10
2.3.5. Non-debt tax shield (NDTS)........................................................................11

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2.3.6. Income variability (INVAR)........................................................................12
2.3.7. Time dummies............................................................................................. 12
2.3.8. Industry Dummies ....................................................................................... 13
2.4. Measures of capital structure/financial leverage.............................................. 13
2.4.1. Financial leverage of firms...........................................................................13
2.4.2. Decomposition of total debt into short-term and long-term debt ratios.........16
2.5. Conclusion......................................................................................................19
CHAPTER 3: RESEARCH METHODOLOGY .................................................... 21
3.1. Introduction....................................................................................................21
3.2. Data specifications.......................................................................................... 21
3.2.1 Research sample description.........................................................................21
3.2.2. Explanatory variables .................................................................................. 22
3.2.3 Dependent variables...................................................................................... 22
3.3. Empirical model specifications.......................................................................22
3.3.1 Model 1 ........................................................................................................23
3.3.2 Model 2 ........................................................................................................24
3.3.3 Model 3 ........................................................................................................24
CHAPTER 4: DATA ANALYSIS AND FINDINGS ............................................ 26

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4.1 Introduction.....................................................................................................26
4.2 Descriptive statistics........................................................................................ 26
4.3 Correlation matrix of explanatory variables ..................................................... 29
4.4 Results of Model 1........................................................................................... 30
4.5 Results of Model 2........................................................................................... 33
4.6 Results of Model 3........................................................................................... 35
4.7 Robustness tests............................................................................................... 38
CHAPTER 5: CONCLUSION .............................................................................. 42
5.1 Introduction.....................................................................................................42
5.2 Conclusion.......................................................................................................42
5.3 Limitations ......................................................................................................44
5.4 Recommendations ........................................................................................... 45
References ..................................................................................................47
Appendix A – Regression results of 3 models.............................................. 52
Appendix B – Research data set (2006 – 2010)............................................ 76

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LIST OF TABLES
Table 2.1. Short-term vs. long-term debt.............................................................17
Table 2.2. Short-term debt ratios and firm sizes ....................................................19
Table 2.3. Long-term debt ratios and firm sizes ..................................................19
Table 3.1. Potential determinants of capital structure, corresponding measures,
and expected effect on financial leverage ...........................................23
Table 4.1. Summary of the industry structure ..................................................... 27
Table 4.2. Descriptive statistics of the variables used in the study for the non-
financial firms listed on HOSE for the period 2006 to 2010 ............... 28
Table 4.3. Comparative means for different size of firms ...................................28
Table 4.4. Correlation coefficients among the explanatory variables .................. 29
Table 4.5. The reported results of Model 1 .....................................................31
Table 4.6. The reported results of Model 2 ........................................................ 34
Table 4.7. The reported results of Model 3 ........................................................ 36
Table 4.8. Results of Model 1 : Fixed Effects versus Random Effects ................ 39
Table 4.9. Results of Model 3 : Fixed Effects versus Random Effects ................ 41

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ABBREVIATIONS
HOSE Hochiminh Stock Exchange
PROF Profitability
SIZE Firm size
TANG Asset tangibility
GRO Growth opportunities
NDTS Non-debt tax shield
INVAR Income variability
TDTA Total debt to total assets
SDTA Short-term debt to total assets
LDTA Long-term debt to total assets

Determinants of Capital Structure
1
CHAPTER 1: INTRODUCTION
1.1. Introduction
One of the tough challenges that firms face is the choice of capital structure.
Capital structure decision is important because it affects the financial performance
of the firm. The capital structure of a firm is defined by Abor J. (2005, p.438-45) as
specific mix of debt and equity that a firm uses to finance its operations.
The modern theory of capital structure was firstly established by Modigliani and
Miller (1958). Thirty-seven years later, Rajan and Zingales (1995, p. 1421) stated:
“Theory has clearly made some progress on the subject. We now understand the
most important departures from the Modigliani and Miller assumptions that make
capital structure relevant to a firm’s value. However, very little is known about the
empirical relevance of the different theories”. Similarly, Harris and Raviv (1991, p.
299) in their survey of capital structure theories claimed: “The models surveyed
have identified a large number of potential determinants of capital structure. The
empirical work so far has not, however, sorted out which of these are important in
various contexts.” Thus, several conditional theories of capital structure exist (none
is universal), but very little is known about their empirical relevance. Moreover, the
existing empirical evidence is based mainly on data from developed countries (G7
countries). Findings based on data from developing countries have not appeared
until recently – for example Booth et al. (2001) or Huang and Song (2002). So far,
no study has been published based on data from Vietnam (especially the
Hochiminh Stock Exchange (HOSE)), at least to the extent of this author’s
knowledge. The main goal of this thesis is to fill this gap, exploring the case of the
listed firms in HOSE.
The remainder of this chapter provides general introduction about the
research objectives, research methodology and the structure of the research.

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1.2. Research objectives
The research is planned in the context of firms listed on Hochiminh Stock
exchange of Vietnam. The purpose of this thesis is to empirically examine the link
between a number of potential capital structure determinants and debt measures for
non-financial Vietnamese firms listed on HOSE for the period of 2006-2010.
The purpose of this research is looking for answers to the following questions:
Q1.: How is financial leverage (total debt ratio, long-term debt ratio and short-term
debt ratio) of listed firms in Hochiminh Stock Exchange impacted by determinants
of capital structure (profitability, size, firm tangibility (asset structure), growth
opportunities, non-debt tax shield, and income variability)? Are these impacts
shifted over years?
Q2.: What are the effects of industry dummies on those impacts?
Q3.: Are the determinants different in firms of different size (small, medium and
large size)?
1.3. Research methodology
The research uses a firm-level panel data set of 77 publicly traded non-financial
firms on Hochiminh Stock Exchange between 2006 and 2010.
The empirical steps to examine the above mentioned research objectives
proceed as follows :
 Descriptive statistics
 Correlation matrix
 Using random effects logistic regression model to test the determinants of
capital structure

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Stata software version 11 is used as an data analysis tool to implement this
research.
1.4. The structure of the research
The structure of the study consist five chapters:
Chapter 1: Introduction
This chapter presents introduction of the thesis, as well as research objectives and
research methodology.
Chapter 2: Literature Review
A summary of the literature review is provided, including the potential
determinants of capital structure as well as some variables to explain the reasons
for firms to choose debt measures.
Chapter 3: Research Methodology
Based on the research objectives, research methodology concerned in chapter 1,
and literature review presented in chapter 2, this chapter particularly presents the
data and empirical model specifications.
Chapter 4: Data Analysis and Findings
Chapter 4 presents the analysis of results from the study. We use descriptive
statistics to explore the features of explanatory variables and correlation matrix to
present the relationship between explanatory variables. Furthermore, we use
regression analysis to explore the impacts of debt measures on the determinants of
the capital structure of listed firms on Hochiminh Stock Exchange.
Chapter 5: Conclusions

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Chapter 5 presents main conclusions and the limitations of this thesis. From the
results of the previous chapters as well as those limitations, some recommendations
are suggested by the author.

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CHAPTER 2: LITERATURE REVIEW
2.1. Introduction
In this chapter, a summary of the literature review is provided, including the
potential determinants of capital structure as well as some variables to explain the
reasons for firms to choose debt measures. The purpose of this review is to provide
the background for the research hypotheses.
2.2. Theoretical and Empirical Findings
According to Myers (2001, p. 81), “there is no universal theory of the debt-equity
choice, and no reason to expect one”. However, there are several useful conditional
theories, each of which helps to understand the debt-to-equity structure that firms
choose. These theories can be divided into two groups – either they predict the
existence of the optimal debt-equity ratio for each firm (so-called static trade-off
models) or they declare that there is no well-defined target capital structure
(pecking-order hypothesis).
Static trade-off models understand the optimal capital structure as an optimal
solution of a trade-off, for example the trade-off between a tax shield and the costs
of financial distress in the case of trade-off theory. According to this theory the
optimal capital structure is achieved when the marginal present value of the tax
shield on additional debt is equal to the marginal present value of the costs of
financial distress on additional debt. The trade-off between the benefits of signaling
and the costs of financial distress in the case of signaling theory implies that a
company chooses debt ratio as a signal about its type. Therefore, in the case of a
good company, the debt must be large enough to act as an incentive compatible
signal, i.e., it does not pay off for a bad company to mimic it. In the case of agency
theory the trade-off between agency costs stipulates that the optimal capital
structure is achieved when agency costs are minimized. Finally, the trade-off

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between costs of financial distress and increase of efficiency in the case of free
cash-flow theory, which is designed mainly for firms with extra-high free cash-
flows, suggests that the high debt ratio disciplines managers to pay out cash instead
of investing it below the cost of capital or wasting it on organisational
inefficiencies.
On the other hand, the pecking-order theory suggests that there is no optimal capital
structure. Firms are supposed to prefer internal financing (retained earnings) to
external funds. When internal cash-flow is not sufficient to finance capital
expenditures, firms will borrow, rather than issue equity. Therefore there is no
well-defined optimal leverage, because there are two kinds of equity, internal and
external, one at the top of the pecking order and one at the bottom.
Existing empirical evidence is based mainly on data from developed countries. For
example Bradley et al. (1984), Kim and Sorensen (1986), Friend and Lang (1988),
Titman and Wessels (1988) and Chaplinsky and Niehaus (1993) focus on United
States companies; Kester (1986) compares United States and Japanese
manufacturing corporations; Rajan and Zingales (1995) examine firms from G7
countries; and Wald (1999) uses data for G7 countries except Canada and Italy.
Findings based on data from developing countries have appeared only in recent
years, for example Booth et al. (2001) or Huang and Song (2002).
To our knowledge, only several such studies have dealt with Vietnam. Of these,
San (2002) focused on a single industry (tourism) in a single locality (Thua Thien
Hue Province) whilst Nguyen and Ramachandran (2006) focused on small and
medium-sized enterprises (SMEs) only. By contrast, Vu (2003) analyzed
companies listed on the main stock exchange (Ho Chi Minh City, HCMC).
Although they are far less numerous than unlisted companies (most of the latter are
SMEs), listed companies account for a larger share of economic activity: The small
business sector produces only about 25% of GDP.

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This study represents an effort to update the analysis of Vu (2003), in that it
investigates the determinants of leverage among the companies listed on
Hochiminh Stock Exchange during the period 2006-2010.
2.3. Potential determinants of capital structure
In the light of these above mentioned theories, we will choose some variables to
explain the reasons for firms’ determinants of debt over equity finance. As Harris
and Raviv’s (1991) demonstrate in their review article, the motives and
circumstances that could determine capital structure choices seem nearly
uncountable. In this paper though, we will restrict ourselves to the most commonly
used explanatory variables.
Then, what are the determinants of capital structure? According to Harris and
Raviv (1991), the consensus is that “leverage increase with fixed assets, non-debt
tax shields, investment opportunities, and firm size, and decreases with volatility,
advertising expenditure, the probability of bankruptcy, profitability, and uniqueness
of the product.” Titman and Wessels (1988) state that asset structure, non-debt tax
shields, growth, uniqueness, industry classification, size, earnings volatility, and
profitability are factors that may affect leverage according to different theories of
capital structure. Still, other authors may provide another set of potential
determinants of capital structure. This clearly shows that even if there is a
consensus among researchers what factor may constitute a minimum set of
attributes, there is still plenty of room for arguing in favor of including other
determinants as well.
In this thesis, following determinants will be used:
 Profitability,
 Firm size,
 Assets tangibility,

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 Growth opportunities,
 Non-debt tax shield,
 Income variability,
 Time dummies,
 Industry dummies.
A short discussion of each of the determinants used in this thesis, their relationship
to capital structure theories, and how they can be measured will be presented
below.
2.3.1. Profitability (PROF)
The pecking order theory, based on works by Myers and Majluf (1984) suggests
that firms have a pecking-order in the choice of financing their activities. This
theory states that firms prefer internal funds rather than external funds. If external
finance is required, the first choice is to issue debt, then possibly with hybrid
securities such as convertible bonds, then eventually equity as a last resort (Brealey
and Myers, 1991). This behavior may be due to the costs of issuing new equity, as a
result of asymmetric information or transaction costs. There are conflicting
theoretical predictions on the effects of profitability on leverage (Rajan and
Zingales, 1995); while Myers and Majluf (1984) predict a negative relationship
according to the pecking order theory, Jensen (1986) predicts a positive relationship
if the market for corporate control is effective. However, if it is ineffective, Jensen
(1986) predicts a negative relationship between profitability and leverage. In this
paper, we expect that there is a negative correlation between profitability and
leverage, i.e. high profit firms should have a lower leverage. The hypothesis is
formulated to test profitability as: The leverage is negatively associated with the
profitability.

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Here, we use the ratio of earnings before interest and taxes (EBIT) to total assets as
a measure profitability.
EBIT
PROF =
Total asset
2.3.2. Firm size (SIZE)
The relationship between firm size and leverage is also unclear. If the relationship
is a proxy for probability of bankruptcy, then size may be an inverse proxy for the
probability of bankruptcy, since larger firms are more likely to be more diversified
and fail less often. Accordingly, larger firms may issue debt at lower costs than
smaller firms. In this case therefore, we can expect size to be positively related to
leverage. However, Fama and Jensen (1983) argue that there may be less
asymmetric information about large firms, since these firms tend to provide more
information to outside investors than smaller firms. This should therefore increase
their preference for equity relative to debt (Rajan and Zingales, 1995). In this study,
our expectation on the effect of size on leverage is ambiguous. The hypothesis is
formulated to test firm size as: The leverage is positively/negatively associated with
the firm size.
To proxy for the size of a company, the natural logarithm of sales is used in this
study (as it is in most studies of similar character). Another possibility is to proxy
the size of a company by the natural logarithm of total assets. The natural logarithm
of sales and the natural logarithm of total assets are highly correlated (0.68 in 2006,
0.63 in 2007, 0.65 in 2008, 0.70 in 2009 and 0.71 in 2010), therefore each of them
should be a sound proxy for company size. Here sales rather than total assets are
used to avoid the probability of spurious correlation.
SIZE = Log(sales)

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2.3.3. Assets tangibility (TANG)
It is assumed, from the theoretical point of view, that tangible assets can be used as
collateral. Therefore higher tangibility lowers the risk of a creditor and increases
the value of the assets in the case of bankruptcy. As Booth et al. (2001, p. 101)
state: “The more tangible the firm’s assets, the greater its ability to issue secured
debt and the less information revealed about future profits.” Thus a positive relation
between tangibility and leverage is predicted. Several empirical studies confirm this
suggestion, such as (Rajan – Zingales, 1995), (Friend – Lang, 1988) and (Titman –
Wessels, 1988) find. Therefore, the hypothesis is formulated to test assets
tangibility as: The leverage is positively associated with assets tangibility.
In order to estimate the econometric models below, we use the ratio of fixed assets
over total assets as a measure of tangible assets.
Fixed assets
TANG =
Total assets
2.3.4. Growth opportunities (GRO)
Theoretical studies generally suggest growth opportunities are negatively related
with leverage. On the one hand, as Jung, Kim and Stulz (1996) show, if
management pursues growth objectives, management and shareholder interests tend
to coincide for firms with strong investment opportunities. But for firms lacking
investment opportunities, debt serves to limit the agency costs of managerial
discretion as suggested by Jensen (1986) and Stulz (1990). The findings of Berger,
Ofek, and Yermack (1997) also confirm the disciplinary role of debt. On the other
hand, debt also has its own agency cost. Myers (1977) argues that high-growth
firms may hold more real options for future investment than low-growth firms. If
high-growth firms need extra equity financing to exercise such options in the
future, a firm with outstanding debt may forgo this opportunity because such an

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investment effectively transfers wealth from stockholders to debtholders. So firms
with high-growth opportunity may not issue debt in the first place and leverage is
expected to be negatively related with growth opportunities. Berens and Cuny
(1995) also argue that growth implies significant equity financing and low leverage.
And in this study, the hypothesis is formulated to test growth opportunities as: The
leverage is negatively associated with growth opportunities.
Empirical studies such as Booth et al. (2001), Kim and Sorensen (1986), Rajan and
Zingales (1995), Smith and Watts (1992), and Wald (1999) predominately support
theoretical prediction. The only exception is Kester (1986). There are different
proxies for growth opportunities. Wald (1999) uses a 5-year average of sales
growth. Titman and Wessels (1988) use capital investment scaled by total assets as
well as research and development scaled by sales to proxy growth opportunities.
Rajan and Zingales (1995) use Tobin’s Q (market-to-book ratio of total assets) and
Booth et al. (2001) use market-to-book ratio of equity to measure growth
opportunities. We argue that sales growth rate is the past growth experience, while
Tobin’s Q better proxies future growth opportunities; therefore, Tobin’s Q is
employed to measure growth opportunities in this study.
Equity market value + Total liabilities
GRO =
Total assets
2.3.5. Non-debt tax shield (NDTS)
According to Modigliani and Miller (1958), interest tax shields create strong
incentives for firms to increase leverage. But also the size of non-debt related
corporate tax shields like tax deductions for depreciation and investment tax credits
may affect leverage. Indeed, DeAngelo and Masulis (1980) argue that such non-
debt tax shields are substitutes for the tax benefits of debt financing. Therefore, the
tax advantage of leverage decreases when other tax deductions like depreciation
increase (Wanzenried, 2002). Hence, we expect that an increase in non-debt tax

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shields will affect leverage negatively. The hypothesis is formulated to test non-
debt tax shield as: The leverage is negatively associated with non-debt tax shield.
Titman and Wessels (1988) use the ratio of tax credits over total assets and the ratio
of depreciation over total assets as measures of non-debt tax shield. In this thesis,
we have only data on depreciation and therefore, the ratio of depreciation over total
assets will serve as a measure for non-debt tax shield.
Depreciation
NDTS =
Total assets
2.3.6. Income variability (INVAR)
Income variability is a measure of business risk. Since higher variability in earnings
indicates that the probability of bankruptcy increases, we can expect that firms with
higher income variability have lower leverage. The hypothesis is formulated to test
income variability as: The leverage is negatively associated with the income
variability.
We will use the ratio of the standard deviation of EBIT over total assets as a
measure of income variability.
Standard deviation of EBIT
INVAR =
Total assets
2.3.7. Time dummies
In addition to the determinants above, a full set of time-dummies (one for each
year, except for the first year 2006, which serves as the base year upon which the
estimated dummy coefficients should be interpreted) will also be included in some
regression models. By including time dummies, we may be able to investigate
whether leverage shifts over time, after controlling for the other observable

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determinants; i.e. the unobserved time-specific effects will be represented by the set
of time dummies (Lööf, 2003).
Furthermore, Bevan and Danbolt (2000) extend the use of time-dummies in panel
data regression by interacting time dummies with the constant term and all the
explanatory variables. They argue that two factors can be analyzed simultaneously;
“interactive intercept dummies enable us to examine the general of time-variant but
firm-variant factors; interactive independent variables dummies allow us to identify
how time-variant general factors influence the relation between our determining
factors and gearing (leverage)”. For this study though, we will restrict the use of
time-dummies to be stand-alone factors, and not used in interaction terms.
2.3.8. Industry Dummies
Some empirical studies identify a statistically significant relationship between
industry classification and leverage, such as (Bradley et al., 1984), (Long – Malitz,
1985), and (Kester, 1986). As Harris and Raviv (1991, p. 333) claim, based on a
survey of empirical studies: “Drugs, Instruments, Electronics, and Food have
consistently low leverage while Paper, Textile Mill Products, Steel, Airlines, and
Cement have consistently large leverage.”
To estimate the effect of industry classification on leverage, firms in our sample are
divided into eleven groups: Basic Materials (BM), Construction & Materials (CM),
Consumer Discretionary (CD), Consumer Staples (CS), Industrials (IN),
Information Technology (IT), Multi-scope Business and Group (MS), Oil/Gas
(OG), Real Estate (RE), Transportation (TR), Utilities (UT).
2.4. Measures of capital structure/financial leverage
2.4.1. Financial leverage of firms

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Firstly, we would like to briefly repeat the term capital structure and its related
terms (financial structure, financial leverage or gearing). The term capital structure
refers to the mix of different types of securities (long-term debt, common stock,
preferred stock) issued by a firm to finance its assets. A firm is said to be unlevered
as long as it has no debt, on the contrary, one with debt in its capital structure is
said to be leveraged. There exist two major leverage terms: Operational leverage
and financial leverage. While operational leverage is related to a company’s fixed
operating costs, financial leverage is related to fixed debt costs. In other words,
operating leverage increases the business (or the operating) risk, while financial
leverage increases the financial risk. Then, total leverage is given by a firm’s use of
both fixed operating costs and debt costs, implying that a firm’s total risk equals
business risk plus financial risk. In this study of determinants of capital structure,
with leverage, we mean financial leverage, or its synonym gearing.
The firms’ capital structure, or financial leverage, constitutes this study’s dependent
variable. There were a lot of articles written about determinants of capital structure
after the paper on 1958 of Modigliani and Miller. And the fact is that there are
different measures of capital structure exist, and each capital structure measure
itself can be measured in different ways. Roughly, two major categories of leverage
measures exist: Those that are based on market value of equity, and those that are
based on booked value of equity (Lööf, 2003). For instance, Titman and Wessels
(1988) discuss six measures of financial leverage in their study of capital structure
choice: Long-term, short-term, and convertible debt divided by market and book
values of equity respectively. Due to data limitations, almost empirical studies used
only leverage measures in terms of book values rather than market values of equity.
Indeed, for this study, market data is not available enough, implying that we have
to measure leverage in terms of booked values only.
Then, how serious is the problem of lacking market data in an empirical study of
determinants of capital structure choice? Unfortunately, an exhaustive discussion of

15
this matter is outside the scope of this paper. Though, some hints can be given
based on the fact that when both booked and market values are available, they are
both used simultaneously. The reason is that the information signaled in book value
and market value is informative in different aspects (Lööf, 2003). On the contrary,
Titman and Wessels (1988) refers to an earlier study by Bowman (1980), which
proved that the cross-sectional correlation between the book value and market
value of debt is very large. Furthermore, Brealey and Myers (2003) argue that it
should not matter much if only book values are used, since the market value
includes the value of intangible assets generated by for instance research and
development, staff education, advertising, and so on. These kinds of assets cannot
be sold easily, and in fact, if the company goes down, the value of intangible assets
may disappear altogether. Hence, misspecification due to using book value
measures may be pretty small, or even totally unessential.
Irrespective of market or book value, we still face the problem of choosing an
appropriate leverage measure as the dependent variable. Indeed, in an important
paper by Rajan and Zingales (1995), they argue that the choice of the most relevant
measure depends on the objective of the analysis. Though, they conclude “the
effects of past financing decisions is probably best represented by the ratio of total
debt over capital (defined as total debt plus equity)”.
To complete the discussion of different leverage measures, we may consider the
following statement by Harris and Raviv (1991, p. 331) when we compare different
empirical studies: “The interpretation of the results must be tempered by an
awareness of the difficulties involved in measuring both leverage and the
explanatory variables of interest. In measuring leverage, one can include or exclude
accounts payable, accounts receivable, cash, and other short-term debt. Some
studies measure leverage as a ratio of book value of debt to book value of equity,
others as book value of debt to market value of equity, still others as debt to market

16
value of equity plus book value of debt. […] In addition to measurement problems,
there are the usual problems with interpreting statistical results.”
With those words of caution in mind, we now continue with choosing leverage
measures for this study. Indeed, for the objective of this study, following leverage
measures will be analyzed in a litter bit more detail below; the ratio of total debt
over total assets.
2.4.2. Decomposition of total debt into short-term and long-term debt ratios
It is of interest to examine the sources of debt in more detail. As specification of
Vietnamese firms, the data set used in this study only allows for a decomposition of
total liabilities into two items: Short-term debt, long-term debt. So total liabilities in
this case equal total debt. It would though have been of great interest to have
information about the magnitudes of the components that make up short-term and
long-term debt respectively, for instance the size of companies’ trade credit (that is
a component in short-term debt). Indeed, based on a cross-sectional analysis of
leverage in UK companies (1991 figures), Bevan and Danbolt (2000) find
significant differences in the determinants of short-term and long-term forms of
debt. In particular, given that short-term debts like trade credit and equivalent, on
average accounts for more than 62% of total debt of the UK companies, the results
are particularly sensitive to whether such debt is included in the leverage measures.
Hence in line with their findings, Bevan and Danbolt argue that analysis of
corporate structure is incomplete without a detailed examination of corporate debt.
In another study of capital structure of small and medium sized enterprises (SMEs),
Michaelas et. al. (1999) find that most of the determinants of capital structure (e.g.
size, profitability, growth, and more) seem to be relevant for both short-term and
long-term debt ratios. They also find that time and industry dummies influence the
maturity structure of debt raised by SMEs. By analyzing the coefficients of the time
dummies over the years studies (1988 to 1995) in relation to changes in real GDP,

17
Michaelas et. al. found that short-term debt ratios in SMEs appear to be negatively
correlated with changes in economic growth, while long-term debt ratios exhibit a
positive relationship with changes in economic growth.
In attempt to analyze determinants of corporate debt with respect to both short-term
and long-term debt ratios, we create two such leverage measures. The resulting
leverage figures are presented in table 2.1. below. Interestingly, we can see that the
short-term debt ratio is on average four times as large as the long-term debt ratio.
Notice also the relatively sharp fall in mean and median values for short-term
leverage between 2006 and 2008. On the other hand, the figures for long-term debt
ratio do not show any clear downward trend.
Table 2.1. Short-term vs. long-term debt. For convenience, the figures for total
debt to total assets are shown here too.
Year
TDTA LDTA SDTA
Mean Median Mean Median Mean Median
2006 47.32% 47.94% 8.87% 4.41% 38.45% 37.81%
2007 41.28% 43.46% 8.49% 3.84% 32.79% 30.76%
2008 39.87% 40.47% 9.06% 3.05% 30.81% 28.47%
2009 42.98% 42.06% 9.63% 3.58% 33.35% 29.52%
2010 43.95% 45.29% 8.31% 3.27% 35.65% 31.58%
Average 43.08% 43.84% 8.87% 3.63% 34.21% 31.63%
Inspired by the result of this decomposition of total debt, in combination with the
contradictory findings of the cross-sectional analysis by Bevan and Danbolt (2000)
and the panel data analysis Michaelas et. al. (1999), we will include the two new
measures of leverage in the econometric analysis below.
Without having data on size of trade credit at hand, we may just speculate whether
trade credit makes up a large portion of short-term debt, and why it may be so.
Now, suppose that trade credit and equivalent components constitutes a large share
of short-term debt. Following the arguments in Bevan and Danbolt (2000), we may

18
then suggest that this kind of reliance on trade credit reflects a rational corporate
debt policy, given that other form of borrowing result in higher costs.
Now we know that short-term debt constitutes a large portion of total debt, it may
be interesting to see if short-term and long-term debt rations vary across firm sizes.
Again as usual in corporate finance, there exist several different definitions of
specific factor: Number of persons employed, size of total assets, size of turnover,
and more. Furthermore, size can be measured as a continuous variable or as a
categorical variable. In order to present a rough picture of leverage figures across
different firms sizes, we choose to categorize firm sizes according to following
scheme: Firms with total assets less than 500 billion VND are defined as small
firms; medium sized firms are companies with total assets from 501 to 5,000 billion
VND; and finally large a firms are characterized as having total assets more than
5,000 billion VND (refer to definition of R.Dhawan (1999) for size of total assets
of US companies). The resulting figures are presented in table 2.2. below. What is
most strikingly is the decrease of short-term debt for small and large firms. There is
a clear downward trend from 2006 to 2008 for small firms (then increase, but just a
little), and until 2010 for large firms. On the other hand, debt ratios of medium size
firms appear to lightly decrease in 2007, then develop until 2010.

19
Table 2.2. Short-term debt ratios and firm sizes.
Short-term debt ratios and firm sizes
Year
Small firms Medium firms Large firms
2006 40.80% 44.19% 32.94% 31.43%
2007 35.20% 37.18% 29.11% 22.79% 36.86% 36.86%
2008 30.67% 29.38% 30.90% 23.26% 31.31% 30.69%
2009 33.98% 29.52% 33.65% 32.08% 25.03% 20.31%
2010 33.84% 29.31% 38.21% 34.99% 24.98% 22.54%
Average 34.90% 33.92% 32.96% 28.91% 29.55% 27.60%
Contrary to the findings above, table 2.3. below reveals that long-term debt ratios
have declined across medium firms, both in terms of means and medians. And this
ratio stay stable with small firms, while increase with large firms.
Table 2.3. Long-term debt ratios and firm sizes.
Long-term debt ratios and firm sizes
Year
2006 4.96% 3.63% 18.06% 18.49%
2007 5.31% 1.28% 13.43% 7.48% 1.91% 1.91%
2008 6.13% 2.89% 12.48% 8.63% 8.60% 4.90%
2009 6.47% 2.47% 11.36% 6.86% 20.86% 17.46%
2010 5.30% 1.22% 9.13% 4.01% 15.86% 16.36%
Average 5.63% 2.30% 12.89% 9.09% 11.81% 10.16%
In brief, we now state the three measures that will be used in the econometric
analysis below: The ratio of total debt over total assets (TDTA), short-term debt to
total assets (SDTA), and long-term debt to total assets (LDTA).
2.5. Conclusion
Firms’ capital structure in emerging markets is often different in its nature,
characteristics, and efficiency, from that of developed markets. The above review
of the literature reveals several different explanations for the factors which effect

20
on the derterminants of capital structure as well as the measures of capital
structure/financial leverage. These factors vary substantially from country to
country. That is one of the reasons why we implement this study for one of
theVietnam stock markets, Hochiminh Stock Exchange, because we do not know
yet which theory applies to Vietnam firms and which empirical studies is consistent
with Vietnam stock market.

21
CHAPTER 3: RESEARCH METHODOLOGY
3.1. Introduction
Based on the research objectives, research methodology concerned in chapter 1,
and literature review in chapter 2, this chapter provides the details of research
data collection and applied research hypothesis and methodology to test research
objectives.
3.2. Data specifications
This thesis used the accounting and market data of 77 listed companies in the
Hochiminh Stock Exchange (HOSE) in the period 2006-2010. The data set
contained detailed information of each firm. The items of interest were: Balance
sheets, income statements and depreciation. By law, the full financial statements
were available from firms. This data set was collected from website cafef.vn and
www.vinabull.com.
3.2.1 Research sample description
For the study, the sample of 77 firms of Hochiminh Stock Exchange has been taken
into the consideration. We select 77 firms based on the following criteria:
a. Listed on Hochiminh Stock Exchange during the period 2006-2010.
b. Be non-financial firms. In other words, funds, securities companies, banks and
other financial firms are excluded from the sample. Following previous research
(Fama and French (2001), DeAngelo et al. (2006)), we exclude financial firms
because these firms operate in a highly regulated environment.
c. Must have non-missing values on the financial database.

22
d. Trading sections of the firms are not be interrupted because of their violation of
the regulations of Hochiminh Stock Exchange.
The data has been collected from the web of Hochiminh Stock Exchange for the
period 2006 to 2010, specified as follows :
 The explanatory variables of the study including PROF, SIZE, TANG,
GRO, NDTS, INVAR as well as the dependent variables including TDTA, SDTA,
LDTA, have been calculated from the Audited Annual Financial Statements of 77
firms for the period of 2006 to 2010.
 The classification of industries has been adjusted with reference of the
“Market Review” report of Vietnam International Securities Company
(www.vise.com.vn)
 Panel structure is by year.
3.2.2. Explanatory variables
There are eight explanatory variables which introductions and formulas have been
stated in previous chapter. Those are profitability (PROF), firm size (SIZE), assets
tangibility (TANG), grow opportunities (GRO), non-debt tax shiels (NDTS),
income variability (INVAR), time dummies and industry dummies.
3.2.3 Dependent variables
As mentions in previous chapter, there are three dependent variables in this
research: Total debt to total assets (TDTA), short-term debt to total assets (SDTA)
and long-term debt to total assets (LDTA).
3.3. Empirical model specifications
There are eight hypotheses which will be the same for our three models, and all are
stated in table 3.1. below.

23
Table 3.1. Potential determinants of capital structure, corresponding measures, and
expected effect on financial leverage
Hypotheses Determinant Measure (proxy) Expected effect on leverage
H1 Profitability EBIT / Total assets Negative
H2 Size Log(sales) Ambiguous
H3 Tangibility Fixed assets / Total assets Positive
H4 Growth Market-to-book ratio of Negative
total assets
H5 Non-debt tax shield Depreciation / Total assets Negative
H6 Income variability Standard deviation of EBIT / Negative
Total assets
H7 Time dummies Influent
H8 Industry dummies Influent
Since the dataset is a cross-sectional time-series dataset, random effects generalized
least square (GLS) regression model is used to test the three below models. Then
we run Robustness tests to ensure the validity of the results.
3.3.1 Model 1
This model tests the impacts of the financial leverages on determinants of capital
structure of listed firms in Hochiminh Stock Exchange over period 2006-2010.
The functional form of our model is as follows:
LEVit = α + β1PROFit + β2SIZEit + β3TANGit + β4GROit + β5NDTSit + β6INVARit +
γnTni + εit
Where :
 LEV: TDTA, SDTA and LDTA of firm i at year t
 i = 1… 77 cross-sectional observation unit in the sample
 t = 1… 5 time period (2006 – 2010)
 n = 2 … 5 time period (except 2006 as base year)

24
 Tni : Time dummies
 εit is residual error for firm i in year t
3.3.2 Model 2
This model tests the effects of industry dummies on the impacts of the financial
leverages on determinants of capital structure of listed firms in Hochiminh Stock
Exchange.
LEVit = α + β1PROFit + β2SIZEit + β3TANGit + β4GROit + β5NDTSit + β6INVARit +
υnXni + ξit
Where :
 n = 2 … 11 industries (except MS as base coefficient)
 Xni: Industry dummies
 ξit is residual error for firm i in year t
3.3.3 Model 3
This model tests the difference of determinants of capital structure of listed firms in
Hochiminh Stock Exchange with different size (small, medium, large).
LEVit = α + β1PROFit + β2TANGit + β3GROit + β4NDTSit + β5INVARit + μit
Where :
 μit is residual error for firm i in year t

25
We estimate the above equation for TDTA, SDTA and LDTA. We repeat each
estimation with different definitions of size: Small, medium and large.

26
CHAPTER 4: DATA ANALYSIS AND FINDINGS
4.1 Introduction
This chapter presents the analysis of results from the study. We use descriptive
statistics to explore the features of explanatory variables and correlation matrix to
present the relationship between explanatory variables. Furthermore, we use
regression analysis to explore the determinants of capital structure of the firms
listed on HOSE.
4.2 Descriptive statistics
Among 77 selected companies in the study, there are 12 companies in Basis
Materials sector, 11 companies in Constructions and Materials sector, 7 companies
in Consumer Discretionary sector, 18 companies in Consumer Staples sector, 8
companies in Industrials sector, 1 company in Information Technology sector, 1
company in Multi-Scope Business and Group sector, 5 companies in Oil/Gas
sector, 3 companies in Real Estate sector, 7 companies in Transportation sector,
and 4 companies in Utilities sector. The percentage of Consumer Staples sector is
23.38 percent, highest in the industry structure. The percentage of Information
Technology sector and Multi-Scope Business and Group sector is 1.30 percent for
each, lowest in the industry structure. Table 4.1. will show more.

27
Table 4.1. Summary of the industry structure
Industry Frequency Percent Cummulation
Basis materials (BM) 12 15.58% 15.58%
Constructions and Materials (CM) 11 14.29% 29.87%
Consumer Discretionary (CD) 7 9.09% 38.96%
Consumer staples (CS) 18 23.38% 62.34%
Industrials (IN) 8 10.39% 72.73%
Information technology (IT) 1 1.30% 74.03%
Multi-scope business and group (MS) 1 1.30% 75.32%
Oil/Gas (OG) 5 6.49% 81.82%
Real estate (RE) 3 3.90% 85.71%
Transportation (TR) 7 9.09% 94.81%
Utilities (UT) 4 5.19% 100.00%
77 100.00%
The descriptive statistics of explanatory variables from the period of 2006 to 2010
contain a sample of 77 non-financial listed firms of Hochiminh Stock Exchange
and show the average indicators of variables computed from the financial
statements. From the descriptive statistics we find that the mean value of total debt
ratio (TDTA) is 43.08% which shows the firms in our sample use the debts to
finance their assets. The mean of the asset tangibility is 29.67% which shows that
fixed assets is not much invested in the asset structure of firms. The mean of
profitability is 29.31%, and minimum is -41.38% (get loss). The mean of the firm
size is 11.74 which shows that the listed firms of HOSE do not invest more in their
asset. The average growth rate of listed non-financial firms is 172.66% (see more in
Table 4.2. below).

28
Table 4.2. Descriptive statistics of the variables used in the study for the non-
financial firms listed on HOSE for the period 2006 to 2010
Determinants Mean Median Minimum Maximum
Standard
Deviation
PROF 0.2931 0.2507 (0.4138) 2.5280 0.2244
SIZE 11.7388 11.7519 10.6068 13.3304 0.4910
TANG 0.2967 0.2607 0.0132 0.9382 0.1927
GRO 1.7266 1.3621 0.4176 14.0072 1.2899
NDTS 0.1854 0.1258 0.0003 0.8196 0.1688
INVAR 0.1269 0.0875 0.0028 1.3072 0.1350
TDTA 0.4308 0.4466 0.0309 0.9894 0.1960
LDTA 0.0887 0.0355 0.0000 0.6493 0.1185
SDTA 0.3421 0.3239 0.0260 0.8176 0.1851
Table 4.3. below will show us the comparison of mean level of firms with different
size. From the comparison, we find that small firms get the highest mean in
profitability, income variability and short-term debt ratio. Medium firms get the
highest mean in tangibility, non-debt tax shield and total debt ratio. Large firms get
the highest mean in size, of course, and they also get highest grow opportunities.
Long-term debt ratio of large firms is also biggest.
Table 4.3. Comparative means for different size of firms
All Small Medium Large
PROF 0.2931 0.3028 0.2864 0.2448
SIZE 11.7388 11.4632 11.9645 12.7675
TANG 0.2967 0.2790 0.3179 0.2911
GRO 1.7266 1.7153 1.6984 2.1665
NDTS 0.1854 0.1890 0.1902 0.0891
INVAR 0.1269 0.2077 0.0439 0.0055
TDTA 0.4308 0.4101 0.4564 0.4161
LDTA 0.0887 0.0558 0.1229 0.1355
SDTA 0.3421 0.3543 0.3335 0.2806

29
4.3 Correlation matrix of explanatory variables
Table 4.4. presents correlations between the dependent and independent variables.
Asset tangibility is positive correlated with total debt ratio in the same to what we
expected. According to the theory, since fixed assets can be used as collateral, debt
level should increase with higher fixed assets. We find this positive relation when
we look at the correlations between asset tangibility and total debt as well as long-
term debt ratio. But asset tangibility is negatively correlated with short-term debt
ratio. Non-debt tax shield is positive correlated with total debt ratio, contrast to
what we expect. Profitability is inversely related to total debt ratio. In accordance
with Pecking Order theory, profitable firms prefer to finance internally. Size is
positively related with total debt ratio. As firm gets larger, their debt increases (in
this case, short-term debt increases while long-term debt decreases). Growth is
negative correlated with all three leverage measures. Income variability is inversely
related to total debt and long-term debt ratio, while positive with short-term debt
ratio.
Table 4.4. Correlation coefficients among the explanatory variables
PROF SIZE TANG GRO NDTS INVAR TDTA LDTA SDTA
PROF 1
SIZE -0.031 1
TANG 0.160 -0.103 1
GRO 0.158 0.095 -0.024 1
NDTS 0.777 -0.016 0.263 -0.041 1
INVAR 0.065 -0.553 -0.047 0.064 -0.008 1
TDTA -0.115 0.191 0.140 -0.150 0.074 -0.089 1
LDTA 0.075 -0.033 0.606 -0.053 0.188 -0.232 0.392 1
SDTA -0.169 0.223 -0.240 -0.124 -0.041 0.054 0.808 -0.225 1

30
4.4 Results of Model 1
Model 1 aims to shed the light on the impacts of the financial leverages (TDTA,
SDTA and LDTA) on determinants of capital structure, and find out whether those
impacts shift over years.
As can be seen in table 4.5. (the section of dependent variable TDTA), the
statistically significant variables at the 99% confidence level are profitability, firm
size, assets tangibility and income variability. The insignificant variables are grow
opportunities and non-debt tax shield. Since the variables of grow opportunities and
non-debt tax shield are not significant, the hypotheses H4 and H5 cannot be
supported by the data from the 77 non-financial firms considered in this study.
The estimated results from Model A present that profitability is negatively
associated with the total debt ratio (TDTA). Take more look on the results of
LDTA and SDTA, they are also negative ones. This is consistent with the
hypothesis 1 (H1). Profitability is negatively correlated with all three leverage
measures, which is in line with the pecking-order theory; firms prefer using surplus
generated by profits to finance investments. This result may also indicate that firms
in general always prefer internal funds rather than external funds, irrespective of
the characteristic of an asset that shall be financed (e.g. tangible or non-tangible
asset).

31
Table 4.5. The reported results of Model 1
DEPENDENT
VARIABLES
TDTA LDTA SDTA
Model A Model B Model A Model B Model A Model B
Coef. t-statistics Coef. t-statistics Coef. t-statistics Coef. t-statistics Coef. t-statistics Coef. t-statistics
Explanatory variables
Profitability (PROF) -0.1274*** -2.81 -0.1150*** -2.65 -0.0170 -0.65 -0.0121 -0.46 -0.1129*** -2.68 -0.1052** -2.56
Firm size (SIZE) 0.0961*** 3.18 0.1174*** 3.78 -0.0190 1.19 -0.0092 -0.56 0.1142*** 4.12 0.1229*** 4.28
Assets tangibility (TANG) 0.2344*** 4.33 0.2554*** 4.87 0.2873*** 9.61 0.2909*** 9.70 -0.0453 -0.91 -0.0260 -0.53
Grow opportunities (GRO) 0.0067 1.32 -0.0070 -1.13 0.0020 0.68 -0.0031 -0.83 0.0045 0.95 -0.0049 -0.83
Non-debt tax shield (NDTS) 0.0373 0.43 -0.0356 -0.41 -0.0430 -0.90 -0.0600 -1.23 0.1109 1.37 0.0631 0.78
Income variability (INVAR) 0.1804*** 2.63 0.0545 0.76 -0.1119*** -2.88 -0.1485*** -3.52 0.2782*** 4.37 0.1858*** 2.76
Time dummies
2006 Omitted Omitted Omitted
2007 -0.0674*** -3.90 -0.0165 -1.58 -0.0499*** -3.05
2008 -0.1169*** -5.75 -0.0310** -2.54 -0.0878*** -4.57
2009 -0.0758*** -3.80 -0.0204* -1.71 -0.0568*** -3.02
2010 -0.0770*** -3.59 -0.0330*** -2.59 -0.0454** -2.25
Constant -0.7714** -2.13 -0.9099** -2.48 0.2500 1.31 0.1700 0.87 -1.0160*** -3.06 -1.0407*** -3.07
Number of observations 385 385 385 385 385 385
Number of firms 77 77 77 77 77 77
Notes : The dependent variable is TDTA, LDTA and SDTA of firms listed on HOSE. Base year is 2006.
Model A is the model without time dummies and Model B is with time dummies.
***,**,* significant at 1%, 5% and 10% respectively

32
The results reveal that size is a significant determinant of leverage. But while size
is positively related to both total debt and short-term debt ratio, it is negatively
correlated with long-term debt ratio (and the result is insignificant). Even if the data
does not allow us to further decompose short-term debt, we may still find the
results of Bevon and Danbolt (2000) interesting. They find that while size is
positively correlated with both trade credit and equivalent and short-term
securitized debt, it is negatively correlated with short-term bank borrowing. This
may indicate that small firms are supply constrained, in that they do not have
sufficient credit ranking to allow them to long-term borrowing. At least, the result
is consistent with the hypothesis 2 (H2).
As can be seen, the coefficients of tangibility are highly statistically significant for
total debt and long-term debt ratio. While the results show that tangibility has a
positive relationship with total debt ratio and long-term debt ratio - as expected
according to the theoretical discussion above and consistent with the hypothesis 3
(H3); tangibility is negatively related to the short-term debt ratio. This finding is
consistent with the results of Bevan and Danbolt (2000), Huchinson et. al. (1999),
Chittenden et. al. (1996) and Van der Wijst and Thurik (1993) report (see also
Michaleas et.al., 1999). Indeed, this result supports the maturity matching principle:
Long-term debt forms are used to finance fixed (tangible) assets, while non-fixed
assets are financed by short-term debt (Bevan and Danbolt, 2000).
Table 4.5. reveals that the effect of income variability on debt is positively,
contrary with the hypothesis 6 (H6) but still statistically significant. According to
Lööf (2003), who also obtained similar results, this may be due to the fact that the
time period studied coincided with a period of strong economic recovery and a
generally positive trend in revenues. For this result, hypothesis 6 (H6) is rejected in
this study.

33
About the grow opportunities and non-debt tax shield, the result are insignificant
and contrary with hypotheses 4 and 5 (H4 and H5). The regression show that these
two variables have positive impact on the determinant of capital structure (for all:
ratio of total debt as well as short-term debt and long-term debt). For that result, H4
and H5 are rejected in this study.
In brief, there is only profitability has negative impact on the determinant of capital
structure of 77 firms listed on Hochiminh Stock Exchange in the period 2006 –
2010. Other factors such as firm size, assets tangibility, grow opportunities, non-
debt tax shield and income variability have positive effect.
Following Michaelas et. al. (1999), we present the regression coefficients of the
time dummies, which represent unobserved time-specific effects. Model B of table
4.5. reveals that almost all of the time dummies are significant (the base year is
2006). While this is in line with the declining total and long-term debt ratios
observed in table 2.1. above, it is not clear why the time dummy coefficients are
mostly negative even for the short-term debt, which has not decreased during the
period (2006 – 2010). Anyway, the decrease in total and long-term debt ratio may
reflect the impact of global financial crisis on Vietnamese economy. As a result,
banks had to limit and eliminate the previous loans, which is revealed by the (all)
negative coefficients.
The duty of Model 2 is to find out the effects of industry dummies on the impacts
of the three financial leverages on determinants of capital structure. There are also
two model: Model A and model B as in model 1 above. And model A here is the
same with that in model 1. We only do one more test including industry dummies
(model B) to compare the difference with model A and find out the impact of
industry dummies on the result.

34
DEPENDENT VARIABLES
TDTA LDTA SDTA
Model A Model B Model A Model B Model A Model B
Profitability (PROF) -0.1274*** -2.81 -0.1259*** -2.79 -0.0170 -0.65 -0.0194 -0.75 -0.1129*** -2.68 -0.1111*** -2.65
Firm size (SIZE) 0.0961*** 3.18 0.1194*** 3.58 -0.0190 1.19 -0.0209 -1.17 0.1142*** 4.12 0.1406*** 4.65
Assets tangibility (TANG) 0.2344*** 4.33 0.2610*** 4.63 0.2873*** 9.61 0.2623*** 8.36 -0.0453 -0.91 0.0026 0.05
Grow opportunities (GRO) 0.0067 1.32 0.0083 1.60 0.0020 0.68 0.0015 0.51 0.0045 0.95 0.0063 1.33
Non-debt tax shield (NDTS) 0.0373 0.43 0.0035 0.04 -0.0430 -0.90 -0.0564 -1.14 0.1109 1.37 0.0940 1.14
Income variability (INVAR) 0.1804*** 2.63 0.2120*** 3.02 -0.1119*** -2.88 -0.1010** -2.53 0.2782*** 4.37 0.3028*** 4.67
Industry dummies
Basis Materials 0.0845 0.52 -0.0138 -0.18 0.0938 0.66
Construction & Materials 0.1813 1.11 0.0075 0.10 0.1698 1.39
Consumer Discretionary 0.1915 1.15 -0.0151 -0.19 0.2026 1.19
Consumer Staples 0.0820 0.51 -0.0282 -0.37 0.1063 0.76
Industrials 0.2535 1.53 -0.0033 -0.04 0.2496* 1.72
Information Technology 0.1316 0.59 0.0419 0.39 0.0878 0.45
Oil/Gas 0.0334 0.19 0.0400 0.49 -0.0113 -0.08
Real Estate 0.1268 0.71 0.1095 1.28 0.0142 0.09
Transportation 0.1281 0.77 -0.0040 -0.05 0.1270 0.87
Utilities 0.1292 0.73 0.0983 1.15 0.0163 0.1
Multi-Scope Business and Group Omitted Omitted Omitted
Constant -0.7714** -2.13 -1.1817*** -2.73 0.2500 1.31 0.2795 1.23 -1.0160*** -3.06 -1.4652*** -3.75
Number of observations 385 385 385 385 385 385
Number of firms 77 77 77 77 77 77
Notes : The dependent variable is TDTA, LDTA and SDTA) of firms listed on HOSE
Model A is the model without industry dummies and Model B is with industry dummies.
***,**,* significant at 1%, 5% and 10% respectively

35
As can be seen in table 4.6. above, both models provide similar results in almost
significant variables, but the effect of industry dummies makes income variability
variable in the test of LDTA become less significant (from 99% down to 95%).
Moreover, the assets tangibility variable in the test of SDTA becomes positive in
model B (with industry dummies), and this result still insignificant as that with
Model A.
Let take a look onto model B, we could find that all industries (except for Multi-
Scope Business and Group omitted) have positive influent on the total debt ratio
(TDTA) though there is no significant result. But in the result of LDTA, there are
five industries are negatively associated with LDTA, they are Basis Materials,
Consumer Discretionary, Consumer Staples, Industrials and Transportation. While
in the result of SDTA, there is only result of Oil/Gas get negative value. And in the
result of SDTA, there are Oil/Gas that is negatively associated with SDTA.
Our last question is to analyze whether the determinants of capital structure are
different for different firm sizes. We divide the sample into three different firm
sizes based on small, medium and large. Table 4.7. presents the results for the
small, medium and large firms.
We will consider the results of each explanatory variables (profitability, assets
tangibility, grow opportunities, non-debt tax shield and income variability) by order
of firms sizes: Small – Medium – Large.

36
TDTA LDTA SDTA TDTA LDTA SDTA TDTA LDTA SDTA
Profitability (PROF)
Coef. -0.0957* -0.0215 -0.0776* -0.5585*** -0.0509 -0.4986*** -0.2415 -0.8306*** -0.5891
t-statistics -1.95 -0.86 -1.68 -3.90 -0.57 -3.70 -0.34 -5.31 -0.79
Assets tangibility (TANG)
Coef. 0.0775 0.2281*** -0.1019 0.1573* 0.3651*** -0.1909** 0.2881 0.1977*** 0.0904
t-statistics 0.94 6.73 -1.34 1.91 7.30 -2.41 1.20 3.77 0.36
Grow opportunities (GRO)
Coef. 0.0083 0.0015 0.0042 0.0095 0.0051 0.0056 0.0313 0.0051 -0.0262
t-statistics 1.15 0.42 0.62 1.04 0.90 0.64 0.70 0.52 0.56
Non-debt tax shield (NDTS)
Coef. 0.0090 0.0401 0.0323 0.6017*** -0.0378 0.6634*** -1.1113 0.7447** -1.8557
t-statistics 0.08 0.92 0.31 3.14 -0.32 3.64 -0.67 2.06 -1.08
Income variability (INVAR)
Coef. 0.1096* -0.0507 0.1600* -0.7232 0.1143 -1.0536 -40.1276 -39.9909*** -0.1255
t-statistics 1.64 -1.60 2.57 -1.07 0.28 -1.63 -1.17 -5.35 -0.00
Constant
Coef. 0.3868*** -0.0024 0.3703*** 0.4738*** 0.0120 0.4584*** 0.6426*** 0.4232*** 0.2193
t-statistics 9.41 -0.15 9.77 9.33 0.39 9.37 2.74 8.25 0.90
R-squared 0.0006 0.3446 0.0282 0.1132 0.3714 0.0819 0.5298 0.9544 0.1598
Number of observations 199 170 16
Notes : ***,**,* significant at 1%, 5% and 10% respectively

37
Profitability is negatively correlated with all three leverage measures of small size
firms, which is in line with the pecking-order theory; firms prefer using surplus
generated by profits to finance investments. The result is also negatively with
medium and large size firms, especial significant in TDTA and SDTA of medium
firms and LDTA of large firms. One more point is that, the result is significant at
TDTA and SDTA of small and medium firms, while significant at LDTA of large
firms. Anyway, this negative result is consistent with hypothesis 1 (H1):
Profitability is expected to effect negative on leverage.
As can be seen, the coefficients of tangibility are highly statistically significant for
long-term debt measure of three firms sizes. But while the results show that
tangibility has a positive relationship with total debt ratio and long-term debt ratio -
as expected according to the theoretical discussion above, tangibility is negatively
related to the short-term debt ratio (except for large size firms). This result is
consistent with the results of Bevan and Danbolt (2000), Huchinson et. al. (1999),
Chittenden et. al. (1996) and Van der Wijst and Thurik (1993) report (see also
Michaleas et.al., 1999). Indeed, this result supports the maturity matching principle:
Long-term debt forms are used to finance fixed (tangible) assets, while non-fixed
assets are financed by short-term debt (Bevan and Danbolt, 2000). This result is
consistent with hypothesis 3 (H3): Assets tangibility is expected to effect positive
on leverage.
According to the theoretical discussion above, we either expect a negative
relationship between growth opportunities and leverage. The coefficient estimate
for growth in this model is insignificant and positive for all three leverages of three
firms sizes (except for short-term debt of large firms). This result can be explained
that, as economy grows, leverage increases. This result is inconsistent with
hypothesis 4 (H4): Grow opportunities is expected to effect negative on leverage.

38
According to the result, non-debt tax shield has no correlation with leverage of
small firms. For medium firms, the result is significant at total debt and short-term
debt ratio, but it is positive, inconsistent with hypothesis 5 (H5): Non-debt tax
shield is expected to effect negative on leverage. The situation is the same with
large firms which only have significant result at long-term debt ratio, but positive.
The result of income variability coefficient just get significant value at LDTA of
large firms. For that reason, the hypothesis 6 (H6: Income variability is expected to
effect negative on leverage) is inconsistent.
Therefore, according to our sample the determinants of capital structure show some
differences among small and medium size enterprises and large firms. Collateral is
important for all types of firms to access debt financing and they follow the
maturity matching principle. Also the firms follow the pecking order; therefore,
they choose to be financed internally first. However, for short-term debt financing,
profitability does not have any impact for small and large firms, on the other hand,
it does not impact small and medium firms at long-term debt. The effect of assets
tangibility variables shows differences among small, medium and large firms, but it
is really significant at long-term debt of all three firms sizes. Grow opportunities
variable is not influent by the firm sizes. Non-debt tax shield variable shows its
significant effect on total debt (especial short-term debt) of medium firms and long-
term debt of large firms. And income variability also effects on long-term debt of
large firms.
4.7 Robustness tests
Robustness tests are run to ensure the validity of the results. We will test with
Model 1 (Model 2 is the same as model 1) and Model 3 (just test with variable
TDTA of three firms sizes).

39
Table 4.8. Results of Model 1 : Fixed Effects versus Random Effects
DEPENDENT
VARIABLES
TDTA LDTA SDTA
Fixed Effects Random Effects Fixed Effects Random Effects Fixed Effects Random Effects
Profitability (PROF) -0.0958** -2.13 -0.1274*** -2.81 -0.0185 -0.71 -0.0170 -0.65 -0.773* -1.85 -0.1129*** -2.68
Firm size (SIZE) 0.0899** 2.12 0.0961*** 3.18 -0.0153 -0.62 -0.0190 1.19 0.1052*** 2.66 0.1142*** 4.12
Tangibility (TANG) 0.2769*** 4.53 0.2344*** 4.33 0.2334*** 6.59 0.2873*** 9.61 0.0434 0.76 -0.0453 -0.91
Grow opportunities (GRO) 0.0103** 1.97 0.0067 1.32 0.0017 0.56 0.0020 0.68 0.0086* 1.76 0.0045 0.95
Non-debt tax shield (NDTS) -0.1314 -1.21 0.0373 0.43 -0.2245*** -3.58 -0.0430 -0.9 0.0931 0.92 0.1109 1.37
Income variability (INVAR) 0.2162*** 2.98 0.1804*** 2.63 -0.0416 -0.99 -0.1119*** -2.88 0.2578*** 3.82 0.2782*** 4.37
Constant -0.6993 -1.38 -0.7714** -2.13 0.2482 0.85 0.2500 1.31 -0.9481** -2.01 -1.0160*** -3.06

40
In Model 1 (and also Model 2), the selection between Fixed and Random Effects at
the result of total debt ratio (TDTA) does not make a difference for the study,
because the variable of profitability, firm size, assets tangibility and income
variability are significant in both models (as can be seen from Table 4.8.). And in
Fixed Effects, the variable of growth opportunities become significant.
Furthermore, the addition of time dummies (in Model 1) and industry dummies (in
Model 2) also do not make profitability, firm size, assets tangibility and income
variability insignificant (as can be seen from Table 4.5. and Table 4.6.).
Consequently, suggesting that Model 1 as above presented is reliable with the four
significant explanatory variables: PROF, SIZE, TANG and INVAR.
In Model 3, the selection between Fixed Effects and Random Effects make a
difference for the study because the variable of PROF, TANG, NDTS, are still the
same at Fixed Effects and Random Effects; but Fixed Effects makes GRO and
INVAR of medium firms become significant (as can be seen from Table 4.9.).
However, we have noticed that the coefficients obtained with Fixed Effects do not
look correct from an Economics perspective in many cases, while the ones returned
by Random Effects do.

41
Table 4.9. Results of Model 3: Fixed Effects versus Random Effects
FE RE FE RE FE RE
Profitability (PROF)
Coef. -0.0727 -0.0957* -0.4682*** -0.5585*** -0.2968 -0.2415
t-statistics -1.50 -1.95 -3.18 -3.90 -0.72 -0.34
Tangibility (TANG)
Coef. 0.1507 0.0775 0.2786*** 0.1573* -1.5563 0.2881
t-statistics 1.64 0.94 2.63 1.91 -0.97 1.20
Grow opportunities (GRO)
Coef. 0.0106 0.0083 0.0288** 0.0095 0.0006 0.0313
t-statistics 1.43 1.15 2.58 1.04 0.02 0.70
Non-debt tax shield (NDTS)
Coef. -0.2806* 0.0090 0.7148*** 0.6017*** -0.9303 -1.1113
t-statistics -1.76 0.08 3.04 3.14 -0.81 -0.67
Income variability (INVAR)
Coef. 0.1862*** 0.1096* -2.7281*** -0.7232 -28.0520* -40.1276
t-statistics 2.62 1.64 -2.98 -1.07 -2.52 -1.17
Constant
Coef. 0.3863*** 0.3868*** 0.4368*** 0.4738*** 1.1772* 0.6426***
t-statistics 9.67 9.41 8.16 9.33 2.05 2.74
Number of observations
199 170 16

42
CHAPTER 5: CONCLUSION
5.1 Introduction
This chapter presents main conclusions and recommendations for further study
based on the results of the previous chapters, as well as the limitations of this study.
5.2 Conclusion
This study investigated the determinant of capital structure of a sample of
Vietnamese firms listed on Hochiminh Stock Exchange in period 2006 – 2010
utilizing panel data analysis. Three different leverage measures based on book
values have been applied: Total debt ratio, long-term debt ratio, and short-term debt
ratio. The empirical evidences provide that there exist significant differences in the
determinants of these three leverage measures. While all three forms of debt ratio
are significantly related to income variability, profitability and firm size are related
to the total and short-term debt, and assets tangibility is related to the total and
long-term forms of debt. Grow opportunities and non-debt tax shield are not related
to any of the three debt measures.
Firm profitability is found to have a significant and negative impact on TDTA,
SDTA. These findings could to some extent support pecking order theory of Myers
and Majluf (1984). They argue that internal funds are used first, and when that is
depleted, debt is issued, and when it is not sensible to issue any more debt, equity is
issued. An interesting finding is that firm size has a positive and significant impact
on the leverage measures TDTA, SDTA. This finding is consistent with a previous
study of Rajan and Zingales (1995), and indicating that a firm size is an important
determinant of corporate capital structure. Firm asset tangibility is found to have
positive and significant impact on TDTA and LDTA. This finding supports the
argument of previous researchers such as Titman and Wessels (1988), Rajan and
Zingales (1995), Booth et al. (2001), Margaritis and Psillaki (2007). They argue

43
that the greater the proportion of tangible assets on the balance sheet, the more
willing should lenders be to supply loans, and leverage should be higher. Firm
growth opportunities does not have significant impact on any leverage measures,
which is inconsistent with Myers (1977), Berens and Cuny (1995). They argue that
firms with high-growth opportunity may not issue debt in the first place and
leverage is expected to be negatively related with growth opportunities. Firm non-
debt tax shield does also not have significant impact on all three leverage measures,
and this finding is inconsistent with DeAngelo and Masulis (1980) and Wanzenried
(2002), who argue that non-debt tax shield are substitutes for the tax benefit of debt
financing, therefore, the tax advantage of leverage decreases when other tax
deductions like depreciation increase. Firm income variability has significant and
possitive impact on TDTA and SDTA while has significant and negative impact on
LDTA. This result is inconsistent with Loof (2003) who did research in the period
1991-1998 (coincided with a period of strong economic recovery and a generally
positive trend in revenues ) and got the effect of income variability on debt
approximately zero but still statistically significant.
The most interesting finding in this study is though that there exist significant
differences between short-term and long-term debt ratios in three cases. While firm
size is positively related to both total debt and short-term debt ratio, it is negatively
correlated with long-term debt ratio. There may be explained by high interest rate
during the last three years which have demotivated long term borrowing.
Furthermore, assets tangibility is positively related to long-term debt (and total debt
as well), it is negatively related to short-term debt. Finally, while income variability
has a positive effect on total and short-term debt ratio, it is negatively correlated
with long-term debt ratio.
These findings suggest that future analysis of leverage determinants should be
based on not only long-term or total debt ratios, but on short-term debt ratios as
well. This may be of particular interest and importance for the Vietnamese case,

44
since short-term debt constitutes a major part of total debt (see table 2.1. above).
Why do Vietnamese firms have such a low long-term debt ratio? One possible
reason is that Vietnamese firms prefer and have access to equity financing once
they go public, as most firms enjoy a favorable high stock price. Another possible
explanation is the fact that the Vietnamese bond market is still in an infant stage of
development. Banks are the major or even the only source of firms’ external debt.
As a result, firms have to rely on equity financing and trade credit, where firms owe
each other in the form of accounts payable. In order to provide more financing
opportunities for Vietnamese firms, it is desirable for Vietnam to accelerate the
development of its bond market.
Back to short-term debt ratio, due to data limitations, we have not been able to
decompose short-term debt to its basic elements. Only when we have data on for
instance trade credit and equivalent, short-term securitized debt and short-term
bank borrowing, we may find answers to why Vietnamese firms have such large
short-term debt ratios. Indeed, Bevan and Danbolt (2000) argue that a fuller
understanding of capital structure and its determinants requires a detailed analysis
of all forms of corporate debt.
The next findings in this study is the effect of two specific dummies: Time and
industry dummies. Both time dummies and industry dummies do not change the
significant relation of three forms of debt ratio to the determinants of capital
structure. However, time dummies make a decrease in total and long-term debt
ratio, reflecting the affect of global financial crisis on Vietnamese economy. On the
other hand, among industry dummies, there is only firms in Industrials sector have
significant relation with short-term debt ratio.
5.3 Limitations
As can be seen, this study includes the sample of 77 non-financial listed firms on
HOSE. Financial firms such as funds, banks, securities companies, insurance

45
companies are excluded from the study. Moreover, doing test with firms not listed
in HOSE may bring us a more complete view of the determinant of capital.
Due to lacking in information, some interesting proxies of potential determinants of
capital structure are not considered. The institutional holdings (proxy the ownership
structure) and managerial holdings (proxied by number of shares held by top
managers, directors and supervisors scaled by the number of shares outstanding)
may be add for testing in future research.
Finally, this study is only stopped as an academic research to test the determinants
of capital structure of listed firms on Hochiminh Stock Exchange. Based on this
research, researchers and corporate managers can be continue to implement more
research, in order to evaluate more precisely about the determinants of capital
structure of Vietnamese firms.
5.4 Recommendations
From the above mentioned limitations of the study, we suggest for applying
dynamic panel data regression in future research to make it possible to reveal
interesting relationships between short - term and long - term leverage, from which
important discussions on the relationship between financial systems, corporate debt
structure and growth may be based upon.
Moreover, firms’ capital structure includes debt and equity. This thesis only study
the influence of several potential determinant on debt structure, and there are need
for finding more factors that impact the determinants of equity structure. We could
mention about the impact on firm capital mobilization of the development of
Vietnamese stock exchange, or diluted factors of increasing the firm share capital
that affect the determinants of increasing debt or equity, etc …
Tải bản FULL (104 trang): https://bit.ly/3YsJvhu
Dự phòng: fb.com/TaiHo123doc.net

46
From the findings of this study it would also be useful to consider the following
directions for future research :
 What determines the capital structure of financial listed firm on HOSE as
well as HASE?
 Are determinations of capital structure of firms listed on HOSE different
with those of firms listed on HASE? What is the difference? (if any)
 Are determinations of capital structure of listed firms different with those of
non-listed firms? What is the difference? (if any) Evidence from Vietnam.
 How firms capital mobilization impacted by the development of Vietnamese
stock exchange?
Tải bản FULL (104 trang): https://bit.ly/3YsJvhu
Dự phòng: fb.com/TaiHo123doc.net

47
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