Luận văn thạc sĩ UEH determinants of capital structure evidence from listed companies on hochiminh stock exchange

INTRODUCTION

Introduction

One of the tough challenges that firms face is the choice of capital structure

The capital structure decision is crucial as it directly influences a firm's financial performance According to Abor J (2005, p.438-45), a firm's capital structure refers to the specific combination of debt and equity utilized to fund 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:

Recent advancements in theory have enhanced our understanding of how capital structure impacts a firm's value, highlighting key deviations from the Modigliani and Miller assumptions However, the empirical significance of these various theories remains largely unexplored, as noted by Harris and Raviv (1991).

Numerous capital structure theories have identified various potential determinants; however, empirical studies have not clarified their significance across different contexts Most existing research focuses on developed countries, particularly G7 nations, leaving a gap in understanding capital structure in developing countries Recent studies, such as those by Booth et al (2001) and Huang and Song (2002), have begun to address this issue, but there is still a lack of research specific to Vietnam, especially regarding the Hochiminh Stock Exchange (HOSE) This thesis aims to address this gap by investigating the capital structure of listed firms in HOSE.

The remainder of this chapter provides general introduction about the research objectives, research methodology and the structure of the research.

Research objectives

This research focuses on firms listed on the Ho Chi Minh Stock Exchange (HOSE) in Vietnam, aiming to empirically investigate the relationship between various capital structure determinants and debt measures for non-financial companies during the period from 2006 to 2010.

The purpose of this research is looking for answers to the following questions:

The financial leverage of listed firms on the Ho Chi Minh Stock Exchange, measured by total debt ratio, long-term debt ratio, and short-term debt ratio, is significantly influenced by various determinants of capital structure, including profitability, firm size, asset tangibility, growth opportunities, non-debt tax shields, and income variability Additionally, the effects of these determinants on financial leverage may vary over the years, reflecting changes in market conditions and firm strategies.

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)?

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 :

 Using random effects logistic regression model to test the determinants of capital structure

Stata software version 11 is used as an data analysis tool to implement this research.

The structure of the research

The structure of the study consist five chapters:

This chapter presents introduction of the thesis, as well as research objectives and research methodology

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

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 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.

LITERATURE REVIEW

Introduction

This chapter summarizes the literature review on the determinants of capital structure, highlighting key variables that influence firms' decisions to adopt debt measures The aim of this review is to establish a foundational context for the research hypotheses.

Theoretical and Empirical Findings

Myers (2001) emphasizes that while a universal theory of the debt-equity choice is absent, several conditional theories provide insights into the capital structure decisions of firms These theories fall into two categories: static trade-off models, which suggest an optimal debt-equity ratio for each firm, and the pecking-order hypothesis, which posits that there is no clearly defined target capital structure.

Static trade-off models define the optimal capital structure as a balance between the tax shield benefits and the costs associated with financial distress According to trade-off theory, this optimal structure is reached when the marginal present value of tax benefits from additional debt equals the marginal present value of financial distress costs In signaling theory, a company's debt ratio serves as a signal of its quality, with a strong company needing a sufficient debt level to deter weaker firms from mimicking its signal Agency theory posits that the optimal capital structure minimizes agency costs, while free cash-flow theory emphasizes that a higher debt ratio compels managers to distribute excess cash rather than invest it inefficiently or waste it, particularly in firms with significant free cash flows.

The pecking-order theory posits that firms prioritize internal financing, such as retained earnings, over external funding When internal cash flow falls short for capital expenditures, companies opt to borrow rather than issue new equity Consequently, there is no clearly defined optimal leverage, as internal equity ranks higher than external equity in the financing hierarchy.

Most existing empirical studies primarily utilize data from developed nations, with notable research including Bradley et al (1984), Kim and Sorensen (1986), Friend and Lang (1988), and Titman and Wessels (1988), all focusing on U.S companies Additionally, Kester (1986) compares U.S and Japanese manufacturing firms, while Rajan and Zingales (1995) analyze firms from G7 countries Wald (1999) also contributes by examining data from G7 nations, excluding 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)

Research on Vietnam's economic landscape is limited, with notable studies including San (2002), which examined the tourism industry in Thua Thien Hue Province, and Nguyen and Ramachandran (2006), which concentrated on small and medium-sized enterprises (SMEs) In contrast, Vu (2003) provided insights into companies listed on the Ho Chi Minh City stock exchange, highlighting the diverse focus of these studies.

Listed companies, though fewer in number compared to unlisted firms, particularly small and medium-sized enterprises (SMEs), significantly contribute to economic activity, representing a larger portion of the economy In fact, the small business sector accounts for only around 25% of the gross domestic product (GDP).

This study aims to update Vu's (2003) analysis by examining the factors influencing leverage among companies listed on the Hochiminh Stock Exchange from 2006 to 2010.

Potential determinants of capital structure

This article explores key variables influencing firms' preferences for debt over equity financing According to Harris and Raviv (1991), the factors affecting capital structure decisions are vast and varied However, this paper will focus on the most frequently cited explanatory variables to provide a clearer understanding of these determinants.

Determinants of capital structure include various factors identified by researchers, such as Harris and Raviv (1991), who noted that leverage increases with fixed assets, non-debt tax shields, investment opportunities, and firm size, while it decreases with volatility, advertising expenditure, bankruptcy probability, profitability, and product uniqueness Titman and Wessels (1988) highlighted additional influences on leverage, including asset structure, growth potential, industry classification, and earnings volatility Despite a general consensus on a core set of determinants, the ongoing debate among scholars suggests that there is still significant scope for recognizing additional factors that may impact capital structure decisions.

In this thesis, following determinants will be used:

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

The pecking order theory, proposed by Myers and Majluf (1984), indicates that firms prioritize internal financing over external sources When external funds are necessary, companies first opt for debt, followed by hybrid securities like convertible bonds, and finally resort to equity as a last option (Brealey and Myers, 1991) This preference is influenced by the costs associated with issuing new equity, often stemming from asymmetric information and transaction costs The relationship between profitability and leverage is debated; while Myers and Majluf (1984) suggest a negative correlation, Jensen (1986) argues for a positive relationship in an effective corporate control market, but this shifts if the market is ineffective.

This study anticipates a negative correlation between profitability and leverage, suggesting that firms with higher profits tend to have lower leverage The hypothesis posits that increased leverage is inversely related to profitability, indicating that as profitability rises, leverage decreases.

Here, we use the ratio of earnings before interest and taxes (EBIT) to total assets as a measure profitability

The relationship between firm size and leverage remains ambiguous, as larger firms may be less likely to face bankruptcy due to diversification, potentially allowing them to issue debt at lower costs and leading to a positive correlation between size and leverage Conversely, Fama and Jensen (1983) suggest that larger firms, which typically provide more information to investors, may prefer equity over debt, as indicated by Rajan and Zingales (1995) Thus, the hypothesis of this study posits that leverage is either positively or negatively associated with firm size.

In this study, the natural logarithm of sales is employed as a proxy for company size, a common practice in similar research Alternatively, the natural logarithm of total assets could also serve as a proxy, as both measures are highly correlated (0.68 in 2006, 0.63 in 2007, 0.65 in 2008, 0.70 in 2009, and 0.71 in 2010) However, sales are preferred over total assets to mitigate the risk of spurious correlation.

Tangible assets are considered effective collateral, as their higher tangibility reduces creditor risk and enhances asset value during bankruptcy According to Booth et al (2001), firms with more tangible assets can issue secured debt more easily, revealing less about future profits This suggests a positive correlation between asset tangibility and leverage, supported by empirical studies from Rajan and Zingales (1995), Friend and Lang (1988), and Titman and Wessels (1988) Consequently, the hypothesis posits that leverage is positively associated with asset 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

Theoretical studies indicate a negative relationship between growth opportunities and leverage Jung, Kim, and Stulz (1996) suggest that when management focuses on growth, interests align for firms with strong investment prospects Conversely, for firms with limited investment opportunities, debt can help mitigate agency costs related to managerial discretion, as noted by Jensen (1986) and Stulz (1990) This disciplinary function of debt is further supported by Berger, Ofek, and Yermack (1997) However, Myers (1977) highlights that high-growth firms often possess more real options for future investments than their low-growth counterparts If these firms require additional equity financing to capitalize on such options, existing debt may hinder their ability to do so, as this could shift wealth from shareholders to debtholders Consequently, firms with significant growth opportunities are less likely to issue debt, reinforcing the expectation of a negative correlation between leverage and growth opportunities.

(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, including those by Booth et al (2001), Kim and Sorensen (1986), Rajan and Zingales (1995), Smith and Watts (1992), and Wald (1999), largely support theoretical predictions regarding growth opportunities, with Kester (1986) being a notable exception Various proxies have been utilized to measure growth opportunities; for instance, Wald (1999) employs a five-year average of sales growth, while Titman and Wessels (1988) use capital investment relative to total assets and research and development scaled by sales.

Rajan and Zingales (1995) utilize Tobin’s Q, the market-to-book ratio of total assets, while Booth et al (2001) focus on the market-to-book ratio of equity to assess growth opportunities This study posits that while sales growth rate reflects past growth experiences, Tobin’s Q serves as a more accurate indicator of future growth potential Consequently, Tobin’s Q is adopted as the primary measure of growth opportunities in this research.

Equity market value + Total liabilities GRO Total assets

2.3.5 Non-debt tax shield (NDTS)

Modigliani and Miller (1958) suggest that interest tax shields strongly incentivize firms to increase leverage However, non-debt-related corporate tax shields, such as depreciation deductions and investment tax credits, may also influence leverage decisions DeAngelo and Masulis (1980) argue that these non-debt tax shields serve as substitutes for the tax benefits derived from debt financing Consequently, as non-debt tax shields like depreciation rise, the tax advantages of leverage diminish (Wanzenried, 2002) Therefore, it is anticipated that an increase in non-debt tax shields will have a negative impact on leverage, leading to the hypothesis that leverage is negatively associated with non-debt tax shields.

Titman and Wessels (1988) identify the ratio of tax credits to total assets and the ratio of depreciation to total assets as indicators of non-debt tax shields In this thesis, we will utilize the ratio of depreciation to total assets as our sole measure of the non-debt tax shield, due to the availability of data.

Income variability serves as a key indicator of business risk, with increased earnings fluctuations correlating to a higher likelihood of bankruptcy Consequently, companies exhibiting greater income variability tend to maintain lower levels of leverage This leads to the hypothesis that leverage is inversely related to 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

To enhance our regression models, we will incorporate a full set of time dummies for each year, excluding the base year of 2006 This approach allows us to analyze potential shifts in leverage over time while controlling for other observable factors The inclusion of time dummies effectively captures unobserved time-specific effects, facilitating a more nuanced understanding of leverage dynamics (Lửửf, 2003).

Bevan and Danbolt (2000) enhance panel data regression by incorporating time dummies that interact with both the constant term and all explanatory variables They propose that this method allows for the simultaneous analysis of two key factors.

Measures of capital structure/financial leverage

Capital structure refers to the combination of various securities, such as long-term debt, common stock, and preferred stock, that a firm utilizes to finance its assets A firm without debt is considered unlevered, while one with debt is classified as leveraged The two primary types of leverage are operational leverage, which pertains to fixed operating costs, and financial leverage, associated with fixed debt costs Operational leverage heightens business risk, whereas financial leverage amplifies financial risk Consequently, total leverage encompasses both fixed operating and debt costs, indicating that a firm's overall risk comprises both business and financial risk In this analysis of capital structure determinants, we focus specifically on financial leverage, also known as gearing.

This study focuses on the firms' capital structure, also known as financial leverage, as its dependent variable Since Modigliani and Miller's seminal 1958 paper, numerous articles have explored the determinants of capital structure Various measures of capital structure are available, each capable of being assessed in multiple ways Generally, these measures fall into two primary categories: those based on the market value of equity and those based on the book value of equity (Lửửf, 2003) For example, research by Titman and Wessels highlights these distinctions.

In their 1988 study on capital structure choice, the authors identify six measures of financial leverage, including long-term, short-term, and convertible debt, analyzed in relation to both market and book values of equity However, due to data constraints, most empirical research has predominantly utilized leverage measures based on book values instead of market values.

Indeed, for this study, market data is not available enough, implying that we have to measure leverage in terms of booked values only

The issue of insufficient market data in empirical studies on capital structure determinants is significant, yet a comprehensive analysis is beyond this paper's scope When both book and market values are accessible, they are utilized together due to the distinct information each provides (Lửửf, 2003) Titman and Wessels (1988) reference Bowman's (1980) findings, which highlight a strong correlation between book and market values of debt Brealey and Myers (2003) contend that relying solely on book values may not be problematic, as market values often reflect intangible assets from research, development, and advertising that are not easily liquidated Consequently, the potential for misspecification from using book values alone may be minimal or negligible.

Selecting the appropriate leverage measure as a dependent variable is crucial, regardless of market or book value Rajan and Zingales (1995) emphasize that the choice of leverage measure should align with the analysis's objective They conclude that the ratio of total debt to capital—defined as total debt plus equity—most effectively reflects the impact of past financing decisions.

Harris and Raviv (1991) highlight the complexities in measuring leverage and its related variables, emphasizing that the interpretation of empirical study results requires caution Different studies adopt various methods for measuring leverage, such as using the book value of debt relative to book or market value of equity, which can lead to inconsistent findings Moreover, the challenges in measurement are compounded by typical issues encountered in interpreting statistical results.

In this study, we will analyze leverage measures, specifically focusing on the ratio of total debt to total assets, to provide a comprehensive understanding of their implications.

2.4.2 Decomposition of total debt into short-term and long-term debt ratios

Examining the sources of debt is crucial, particularly for Vietnamese firms, where total liabilities are categorized into short-term and long-term debt, equating total liabilities with total debt Understanding the specific components of these categories, such as the size of trade credit within short-term debt, would provide valuable insights A cross-sectional analysis by Bevan and Danbolt (2000) highlights significant differences in the determinants of short-term and long-term debt among UK companies, where short-term debts, including trade credit, constitute over 62% of total debt This finding underscores the importance of including short-term debt in leverage measures, as it can greatly influence the results.

Hence in line with their findings, Bevan and Danbolt argue that analysis of corporate structure is incomplete without a detailed examination of corporate debt

A study by Michaelas et al (1999) on the capital structure of small and medium-sized enterprises (SMEs) reveals that key determinants such as size, profitability, and growth significantly affect both short-term and long-term debt ratios Additionally, the research indicates that time and industry factors play a crucial role in shaping the maturity structure of debt for SMEs By examining the coefficients of time dummies from 1988 to 1995, the study correlates these changes with fluctuations in real GDP.

A study by Michaelas et al revealed that short-term debt ratios in small and medium-sized enterprises (SMEs) are negatively correlated with fluctuations in economic growth, whereas long-term debt ratios show a positive correlation with economic growth changes.

This article analyzes the factors influencing corporate debt by examining both short-term and long-term debt ratios, resulting in two distinct leverage measures The findings, detailed in Table 2.1, reveal that the average short-term debt ratio is significantly higher, approximately four times greater than the long-term debt ratio.

Between 2006 and 2008, there was a notable decline in both the mean and median values for short-term leverage, while the long-term debt ratio remained stable without a distinct 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

Mean Median Mean Median Mean Median

Building on the insights gained from the decomposition of total debt and the conflicting results from Bevan and Danbolt (2000) and Michaelas et al (1999), we will incorporate two new leverage measures into our econometric analysis.

While we lack specific data on the size of trade credit, it is reasonable to speculate that it constitutes a significant portion of short-term debt due to its role in facilitating immediate business transactions and supporting cash flow.

Trade credit and similar components make up a significant portion of short-term debt, indicating a strategic corporate debt policy According to Bevan and Danbolt (2000), companies may prefer trade credit due to its lower associated costs compared to other borrowing options.

Conclusion

The capital structure of firms in emerging markets, such as Vietnam, differs significantly from that of developed markets in terms of nature, characteristics, and efficiency A review of existing literature highlights various factors influencing capital structure determinants and financial leverage, which vary considerably across countries This study focuses on the Hochiminh Stock Exchange to identify which theoretical frameworks and empirical studies are applicable to Vietnamese firms, as there is currently a lack of clarity regarding their capital structure dynamics.

RESEARCH METHODOLOGY

Introduction

This chapter outlines the research data collection process and the applied research hypothesis and methodology used to test the research objectives, drawing from the research objectives and methodology discussed in Chapter 1 and the literature review presented in Chapter 2.

Data specifications

This thesis analyzes the accounting and market data of 77 companies listed on the Hochiminh Stock Exchange (HOSE) from 2006 to 2010 The comprehensive dataset includes detailed information such as balance sheets, income statements, and depreciation figures, all of which are legally required to be publicly available The data was sourced from reputable websites, cafef.vn and vinabull.com.

The study analyzed a sample of 77 non-financial firms listed on the Hochiminh Stock Exchange between 2006 and 2010 The selection criteria included the exclusion of financial firms such as banks and securities companies due to their highly regulated nature, as noted in previous research by Fama and French (2001) and DeAngelo et al (2006) Additionally, all selected firms had to have complete financial data and uninterrupted trading activities, ensuring compliance with Hochiminh Stock Exchange regulations.

The data has been collected from the web of Hochiminh Stock Exchange for the period 2006 to 2010, specified as follows :

This study analyzes the relationship between various explanatory variables—PROF, SIZE, TANG, GRO, NDTS, and INVAR—and dependent variables such as TDTA, SDTA, and LDTA The data was derived from the Audited Annual Financial Statements of 77 firms over a five-year period from 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

This study examines eight explanatory variables: profitability (PROF), firm size (SIZE), asset tangibility (TANG), growth opportunities (GRO), non-debt tax shields (NDTS), income variability (INVAR), along with time and industry dummies These variables were introduced and defined in the previous chapter, providing a comprehensive framework for analysis.

This research identifies three key dependent variables: Total Debt to Total Assets (TDTA), Short-Term Debt to Total Assets (SDTA), and Long-Term Debt to Total Assets (LDTA).

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

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

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

In this study, we employ a random effects generalized least squares (GLS) regression model to analyze a cross-sectional time-series dataset across three distinct models Additionally, we conduct robustness tests to verify the reliability of our findings.

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:

LEV it = α + β 1 PROF it + β 2 SIZE it + β 3 TANG it + β 4 GRO it + β 5 NDTS it + β 6 INVAR it + γ n T ni + ε it

 LEV: TDTA, SDTA and LDTA of firm i at year t

 i = 1… 77 cross-sectional observation unit in the sample

 n = 2 … 5 time period (except 2006 as base year)

 ε it is residual error for firm i in year t

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

LEV it = α + β 1 PROF it + β 2 SIZE it + β 3 TANG it + β 4 GRO it + β 5 NDTS it + β 6 INVAR it + υ n X ni + ξ it

 n = 2 … 11 industries (except MS as base coefficient)

 ξit is residual error for firm i in year t

This model tests the difference of determinants of capital structure of listed firms in Hochiminh Stock Exchange with different size (small, medium, large)

LEV it = α + β 1 PROF it + β 2 TANG it + β 3 GRO it + β 4 NDTS it + β 5 INVAR it + μ it

 μit is residual error for firm i in year t

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

DATA ANALYSIS AND FINDINGS

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

Table 4.1 Summary of the industry structure

Multi-scope business and group (MS) 1 1.30% 75.32%

Between 2006 and 2010, a study of 77 non-financial listed firms on the Hochiminh Stock Exchange revealed key descriptive statistics of explanatory variables The average total debt ratio (TDTA) was 43.08%, indicating a reliance on debt to finance assets Asset tangibility averaged 29.67%, suggesting limited investment in fixed assets Profitability averaged 29.31%, with a minimum loss of -41.38% The average firm size was 11.74, reflecting a lack of significant investment in assets among listed firms Additionally, the average growth rate for these firms was 172.66%.

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

Table 4.3 presents a comparison of mean levels among firms of varying sizes The analysis reveals that small firms exhibit the highest mean in profitability, income variability, and short-term debt ratio In contrast, medium-sized firms lead in tangibility, non-debt tax shields, and total debt ratios Meanwhile, large firms not only have the highest mean in size but also boast the greatest growth 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

Correlation matrix of explanatory variables

Table 4.4 presents correlations between the dependent and independent variables

Asset tangibility shows a positive correlation with the total debt ratio, aligning with expectations that fixed assets can serve as collateral, thereby increasing debt levels This relationship extends to long-term debt, although asset tangibility negatively correlates with short-term debt Conversely, the non-debt tax shield positively correlates with total debt, contrary to expectations Profitability is inversely related to the total debt ratio, supporting Pecking Order theory, which suggests that profitable firms prefer internal financing Additionally, firm size positively correlates with total debt, indicating that as firms grow, their debt levels rise, specifically with an increase in short-term debt and a decrease in long-term debt Growth negatively correlates with all three leverage measures, while income variability shows an inverse relationship with total and long-term debt ratios, but a positive correlation with short-term debt.

Table 4.4 Correlation coefficients among the explanatory variables PROF SIZE TANG GRO NDTS INVAR TDTA LDTA SDTA

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

Table 4.5 reveals that at the 99% confidence level, profitability, firm size, asset tangibility, and income variability are statistically significant variables affecting the dependent variable TDTA Conversely, growth opportunities and non-debt tax shield are deemed insignificant, leading to the conclusion that hypotheses H4 and H5 are not supported by the data from the 77 non-financial firms analyzed in this study.

The findings from Model A indicate a negative association between profitability and the total debt ratio (TDTA), with similar negative correlations observed for both long-term debt to total assets (LDTA) and short-term debt to total assets (SDTA), supporting hypothesis 1 (H1) This suggests that profitability is inversely related to all three leverage measures, aligning with the pecking-order theory, which posits that firms prefer to use internally generated surplus profits for financing investments Additionally, the results imply that companies generally favor internal funding over external financing, regardless of the type of asset being financed, whether tangible or intangible.

Table 4.5 The reported results of Model 1

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

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

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

The findings indicate that firm size significantly influences leverage, showing a positive correlation with total debt and short-term debt ratios, while exhibiting a negative correlation with long-term debt ratios, though this latter result is not statistically significant Although the data does not permit a detailed analysis of short-term debt, the research by Bevon and Danbolt (2000) is noteworthy, revealing that larger firms tend to have greater access to trade credit and short-term securitized debt, yet smaller firms face constraints in short-term bank borrowing This suggests that smaller firms may lack the necessary creditworthiness for long-term borrowing, aligning with hypothesis 2 (H2).

The analysis reveals that the coefficients of tangibility are statistically significant for both total debt and long-term debt ratios, indicating a positive relationship with these metrics, which aligns with hypothesis 3 (H3) Conversely, tangibility is found to have a negative relationship with the short-term debt ratio These findings corroborate previous research by Bevan and Danbolt (2000), Hutchinson et al (1999), Chittenden et al (1996), and Van der Wijst and Thurik (1993), further supporting 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 shows a positive relationship between income variability and debt, contradicting hypothesis 6 (H6), yet remains statistically significant Lửửf (2003) supports this finding, suggesting it may stem from a period of robust economic recovery and overall increasing revenues Consequently, hypothesis 6 (H6) is rejected in this analysis.

The analysis of growth opportunities and non-debt tax shields reveals insignificant results that contradict hypotheses 4 and 5 (H4 and H5) The regression indicates that both variables positively influence capital structure determinants, affecting the ratios of total debt, short-term debt, and long-term debt Consequently, this study rejects hypotheses H4 and H5.

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

According to Michaelas et al (1999), the regression coefficients for the time dummies illustrate unobserved time-specific effects In Model B of Table 4.5, it is evident that nearly all time dummies are statistically significant, with the base year serving as the reference point.

The analysis indicates a decline in total and long-term debt ratios, as shown in Table 2.1, yet the consistently negative time dummy coefficients, even for short-term debt, remain unclear despite its stability from 2006 to 2010 This decrease in debt ratios may be attributed to the effects of the global financial crisis on the Vietnamese economy, prompting banks to restrict and eliminate prior loans, as evidenced by the negative coefficients across the board.

Results of Model 2

Model 2 aims to analyze the influence of industry dummies on the effects of three financial leverages concerning capital structure determinants This model includes two variations: Model A, which remains consistent with Model 1, and Model B, which incorporates industry dummies The purpose of Model B is to compare its results with Model A, thereby revealing the impact of industry dummies on the overall findings.

Model A Model B Model A Model B Model A Model B

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

Multi-Scope Business and Group Omitted Omitted Omitted

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

Table 4.6 demonstrates that both models yield comparable results for most significant variables; however, the inclusion of industry dummies reduces the significance of income variability in the LDTA test from 99% 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

In Model B, all industries, excluding Multi-Scope Business and Group omitted, exhibit a positive influence on the total debt ratio (TDTA), although these results are not statistically significant However, five industries—Basic Materials, Consumer Discretionary, Consumer Staples, Industrials, and Transportation—show a negative association with the long-term debt ratio (LDTA) Additionally, the oil and gas sector is the only industry that demonstrates a negative value in the short-term debt ratio (SDTA).

Results of Model 3

In our analysis, we investigate whether the determinants of capital structure vary across different firm sizes, categorizing our sample into small, medium, and large firms The findings are detailed in Table 4.7, which illustrates the results for each size category, highlighting the distinct capital structure influences pertinent to small, medium, and large enterprises.

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

Small firms Medium firms Large firms

TDTA LDTA SDTA TDTA LDTA SDTA TDTA LDTA SDTA

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

Notes : ***,**,* significant at 1%, 5% and 10% respectively

Profitability shows a negative correlation with leverage measures across small, medium, and large firms, supporting the pecking-order theory, which suggests that firms prefer using profits for investment financing This negative relationship is particularly pronounced in medium firms' Total Debt to Total Assets (TDTA) and Short-term Debt to Total Assets (SDTA), as well as in large firms' Long-term Debt to Total Assets (LDTA) Notably, the significance of these negative results is evident in both TDTA and SDTA for small and medium firms, while large firms exhibit significance only in LDTA Overall, these findings align with Hypothesis 1 (H1).

Profitability is expected to effect negative on leverage

The analysis reveals that tangibility coefficients are statistically significant for the long-term debt measures across three firm sizes While tangibility positively correlates with the total and long-term debt ratios, it shows a negative relationship with the short-term debt ratio, except in large firms This finding aligns with previous studies by Bevan and Danbolt (2000), Hutchinson et al (1999), Chittenden et al (1996), and Van der Wijst and Thurik (1993), reinforcing the maturity matching principle.

Long-term debt is typically utilized to finance tangible fixed assets, whereas short-term debt is employed for non-fixed assets This supports the hypothesis that asset tangibility positively influences leverage.

The analysis indicates an unexpected positive relationship between growth opportunities and leverage, as the coefficient estimate for growth is insignificant and positive across three firm sizes, with the exception of short-term debt in large firms This suggests that as the economy expands, leverage tends to rise, contradicting hypothesis 4 (H4), which posits that growth opportunities should negatively impact leverage.

The findings indicate that non-debt tax shields do not correlate with the leverage of small firms In medium-sized firms, there is a significant positive relationship with both total and short-term debt ratios, contradicting Hypothesis 5, which posits that non-debt tax shields negatively impact leverage Similarly, large firms show a significant positive relationship with long-term debt ratios.

The income variability coefficient shows significant results only for large firms at LDTA, indicating that hypothesis 6 (H6: Income variability is expected to negatively affect leverage) is not supported.

The determinants of capital structure exhibit notable differences between small and medium-sized enterprises and large firms Collateral plays a crucial role for all firms in securing debt financing, adhering to the maturity matching principle Additionally, firms prioritize internal financing, following the pecking order theory Profitability does not influence short-term debt financing for either small or large firms, nor does it affect long-term debt for small and medium enterprises The tangibility of assets significantly impacts long-term debt across all firm sizes, while growth opportunities remain unaffected by firm size The non-debt tax shield significantly influences total debt, particularly short-term debt for medium firms and long-term debt for large firms Furthermore, income variability affects the long-term debt of large firms.

Robustness tests

Robustness tests are conducted to validate the results, utilizing Model 1 (which is identical to Model 2) and Model 3, focusing specifically on the variable TDTA across three different firm sizes.

Table 4.8 Results of Model 1 : Fixed Effects versus Random Effects

Fixed Effects Random Effects Fixed Effects Random Effects Fixed Effects Random Effects

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

In both Model 1 and Model 2, the choice between Fixed and Random Effects does not impact the study's findings regarding the total debt ratio (TDTA), as profitability, firm size, asset tangibility, and income variability remain significant in both models (refer to Table 4.8) Additionally, in the Fixed Effects model, growth opportunities emerge as a significant variable.

The inclusion of time dummies in Model 1 and industry dummies in Model 2 does not render profitability, firm size, asset tangibility, and income variability insignificant, as demonstrated in Tables 4.5 and 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 choice between Fixed Effects and Random Effects significantly impacts the study, as the variables PROF, TANG, and NDTS remain consistent across both models However, Fixed Effects reveals that GRO and INVAR for medium firms become significant, as illustrated in Table 4.9.

In many instances, the coefficients derived from Fixed Effects models appear inconsistent with economic theory, whereas those produced by Random Effects models align more closely with expected outcomes.

Table 4.9 Results of Model 3: Fixed Effects versus Random Effects

Small firms Medium firms Large firms

FE RE FE RE FE RE

Non-debt tax shield (NDTS)

CONCLUSION

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.

Conclusion

This study analyzes the factors influencing the capital structure of Vietnamese firms listed on the Hochiminh Stock Exchange from 2006 to 2010, using panel data analysis The research employs three leverage measures based on book values: total debt ratio, long-term debt ratio, and short-term debt ratio The findings reveal significant differences in the determinants of these leverage measures, with all three being notably influenced by income variability Additionally, profitability and firm size impact total and short-term debt, while asset tangibility affects total and long-term debt Notably, growth opportunities and non-debt tax shields do not correlate with any of the three debt measures.

Firm profitability negatively influences total debt to total assets (TDTA) and short-term debt to total assets (SDTA), aligning with the pecking order theory proposed by Myers and Majluf (1984), which suggests that firms prioritize internal funds before resorting to debt and equity Conversely, firm size positively affects leverage measures, supporting Rajan and Zingales' (1995) assertion that size is a crucial determinant of corporate capital structure Additionally, firm asset tangibility significantly impacts TDTA and long-term debt to total assets (LDTA), corroborating findings from Titman and Wessels (1988) and others, who argue that tangible assets enhance lenders' willingness to provide loans However, firm growth opportunities and non-debt tax shields do not significantly influence leverage measures, contradicting the views of Myers (1977) and DeAngelo and Masulis (1980), which suggest that high-growth firms typically avoid debt issuance.

In 2002, it was posited that non-debt tax shields serve as substitutes for the tax benefits associated with debt financing, suggesting that the advantages of leverage diminish when other tax deductions, such as depreciation, increase Additionally, firm income variability was found to have a significant positive impact on Total Debt to Total Assets (TDTA) and Short-term Debt to Total Assets (SDTA), while exhibiting a significant negative impact on Long-term Debt to Total Assets (LDTA) This finding contrasts with Loof's 2003 study, which analyzed data from 1991 to 1998—a period marked by strong economic recovery and rising revenues—where the effect of income variability on debt was approximately zero, yet still statistically significant.

This study reveals significant differences between short-term and long-term debt ratios across three cases Notably, firm size shows a positive correlation with both total debt and short-term debt ratios, while exhibiting a negative correlation with long-term debt ratios This trend may be attributed to the high interest rates observed over the past three years, which have discouraged long-term borrowing.

Assets with higher tangibility are positively associated with long-term and total debt, while showing a negative correlation with short-term debt Additionally, while income variability positively impacts both total and short-term debt ratios, it exhibits a negative relationship with the long-term debt ratio.

Future analyses of leverage determinants should consider both long-term and short-term debt ratios, as the latter plays a significant role in total debt, particularly in the context of Vietnam.

Vietnamese firms exhibit a low long-term debt ratio primarily due to their preference for equity financing, especially after going public, as many benefit from favorable stock prices Additionally, the nascent stage of the Vietnamese bond market limits access to alternative financing options, making banks the predominant, if not sole, source of external debt for these companies.

To enhance financing options for Vietnamese firms, it is essential to accelerate the development of the bond market, as many companies currently depend on equity financing and trade credit, which involves mutual debts in the form of accounts payable.

The short-term debt ratio of Vietnamese firms remains unclear due to data limitations that prevent a detailed decomposition of short-term debt into its fundamental components To understand the reasons behind the high short-term debt ratios, it is essential to obtain data on trade credit, short-term securitized debt, and short-term bank borrowing As Bevan and Danbolt (2000) suggest, a comprehensive analysis of capital structure and its determinants necessitates an in-depth examination of all types of corporate debt.

The study reveals that the inclusion of time and industry dummies does not alter the significant relationship between three forms of debt ratios and the determinants of capital structure However, time dummies indicate a decline in total and long-term debt ratios, highlighting the impact of the global financial crisis on the Vietnamese economy Notably, within the industry dummies, only firms in the Industrials sector exhibit a significant relationship with the short-term debt ratio.

Limitations

This study analyzes a sample of 77 non-financial firms listed on HOSE, deliberately excluding financial institutions such as banks, insurance companies, and securities firms Additionally, examining firms not listed on HOSE could provide a more comprehensive understanding of the determinants of capital.

The absence of comprehensive information has led to the oversight of intriguing proxies that could influence capital structure Future research should consider including institutional holdings, which reflect ownership structure, and managerial holdings, represented by the number of shares held by top managers, directors, and supervisors relative to total shares outstanding.

This study serves as an academic exploration of the factors influencing the capital structure of firms listed on the Ho Chi Minh Stock Exchange The findings provide a foundation for researchers and corporate managers to conduct further investigations, aiming for a more accurate understanding of the determinants of capital structure in Vietnamese companies.

Recommendations

To address the limitations identified in this study, we recommend utilizing dynamic panel data regression in future research This approach could uncover significant relationships between short-term and long-term leverage, facilitating important discussions on the interplay between financial systems, corporate debt structures, and economic growth.

This study focuses on the factors influencing the debt structure within firms' capital structures, which comprise both debt and equity It highlights the necessity for further research into the determinants of equity structure Additionally, it addresses the impact of the Vietnamese stock exchange's development on capital mobilization and examines how factors affecting share capital dilution influence the determinants of both debt and equity increases.

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?

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Random-effects GLS regression Number of obs = 385 Group variable: ticker1 Number of groups = 77

R-sq: within = 0.0996 Obs per group: min = 5 between = 0.0635 avg = 5.0 overall = 0.0711 max = 5

Random effects u_i ~ Gaussian Wald chi2(6) = 37.70 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000

- TDTA | Coef Std Err z P>|z| [95% Conf Interval]

- LDTA | Coef Std Err z P>|z| [95% Conf Interval]

- SDTA | Coef Std Err z P>|z| [95% Conf Interval]

-+ - PROF | -.1150134 0434229 -2.65 0.008 -.2001207 -.0299061 SIZE | 1173817 0310533 3.78 0.000 0565183 1782451 TANG | 2553605 052414 4.87 0.000 1526309 3580902 GRO | -.0070466 0062178 -1.13 0.257 -.0192332 0051401 NDTS | -.0356491 08636 -0.41 0.680 -.2049116 1336135 INVAR | 054497 0713739 0.76 0.445 -.0853932 1943872

-+ - PROF | -.0120859 0262635 -0.46 0.645 -.0635615 0393897 SIZE | -.0092481 0165717 -0.56 0.577 -.041728 0232319 TANG | 2908748 0299825 9.70 0.000 2321103 3496393 GRO | -.0030914 0037335 -0.83 0.408 -.0104089 004226 NDTS | -.059985 0486396 -1.23 0.217 -.155317 0353469 INVAR | -.1484895 0421402 -3.52 0.000 -.2310827 -.0658963

-+ - PROF | -.1052109 0411222 -2.56 0.011 -.1858089 -.0246129 SIZE | 1228843 0287312 4.28 0.000 0665723 1791964 TANG | -.026026 0491623 -0.53 0.597 -.1223824 0703304 GRO | -.0048812 0058812 -0.83 0.407 -.0164083 0066458 NDTS | 0631355 0807128 0.78 0.434 -.0950587 2213296 INVAR | 1857873 0673154 2.76 0.006 0538516 317723

-+ - PROF | -.1259082 0451853 -2.79 0.005 -.2144698 -.0373466 SIZE | 1193526 0333239 3.58 0.000 0540389 1846663 TANG | 2609647 0563678 4.63 0.000 1504858 3714436 GRO | 0082605 0051654 1.60 0.110 -.0018635 0183845 NDTS | 0035437 0904121 0.04 0.969 -.1736608 1807482 INVAR | 2119646 0701562 3.02 0.003 0744611 3494682

-+ - PROF | -.0194094 0260379 -0.75 0.456 -.0704428 0316239 SIZE | -.0208721 0177717 -1.17 0.240 -.0557041 0139598 TANG | 2622638 0313794 8.36 0.000 2007613 3237662 GRO | 0015084 0029635 0.51 0.611 -.0042999 0073167 NDTS | -.0563641 0492589 -1.14 0.253 -.1529098 0401816 INVAR | -.1009903 0398718 -2.53 0.011 -.1791375 -.022843

-+ - PROF | -.1111079 0419033 -2.65 0.008 -.1932368 -.0289789 SIZE | 1406313 0302376 4.65 0.000 0813668 1998959 TANG | 0025677 0517902 0.05 0.960 -.0989392 1040745 GRO | 0063388 0047839 1.33 0.185 -.0030376 0157151 NDTS | 0940136 0825212 1.14 0.255 -.0677249 2557522 INVAR | 302825 0648148 4.67 0.000 1757903 4298598

Fixed-effects (within) regression Number of obs = 385 Group variable: ticker1 Number of groups = 77

- TDTA | Coef Std Err t P>|t| [95% Conf Interval]

- LDTA | Coef Std Err t P>|t| [95% Conf Interval]

- SDTA | Coef Std Err t P>|t| [95% Conf Interval]

Ticker Company Industry TDTA LDTA SDTA PROF SIZE TANG GRO NDTS INVAR

1 ABT Ben Tre Aquaproduct Import & Export JSC Consumer Staples 0.3875 0.0079 0.3795 0.3630 11.5203 0.2080 3.4162 0.1501 0.3493

2 AGF An Giang Fisheries Import & Export JSC Consumer Staples 0.3587 0.0030 0.3556 0.2449 12.0779 0.3996 2.2452 0.1367 0.0878

3 BBC Bien Hoa Confectionary Corp Consumer Staples 0.2454 0.0131 0.2323 0.5289 11.5354 0.2660 1.7993 0.4247 0.1692

4 BHS Bien Hoa Sugar JSC Consumer Staples 0.4000 0.2736 0.1263 0.3284 11.8853 0.3921 1.7404 0.2410 0.0697

5 BMC Binh Dinh Minerals JSC Basis Materials 0.1771 0.0000 0.1771 0.5066 10.7223 0.2217 1.4057 0.1451 0.7334

6 BMP Binh Minh Plastics JSC Basis Materials 0.0975 0.0000 0.0975 0.5669 11.7021 0.1071 4.9614 0.3307 0.0983

7 BT6 620 Chau Thoi Concrete JSC Construction & Materials 0.5266 0.0874 0.4392 0.1614 11.6684 0.0907 1.4305 0.1021 0.0675

8 CII Hochiminh City Infrastructure Investment JSC Real Estate 0.6590 0.4489 0.2101 0.4504 11.2217 0.5255 1.9784 0.4009 0.0377

9 CLC Cat Loi JSC Industrials 0.5880 0.0006 0.5874 0.9346 11.8478 0.1429 1.9737 0.8196 0.1439

10 COM Materials - Petrolium JSC Oil/Gas 0.4606 0.0560 0.4046 0.2551 12.2318 0.3537 1.8092 0.1575 0.2805

11 CYC Chang Yih Ceramic JSC Construction & Materials 0.5287 0.0371 0.4915 0.3019 11.1793 0.4494 1.1293 0.3339 0.1819

12 DCT Dong Nai Roofsheet & Construction Matarial JSC Construction & Materials 0.1661 0.0004 0.1656 0.3767 11.2280 0.2233 1.8678 0.3765 0.2141

13 DHA Hoa An Stone Exploitation JSC Construction & Materials 0.0405 0.0081 0.0325 0.2857 10.9242 0.1744 2.4110 0.1301 0.2136

14 DHG Hau Giang Pharmaceutical JSC Consumer Staples 0.6470 0.0441 0.6029 0.2523 11.9411 0.3068 4.7063 0.0720 0.0851

15 DIC DIC Investment & Trading JSC Construction & Materials 0.4767 0.0000 0.4767 0.1183 11.6412 0.1106 1.7107 0.0330 0.4064

16 DMC Domesco Medical Import - Export JSC Consumer Staples 0.4192 0.0102 0.4090 0.2170 11.8208 0.2768 4.3028 0.0749 0.1081

17 DRC Da Nang Rubber JSC Consumer Discretionary 0.7226 0.2628 0.4598 0.7237 11.9689 0.2792 2.4496 0.6148 0.0808

18 DTT Do Thanh Technology Corp Basis Materials 0.1361 0.0010 0.1351 0.2908 10.7073 0.3905 3.4121 0.1286 1.3072

19 FMC Sao Ta Foods JSC Consumer Staples 0.4794 0.0000 0.4794 0.6783 11.9662 0.2363 2.9170 0.5319 0.1887

20 FPT The Corp for Financing & Promoting Technology Information Technology 0.5046 0.0370 0.4676 0.2416 13.3304 0.0879 8.7096 0.0629 0.0121

21 GIL Binh Thanh Trading JSC Consumer Discretionary 0.2334 0.0070 0.2264 0.1952 11.6199 0.1530 1.4799 0.0587 0.2186

22 GMC Saigon Garment Manufacturing Trade JSC Consumer Discretionary 0.6746 0.1363 0.5383 0.4783 11.3494 0.4185 1.7195 0.3787 0.3385

23 GMD General Forwarding & Agency Corp Transportation 0.5294 0.2151 0.3143 0.4306 12.0835 0.4251 3.9250 0.2730 0.0295

24 HAP Hai Phong Paper JSC Basis Materials 0.3480 0.1347 0.2133 0.1653 11.2415 0.5769 1.1367 0.0845 0.1558

25 HAS Ha Noi Post Construction & Installation Co Construction & Materials 0.6729 0.0355 0.6375 0.1313 11.2050 0.0767 1.3470 0.0326 0.2033

26 HAX Hang Xanh Motors Service JSC Transportation 0.5891 0.0091 0.5800 0.1636 11.3690 0.2640 1.7256 0.1151 0.7147

27 HBC Hoa Binh Construction & Real Estate Corp Construction & Materials 0.4740 0.0051 0.4689 0.1550 11.3131 0.2517 4.6245 0.0646 0.3086

28 HMC Hochiminh City Metal Corp Basis Materials 0.5837 0.0025 0.5812 0.0651 12.2428 0.3335 1.8148 0.0147 0.0942

29 HRC Hoa Binh Rubber JSC Basis Materials 0.2128 0.0139 0.1989 0.4774 11.5059 0.2241 4.5193 0.0785 0.1131

30 HTV Ha Tien Transport JSC Transportation 0.1630 0.0018 0.1612 0.4580 10.9383 0.3278 1.4120 0.2864 0.4178

31 IFS Interfood Shareholdings Co Consumer Staples 0.3633 0.0017 0.3616 0.2752 11.8178 0.3657 2.0343 0.1569 0.0658

32 IMP Imexpharm Pharmaceutical JSC Consumer Staples 0.2314 0.0016 0.2298 0.3529 11.7220 0.1151 2.9885 0.1982 0.1349

33 ITA Tan Tao Industrial Park Corp Real Estate 0.6101 0.1212 0.4888 0.1339 11.5653 0.0306 3.3301 0.0078 0.0281

34 KDC Kinh Do Corp Consumer Staples 0.3749 0.0505 0.3244 0.2963 12.0008 0.2753 4.9244 0.1147 0.0439

35 KHA Khanh Hoi Import - Export JSC Consumer Discretionary 0.2467 0.0512 0.1955 0.1895 11.1290 0.1860 1.3727 0.0905 0.2889

36 KHP Khanh Hoa Power JSC Utilities 0.5079 0.2167 0.2912 0.8347 11.8108 0.5799 1.6783 0.7613 0.1096

37 LAF Long An Food Processing & Export JSC Consumer Staples 0.5122 0.0035 0.5087 0.0043 11.8451 0.1447 1.0872 0.0968 0.2870

38 LBM Lam Dong Building Materials JSC Construction & Materials 0.7217 0.1920 0.5297 0.3630 10.8428 0.4097 1.3842 0.3104 0.5912

39 LGC Lu Gia Mechanical Electric JSC Industrials 0.6229 0.0811 0.5418 0.1992 10.6068 0.2681 1.5715 0.1462 0.8742

40 MCP My Chau Printing & Packaging Holdings Co Basis Materials 0.3838 0.0922 0.2916 0.8434 11.1833 0.2800 1.6250 0.7751 0.4475

41 MCV Cavico Vietnam Mining & Contrucstion JSC Industrials 0.8306 0.1658 0.6648 0.2921 11.0792 0.4488 1.5963 0.2477 0.2034

42 MHC Hanoi Maritime Holding Co Transportation 0.4549 0.2018 0.2531 0.6447 11.3500 0.2915 1.5041 0.5515 0.2116

43 NAV Nam Viet JSC Construction & Materials 0.6279 0.0012 0.6268 0.3567 11.2451 0.1678 3.1886 0.1642 0.3759

44 NSC National Seed JSC Consumer Staples 0.3493 0.0137 0.3356 0.1641 11.0333 0.1089 1.9803 0.0693 0.4297

45 PAC Dry Cell & Storage Battery JSC Industrials 0.5368 0.0434 0.4934 0.5923 11.8287 0.2724 2.4634 0.4864 0.1543

46 PGC Gas Petrolimex JSC Utilities 0.4952 0.1171 0.3781 0.2204 12.1059 0.2583 2.1756 0.1599 0.0595

47 PJT Petrolimex JS Tanker Co Oil/Gas 0.5555 0.0013 0.5542 0.1764 11.8935 0.2187 2.0476 0.0860 0.3712

48 PNC Phuong Nam Cultural JSC Consumer Discretionary 0.6066 0.0042 0.6024 0.1844 11.2533 0.1835 1.1781 0.1433 0.2510

49 PVD Petrovietnam Drilling & Well Services JSC Oil/Gas 0.6359 0.3249 0.3110 0.1096 12.1299 0.6857 5.8591 0.0353 0.0189

50 RAL Rang Dong Light Sources & Vacuum Flask JSC Industrials 0.6838 0.1714 0.5124 0.4606 11.7627 0.2461 2.6784 0.3555 0.0933

51 REE Refrigeration & Electrical Engineering Corp

Multi-Scope Business and Group 0.2825 0.0642 0.2182 0.2134 11.9214 0.1196 3.2548 0.0157 0.0272

52 SAM Saigon Cables & Telecommunications Material JSC Industrials 0.5801 0.0175 0.5627 0.2373 12.2190 0.0372 3.7329 0.1121 0.0234

53 SAV Savimex Economic Cooperation and Import-Export JSC Consumer Discretionary 0.4638 0.0203 0.4434 0.1591 11.5808 0.1192 1.3023 0.1029 0.1147

54 SCD Chuong Duong Beverages Co Consumer Staples 0.2643 0.0079 0.2564 0.2895 11.2943 0.1137 3.1761 0.1457 0.2710

55 SFC Saigon Fuel JSC Oil/Gas 0.4444 0.1068 0.3376 0.4099 11.8093 0.3584 1.4746 0.2784 0.6075

56 SFI Sea & Air Freight International Transportation 0.6911 0.0006 0.6904 0.2101 10.9494 0.1451 1.5772 0.0674 0.2908

57 SJD Can Don Hydroelectric JSC Utilities 0.7889 0.6493 0.1396 0.1735 11.3775 0.9334 1.4421 0.1250 0.0318

Song Da Urban & Industrial Zone Invesment & Development

59 SMC SMC Trading Investment JSC Basis Materials 0.7433 0.0200 0.7232 0.0616 12.2810 0.0192 1.3024 0.0045 0.1035

60 SSC Southern Seed JSC Consumer Staples 0.1710 0.0266 0.1444 0.2745 11.1529 0.3202 3.2253 0.1045 0.2906

61 TAC Tuong An Vegetable Oil JSC Consumer Staples 0.5175 0.0583 0.4593 0.2923 12.1808 0.2782 2.7713 0.2088 0.0751

62 TCR Taicera Enterprise Co Construction & Materials 0.5088 0.2380 0.2708 0.3603 11.8328 0.4746 1.5086 0.2953 0.0392

63 TDH Thu Duc Housing Development Corp Construction & Materials 0.3582 0.1961 0.1620 0.1214 11.1614 0.0353 4.0107 0.0135 0.0478

64 TMS Transforwarding & Warehousing JSC Transportation 0.3835 0.0893 0.2942 0.4495 11.1080 0.6522 2.3666 0.3034 0.2794

65 TNA Thien Nam Import & Export JSC Consumer Discretionary 0.7090 0.0126 0.6964 0.1209 11.3837 0.0452 1.4225 0.0233 0.5711

66 TRI Saigon Food & Beverage JSC Consumer Staples 0.7043 0.0111 0.6933 0.3617 11.4803 0.1158 1.5394 0.3049 0.1945

67 TS4 Seafood Import Export JSC No 4 Consumer Staples 0.4317 0.0894 0.3422 0.1965 11.1816 0.2736 1.4402 0.1223 0.4386

68 TTP Tan Tien Plastic Packaging JSC Basis Materials 0.4353 0.0651 0.3702 0.8092 11.8844 0.1931 3.1595 0.6343 0.1297

69 TYA Taya Vietnam Electric Wire & Cable JSC Industrials 0.7308 0.0447 0.6861 0.1407 12.1100 0.2092 1.7608 0.1119 0.0354

70 VFC Vinafico JS Corp Transportation 0.7516 0.1304 0.6212 0.2803 11.5359 0.4042 1.3191 0.2192 0.1418

71 VID Vien Dong Paper JSC Basis Materials 0.5101 0.0696 0.4404 0.2120 11.4245 0.2936 3.9949 0.1557 0.1861

72 VIP Vietnam Petrolium Transport JSC Oil/Gas 0.3404 0.2972 0.0432 0.6472 11.9256 0.4329 3.2391 0.4983 0.0595

73 VIS Vietnam - Italy Steel JSC Basis Materials 0.8507 0.2090 0.6417 0.1403 12.1052 0.2742 1.3621 0.1191 0.0528

74 VNM Vietnam Dairy Corp Consumer Staples 0.2395 0.0299 0.2095 0.4171 12.7986 0.2977 5.7595 0.2330 0.0114

75 VPK Vegetable Oil Packing JSC Basis Materials 0.4190 0.1926 0.2264 0.1188 11.0233 0.5486 1.6485 0.0587 0.2660

76 VSH Vinh Son Song Hinh Hydropower JSC Utilities 0.2900 0.2232 0.0668 0.7681 11.6120 0.9086 3.5808 0.6247 0.0218

77 VTB Viettronics Tan Binh JSC Industrials 0.5567 0.0681 0.4886 0.2562 11.5971 0.0249 2.2941 0.1510 0.1981

53 SAV Savimex Economic Cooperation and Imp.-Exp JSC Consumer Discretionary 0.4625 0.0122 0.4503 0.1174 11.6642 0.1068 1.4027 0.0773 0.0768

58 SJS Song Da Urban & Industrial Zone Inv & Dev JSC Real Estate 0.1786 0.0207 0.1579 0.2547 11.8501 0.5158 6.6417 0.0225 0.0266

31 IFS Interfood Shareholdings Co Consumer Staples 0.8419 0.0243 0.8176 (0.0383) 11.9624 0.7525 1.0230 0.1420 0.0332

Multi-Scope Business and Group 0.1951 0.0404 0.1547 (0.0462) 12.0698 0.0174 0.8414 0.0081 0.0158

66 TRI Saigon Food & Beverage JSC Consumer Staples 0.9894 0.2467 0.7427 (0.4138) 11.7790 0.2190 1.1678 0.0312 0.1261

66 TRI Saigon Food & Beverage JSC Consumer Staples 0.8222 0.1166 0.7056 (0.1825) 11.7696 0.1715 1.4964 0.0443 0.1082

42 MHC Hanoi Maritime Holding Co Transportation 0.6199 0.2125 0.4074 (0.0119) 11.1162 0.2412 1.0393 0.1564 0.1589

Tiêu đề	Determinants of Capital Structure Evidence from Listed Companies on Hochiminh Stock Exchange
Tác giả	VĂNG NGUYỄN PHƯƠNG THẢO
Người hướng dẫn	ASSOC. PROF. – DR. PHẠM VĂN NĂNG
Trường học	University of Economics Ho Chi Minh City
Chuyên ngành	Banking and Finance
Thể loại	Master Thesis
Năm xuất bản	2011
Thành phố	Ho Chi Minh City

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