(LUẬN văn THẠC sĩ) determinants of capital structure evidence from listed companies on hochiminh stock exchange

INTRODUCTION

Introduction

Selecting an appropriate capital structure is a significant challenge for businesses, as it directly influences their 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 the key deviations from Modigliani and Miller's assumptions, highlighting the significance of capital structure in determining a firm's value Nevertheless, the empirical implications of these various theories remain largely unexplored.

Numerous capital structure theories have been proposed, yet their empirical relevance remains largely unexplored, particularly in developing countries While existing research predominantly focuses on G7 nations, studies like those by Booth et al (2001) and Huang and Song (2002) have begun to address this gap However, there is a notable absence of research concerning Vietnam, specifically the Hochiminh Stock Exchange (HOSE) This thesis aims to investigate the capital structure of listed firms in HOSE, contributing valuable insights to the existing literature.

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 non-financial firms listed on the Hochiminh Stock Exchange in Vietnam, aiming to empirically analyze the relationship between various capital structure determinants and debt measures The study covers the period from 2006 to 2010, providing valuable insights into the financial dynamics of these companies.

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 through 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 impact of these factors on financial leverage may evolve over time, reflecting shifts in market conditions and corporate 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, highlighting key determinants of capital structure and variables that explain why firms opt for debt financing The aim of this review is to establish a foundation for the research hypotheses.

Theoretical and Empirical Findings

According to Myers (2001), there is no universal theory governing the debt-equity choice, leading to the understanding that various conditional theories can provide insights into a firm's debt-to-equity structure These theories are categorized into two main groups: static trade-off models, which predict an optimal debt-equity ratio for each firm, and the pecking-order hypothesis, which suggests that there is no clearly defined target capital structure.

Static trade-off models define optimal capital structure as a balance between benefits and costs, such as the tax shield versus financial distress costs in trade-off theory This theory posits that the ideal capital structure is reached when the marginal present value of the tax shield from additional debt equals the marginal present value of financial distress costs Signaling theory suggests that a company's debt ratio serves as a signal of its quality, with successful companies maintaining a high enough debt level to deter imitation by less capable firms Agency theory emphasizes minimizing agency costs to achieve optimal capital structure, while free cash-flow theory argues that a high debt ratio compels managers to distribute excess cash rather than invest it inefficiently, particularly in firms with significant free cash flows.

The pecking-order theory posits that firms do not have a fixed optimal capital structure; instead, they prefer using 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 This creates a hierarchy of financing sources, with internal equity at the top and external equity at the bottom, resulting in a lack of clearly defined optimal leverage.

Most existing empirical evidence primarily stems from developed countries, with notable studies such as Bradley et al (1984), Kim and Sorensen (1986), and Rajan and Zingales (1995) focusing on U.S companies and G7 nations In contrast, research on developing countries has only emerged in recent years, highlighted by works like Booth et al (2001) and Huang and Song (2002).

Research on Vietnam's economic landscape has been limited, with notable studies including San (2002), which examined the tourism industry in Thua Thien Hue Province, and Nguyen and Ramachandran (2006), which focused on small and medium-sized enterprises (SMEs) In contrast, Vu (2003) analyzed companies listed on the Ho Chi Minh City stock exchange While listed companies are fewer in number compared to unlisted SMEs, they significantly contribute to the economy, as the small business sector represents only about 25% of the country's GDP.

This study aims to enhance the analysis conducted by Vu (2003) by examining the factors influencing leverage in companies listed on the Hochiminh Stock Exchange from 2006 to 2010.

Potential determinants of capital structure

Based on the theories discussed, we will identify key variables that explain why firms prefer debt over equity financing According to Harris and Raviv (1991), the factors influencing capital structure decisions are numerous However, this paper will focus on the most widely recognized explanatory variables.

The determinants of capital structure include various factors that influence leverage, as highlighted by Harris and Raviv (1991) and Titman and Wessels (1988) Key elements that increase leverage are fixed assets, non-debt tax shields, investment opportunities, and firm size, while factors like volatility, advertising expenditure, bankruptcy probability, profitability, and product uniqueness tend to decrease it Additionally, asset structure, growth, industry classification, and earnings volatility also play significant roles Despite a general consensus on these factors, there remains considerable debate among researchers regarding the inclusion of other potential determinants of capital structure.

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, introduced by Myers and Majluf (1984), posits that firms prioritize internal financing over external options when funding their activities When external financing is necessary, companies typically opt for debt first, followed by hybrid securities like convertible bonds, and consider equity only as a last resort (Brealey and Myers, 1991) This preference may stem from the costs associated with issuing new equity, influenced by asymmetric information and transaction costs The impact of profitability on leverage presents conflicting theories; while Myers and Majluf (1984) suggest a negative correlation, Jensen (1986) argues for a positive relationship under effective corporate control markets, though this shifts if the market is ineffective.

This paper hypothesizes a negative correlation between profitability and leverage, suggesting that firms with higher profits tend to exhibit lower leverage Specifically, the study aims to test the premise that leverage is inversely related to profitability, supporting the notion that more profitable companies maintain reduced levels of debt.

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, with larger firms potentially facing lower bankruptcy risks due to diversification, which could lead to a positive correlation between size and leverage However, Fama and Jensen (1983) suggest that larger firms provide more information to investors, reducing asymmetric information and increasing their preference for equity over debt (Rajan and Zingales, 1995) Therefore, the hypothesis posits that leverage may be either positively or negatively associated with firm size.

In this study, the natural logarithm of sales is utilized as a proxy for company size, a common approach in similar research Alternatively, the natural logarithm of total assets could serve as a proxy; however, since both measures are highly correlated (with coefficients ranging from 0.63 to 0.71 between 2006 and 2010), either could effectively represent company size This study opts for sales over total assets to mitigate the risk of spurious correlation.

Tangible assets are considered valuable 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 effectively, revealing less about future profits This suggests a positive relationship between asset tangibility and leverage, supported by empirical studies from Rajan and Zingales (1995), Friend and Lang (1988), and Titman and Wessels (1988) Therefore, 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) demonstrate that management's pursuit of growth aligns with shareholder interests in firms with strong investment prospects Conversely, for firms with limited investment opportunities, debt can help mitigate agency costs associated with managerial discretion, as supported by Jensen (1986) and Stulz (1990) Berger, Ofek, and Yermack (1997) further affirm the disciplinary function of debt However, Myers (1977) highlights that high-growth firms may possess more real options for future investments than their low-growth counterparts Consequently, if high-growth firms require additional equity financing to capitalize on these options, existing debt may hinder such investments, as it would transfer wealth from shareholders to debtholders Thus, firms with significant growth opportunities are likely to avoid issuing debt, reinforcing the expectation of a negative correlation between leverage and growth opportunities.

In 1995, it was posited that growth necessitates substantial equity financing and minimal leverage This study aims to examine the hypothesis that leverage is inversely related to 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 validate theoretical predictions regarding growth opportunities, with Kester (1986) being a notable exception Various proxies are utilized to assess growth opportunities, such as Wald's (1999) use of a 5-year average of sales growth, and Titman and Wessels' (1988) approach of scaling capital investment and research and development by total assets and sales, respectively Rajan and Zingales (1995) employ Tobin’s Q, while Booth et al (2001) use the market-to-book ratio of equity This study posits that while sales growth reflects past performance, Tobin’s Q is a more accurate indicator of future growth potential, leading to its selection as the primary measure of growth opportunities.

Equity market value + Total liabilities GRO Total assets

2.3.5 Non-debt tax shield (NDTS)

Modigliani and Miller (1958) highlight that interest tax shields incentivize firms to increase leverage; however, non-debt-related corporate tax shields, such as depreciation deductions and investment tax credits, can also influence leverage DeAngelo and Masulis (1980) suggest 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.

In their 1988 study, Titman and Wessels identified the ratio of tax credits to total assets and the ratio of depreciation to total assets as indicators of non-debt tax shields This thesis utilizes only depreciation data; thus, the ratio of depreciation to total assets will be employed as the sole measure of the non-debt tax shield.

Income variability serves as a crucial indicator of business risk, with greater fluctuations in earnings correlating to an increased likelihood of bankruptcy Consequently, companies exhibiting higher income variability tend to maintain lower leverage levels This leads to the hypothesis that there is a negative relationship between leverage and 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

In certain regression models, a comprehensive set of time dummies will be included, with each year represented except for the base year of 2006 This approach allows for an examination of potential shifts in leverage over time while controlling for other observable factors The time dummies will capture the unobserved effects specific to each year, facilitating a clearer understanding of how leverage may change over the years (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 assert 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 types of 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 that incorporates debt is classified as leveraged There are two key types of leverage: 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 Total leverage encompasses both fixed operating costs and debt costs, indicating that a firm's overall risk is the sum of its business and financial risks In this analysis of capital structure determinants, we focus specifically on financial leverage, also known as gearing.

This study focuses on the capital structure, or financial leverage, of firms as its dependent variable Since the influential 1958 paper by Modigliani and Miller, numerous articles have explored the determinants of capital structure Various measures of capital structure exist, each with different methodologies for assessment Generally, these measures can be categorized into two main types: those based on market value of equity and those based on book value of equity (Lửửf, 2003) For example, research by Titman and Wessels further elaborates on these distinctions.

In their 1988 study on capital structure choice, six measures of financial leverage are analyzed, including long-term, short-term, and convertible debt relative to both market and book values of equity However, due to data limitations, most empirical studies have primarily utilized leverage measures based on book values rather than market values Consequently, this study will also focus on measuring leverage using book values, as sufficient market data is not available.

The lack of market data in empirical studies of capital structure choice poses significant challenges, yet both book and market values are utilized together when available, as they provide different informative insights (Lửửf, 2003) Research by Titman and Wessels (1988), referencing Bowman (1980), indicates a strong cross-sectional correlation between book and market values of debt Brealey and Myers (2003) contend that relying solely on book values may not significantly impact the analysis, as market values often incorporate intangible assets from research, development, and advertising, which are difficult to liquidate and may vanish if a company fails Therefore, the potential misspecification from using only book value measures could be minimal or negligible.

Selecting an appropriate leverage measure as a dependent variable is crucial, regardless of market or book value According to Rajan and Zingales (1995), the choice of the most relevant measure hinges on the analysis's objective They suggest that the impact of past financing decisions is most accurately reflected by the ratio of total debt to capital, where capital is defined as the sum of total debt and equity.

When analyzing various leverage measures, it is essential to recognize the complexities involved in accurately measuring leverage and the relevant explanatory variables, as highlighted by Harris and Raviv (1991) Different empirical studies adopt varying definitions of leverage, including factors such as accounts payable, accounts receivable, cash, and short-term debt For instance, some studies calculate leverage as the ratio of book value of debt to book value of equity, while others use the market value of equity or a combination of both Additionally, researchers must navigate common challenges associated with interpreting statistical results, which can further complicate the analysis.

In this study, we will focus on the leverage measures, specifically analyzing the ratio of total debt to total assets, which is crucial for understanding financial stability and risk.

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

Examining the sources of debt is crucial for understanding the financial structure of Vietnamese firms, particularly since the available data only allows for a breakdown of total liabilities into short-term and long-term debt, equating total liabilities with total debt It would be beneficial to have detailed information on the components of short-term and long-term debt, such as the size of trade credit, which is a significant part of short-term debt Research by Bevan and Danbolt (2000) highlights that there are notable differences in the determinants of short-term and long-term debt, with short-term debts, including trade credit, accounting for over 62% of total debt in UK companies Their findings suggest that the analysis of corporate structure is incomplete without a thorough examination of corporate debt, emphasizing the need for a detailed understanding of debt components.

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 impact 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 among SMEs Analyzing the time dummies from 1988 to 1995 in relation to changes in real GDP further underscores these findings.

A study by Michaelas et al reveals that short-term debt ratios in small and medium-sized enterprises (SMEs) are negatively correlated with fluctuations in economic growth In contrast, long-term debt ratios show a positive relationship with changes in economic growth, indicating that SMEs' financial structures can significantly impact their performance in varying economic conditions.

This analysis examines the factors influencing corporate debt by establishing two leverage measures for short-term and long-term debt ratios, as detailed in Table 2.1 Notably, the average short-term debt ratio is significantly higher, approximately four times that of long-term debt Additionally, there is a marked decline in both the mean and median values of short-term leverage from 2006 to 2008, whereas the long-term debt ratios remain stable without a discernible 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 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 forthcoming econometric analysis.

Trade credit likely constitutes a significant portion of short-term debt, reflecting a rational corporate debt policy According to Bevan and Danbolt (2000), this reliance on trade credit may arise because alternative borrowing methods incur higher costs.

The analysis of short-term and long-term debt ratios across different firm sizes reveals significant trends Short-term debt constitutes a large portion of total debt, prompting an examination of how these ratios vary among small, medium, and large firms Firm size can be defined through various metrics, including total assets, with small firms having assets under 500 billion VND, medium firms between 501 and 5,000 billion VND, and large firms exceeding 5,000 billion VND Notably, data from 2006 to 2010 shows a marked decrease in short-term debt for both small and large firms, while medium-sized firms experienced a slight decline in 2007, followed by growth until 2010 These findings underscore the importance of firm size in understanding debt dynamics within corporate finance.

Table 2.2 Short-term debt ratios and firm sizes

Short-term debt ratios and firm sizes

Year Small firms Medium firms Large firms

Table 2.3 indicates that long-term debt ratios have decreased for medium-sized firms, as evidenced by both mean and median values In contrast, these ratios have remained stable for small firms and have increased for large firms.

Table 2.3 Long-term debt ratios and firm sizes

Long-term debt ratios and firm sizes

Year Small firms Medium firms Large firms

Conclusion

The capital structure of firms in emerging markets, such as Vietnam, exhibits distinct characteristics and efficiency compared to developed markets A review of existing literature highlights various factors influencing capital structure determinants and financial leverage, which differ significantly across countries This study focuses on the Hochiminh Stock Exchange to identify which theoretical frameworks and empirical studies are applicable to Vietnamese firms, as the specific dynamics of capital structure in this context remain unclear.

RESEARCH METHODOLOGY

Introduction

This chapter outlines the research data collection methods and the applied research hypothesis and methodology, aligning with the objectives set forth in Chapter 1 and the literature review discussed 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 dataset includes comprehensive information on balance sheets, income statements, and depreciation, all of which are legally required to be disclosed by firms The data was sourced from cafef.vn and www.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 entities such as banks and securities companies, as their operations are heavily regulated, following the methodologies of Fama and French (2001) and DeAngelo et al (2006) Additionally, only firms with complete financial data and uninterrupted trading sections due to regulatory compliance were included in the sample.

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

This study analyzes various explanatory variables such as profitability (PROF), firm size (SIZE), tangibility (TANG), growth (GRO), non-debt tax shields (NDTS), and investment activity (INVAR), along with dependent variables including total debt to total assets (TDTA), short-term debt to total assets (SDTA), and long-term debt to total assets (LDTA) The data was derived from the audited annual financial statements of 77 firms covering the 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

The study examines eight key explanatory variables: profitability (PROF), firm size (SIZE), asset tangibility (TANG), growth opportunities (GRO), non-debt tax shields (NDTS), income variability (INVAR), as well as time and industry dummies, which were previously defined and formulated.

This research focuses on 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 To validate our findings, we also conduct robustness tests, ensuring the reliability of the results obtained.

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%

From 2006 to 2010, a study of 77 non-financial firms listed on the Ho Chi Minh Stock Exchange revealed significant insights into their financial performance The average total debt ratio (TDTA) stood at 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 mean firm size was recorded at 11.74, reflecting a conservative approach to asset investment among listed firms Notably, the average growth rate of these non-financial firms reached an impressive 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 excel in profitability, income variability, and short-term debt ratio In contrast, medium-sized firms show the highest mean in tangibility, non-debt tax shield, and total debt ratio Large firms, as expected, lead in overall size and growth opportunities, while also having the highest long-term debt ratio.

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 reveals significant correlations between dependent and independent variables Asset tangibility shows a positive correlation with the total debt ratio, aligning with expectations, as fixed assets serve as collateral, leading to increased debt levels This positive relationship is also observed with long-term debt, while asset tangibility is negatively correlated with short-term debt Interestingly, the non-debt tax shield correlates positively with the total debt ratio, contrary to expectations Profitability exhibits an inverse relationship with the total debt ratio, supporting the Pecking Order theory, which suggests that profitable firms favor internal financing Additionally, firm size is positively associated with the total debt ratio, indicating that as firms grow, their debt levels rise, particularly in short-term debt, while long-term debt decreases Lastly, growth negatively correlates with all leverage measures, and 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 highlights that at the 99% confidence level, the statistically significant variables affecting TDTA include profitability, firm size, asset tangibility, and income variability Conversely, growth opportunities and non-debt tax shields 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 relationship between profitability and the total debt ratio (TDTA), with similar negative associations observed in both long-term debt to total assets (LDTA) and short-term debt to total assets (SDTA) This supports hypothesis 1 (H1), demonstrating that profitability is inversely correlated with all three measures of leverage These results align with the pecking-order theory, suggesting that firms prefer to utilize internally generated funds from profits for financing investments rather than relying on external funding sources, regardless of whether the assets being financed are 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 company size significantly influences leverage, showing a positive relationship with total debt and short-term debt ratios, while exhibiting a negative correlation with long-term debt ratios, although this latter result is not statistically significant Notably, research by Bevon and Danbolt (2000) suggests 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 due to inadequate credit ratings, which aligns with hypothesis 2 (H2).

The analysis reveals that tangibility coefficients are significantly correlated with total debt and long-term debt ratios, indicating a positive relationship as anticipated, in line with hypothesis 3 (H3) Conversely, tangibility exhibits a negative correlation with the short-term debt ratio 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, which posits that long-term debt is utilized to finance fixed (tangible) assets, whereas short-term debt is employed for non-fixed assets.

Table 4.5 indicates a positive relationship between income variability and debt, contradicting hypothesis 6 (H6), yet remains statistically significant Lửửf (2003) supports these findings, suggesting that the observed effects may stem from a period of robust economic recovery and rising revenues Consequently, hypothesis 6 (H6) is rejected in this analysis.

The study reveals that growth opportunities and the non-debt tax shield have an insignificant and contrary impact on hypotheses H4 and H5 Regression analysis indicates that these variables positively influence the determinants of capital structure, affecting both total debt ratios and short-term and long-term debt Consequently, hypotheses H4 and H5 are rejected based on these findings.

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 of the time dummies indicate 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 a reference point.

The analysis indicates a decline in both total and long-term debt ratios, as shown in Table 2.1, yet the consistently negative time dummy coefficients raise questions, particularly regarding short-term debt, which remained stable from 2006 to 2010 This overall decrease in debt ratios may be attributed to the repercussions of the global financial crisis on the Vietnamese economy, prompting banks to restrict and withdraw previous loans, as evidenced by the negative coefficients observed.

Results of Model 2

Model 2 aims to analyze the influence of industry dummies on the relationship between three financial leverages and the determinants of capital structure It includes two models: Model A, which replicates the structure of Model 1, and Model B, which incorporates industry dummies By comparing the results of Model A and Model B, the study seeks to identify 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 illustrates that both models yield comparable outcomes for most significant variables; however, the inclusion of industry dummies reduces the significance of income variability in the LDTA test from 99% to 95% Additionally, the asset tangibility variable in the SDTA test turns positive in Model B, which incorporates industry dummies, yet this result remains insignificant, similar to Model A.

In examining Model B, it is observed that all industries, except for Multi-Scope Business and Group omitted, positively influence the total debt ratio (TDTA), although the results are not statistically significant Conversely, 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 exhibits a negative value in the short-term debt ratio (SDTA).

Results of Model 3

This analysis investigates whether the factors influencing capital structure vary by firm size, categorizing the sample into small, medium, and large firms The findings are summarized in Table 4.7, which details the results for each category of firm size.

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 exhibits a negative correlation with all three leverage measures across small, medium, and large firms, aligning with the pecking-order theory that suggests firms prefer using profits to finance investments This negative relationship is particularly pronounced in the Total Debt to Total Assets (TDTA) and Short-term Debt to Total Assets (SDTA) ratios of medium-sized firms, as well as the Long-term Debt to Total Assets (LDTA) ratio of large firms Notably, the results are significant for TDTA and SDTA in small and medium firms, while the significance for large firms is observed in the LDTA measure Overall, these findings support hypothesis 1 (H1), indicating that profitability negatively impacts leverage.

The analysis reveals that tangibility coefficients are statistically significant for long-term debt across three firm sizes While tangibility positively correlates with total and long-term debt ratios, it shows a negative relationship with short-term debt ratios, 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), supporting the maturity matching principle, which posits that long-term debt finances fixed (tangible) assets, while short-term debt is used for non-fixed assets This outcome corroborates hypothesis 3 (H3), indicating that asset tangibility positively influences leverage.

The analysis reveals an unexpected positive relationship between growth opportunities and leverage, as indicated by the insignificant coefficient estimates across three firm sizes, with the exception of large firms' short-term debt This suggests that, contrary to Hypothesis 4 (H4), which posits a negative effect of growth opportunities on leverage, an expanding economy correlates with increased leverage.

The findings indicate that non-debt tax shields do not correlate with the leverage of small firms In medium-sized firms, a significant positive relationship is observed with total debt and short-term debt ratios, contradicting hypothesis 5, which posits that non-debt tax shields should negatively impact leverage Similarly, large firms show a significant positive relationship with long-term debt ratios, further challenging the expected negative effect of non-debt tax shields on leverage.

The income variability coefficient shows a significant value only for large firms at LDTA, indicating that hypothesis 6 (H6: Income variability is expected to negatively affect leverage) is inconsistent.

The determinants of capital structure vary between small, medium, and large enterprises, with collateral being essential for all firms to secure debt financing while adhering to the maturity matching principle Companies generally prefer internal financing first, following the pecking order theory Interestingly, 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-sized enterprises The tangibility of assets significantly impacts long-term debt across all firm sizes, while growth opportunities remain unaffected by firm size Additionally, the non-debt tax shield significantly influences total debt, particularly short-term debt for medium-sized firms and long-term debt for large firms, while income variability affects the long-term debt of large firms.

Robustness tests

Robustness tests are conducted to validate the results, utilizing Model 1 and Model 3, where Model 2 mirrors Model 1 In Model 3, we specifically examine 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, as profitability, firm size, asset tangibility, and income variability remain significant (refer to Table 4.8) Notably, in the Fixed Effects model, growth opportunities also emerge as significant 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 (see Tables 4.5 and 4.6) Therefore, Model 1 is deemed reliable with the four significant explanatory variables: profitability (PROF), firm size (SIZE), asset tangibility (TANG), and income variability (INVAR).

In Model 3, the choice between Fixed Effects and Random Effects significantly impacts the study, as the variables PROF, TANG, and NDTS remain unchanged across both methods However, Fixed Effects reveals that GRO and INVAR for medium firms become significant, as indicated in Table 4.9 Despite this, the coefficients derived from Fixed Effects often appear incorrect from an economic standpoint, while those generated by Random Effects align more accurately with economic principles.

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 capital structure determinants of Vietnamese firms listed on the Hochiminh Stock Exchange from 2006 to 2010 using panel data analysis It 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 among 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 However, growth opportunities and non-debt tax shields show no correlation with any of the debt measures.

Firm profitability has a significant negative effect on Total Debt to Total Assets (TDTA) and Short-term Debt to Total Assets (SDTA), supporting the pecking order theory proposed by Myers and Majluf (1984), which posits that firms prioritize internal funds before resorting to debt and equity Conversely, firm size positively influences TDTA and SDTA, aligning with Rajan and Zingales (1995), highlighting size as a crucial factor in corporate capital structure Additionally, firm asset tangibility shows a positive and significant relationship with TDTA and Long-term Debt to Total Assets (LDTA), corroborating findings from Titman and Wessels (1988) and others, suggesting that higher tangible assets increase lender confidence and leverage However, firm growth opportunities do not significantly affect leverage measures, contradicting the views of Myers (1977) and Berens and Cuny (1995), who argue that firms with high growth potential are less likely to incur debt Similarly, the non-debt tax shield does not significantly impact leverage measures, which is inconsistent with the findings of DeAngelo and Masulis (1980).

In their 2002 study, it was argued that non-debt tax shields serve as substitutes for the tax benefits associated with debt financing, suggesting that the tax 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) These findings contrast with Loof's 2003 research, which analyzed data from 1991 to 1998—a period marked by strong economic recovery and rising revenues—concluding that the effect of income variability on debt was approximately zero, albeit still statistically significant.

The study reveals significant differences between short-term and long-term debt ratios, highlighting that firm size positively influences both total and short-term debt ratios, while negatively affecting long-term debt ratios This trend may be attributed to high interest rates over the past three years, which have discouraged long-term borrowing Additionally, asset tangibility shows a positive relationship with long-term and total debt, but a negative correlation with short-term debt Lastly, while income variability positively impacts total and short-term debt ratios, it negatively correlates with long-term debt ratios.

Future analyses of leverage determinants should consider both short-term and long-term debt ratios, particularly in the context of Vietnam, where short-term debt makes up a significant portion of total debt Vietnamese firms tend to maintain low long-term debt ratios, possibly due to their preference for equity financing after going public, supported by favorable stock prices Additionally, the underdeveloped state of the Vietnamese bond market means that banks remain the primary, if not sole, source of external debt for these firms.

To enhance financing opportunities for Vietnamese companies, it is essential for Vietnam to expedite the growth of its bond market, as businesses 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 challenging to analyze due to data limitations, preventing a breakdown of short-term debt into its fundamental components Comprehensive insights into why these firms maintain high short-term debt ratios can only be achieved with data on trade credit, short-term securitized debt, and short-term bank borrowing Bevan and Danbolt (2000) emphasize that a thorough understanding of capital structure and its determinants necessitates an in-depth examination of all types of corporate debt.

The study's findings reveal that both time and industry dummies do not alter the significant relationship between three forms of debt ratios and the determinants of capital structure However, time dummies indicate a decrease in total and long-term debt ratios, highlighting the impact of the global financial crisis on the Vietnamese economy In contrast, only firms within the Industrials sector show a significant relationship with the short-term debt ratio.

Limitations

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

Many potential determinants of capital structure remain unexplored due to insufficient information Future research should consider institutional holdings, which reflect ownership structure, and managerial holdings, measured by the shares held by top managers, directors, and supervisors relative to total shares outstanding, as important proxies for analysis.

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 further research by scholars and corporate managers, enabling a more precise evaluation of the determinants affecting the capital structure of Vietnamese companies.

Recommendations

Given the study's limitations, 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 influence of various determinants on firms' debt structure within their capital structure, which comprises both debt and equity It highlights the necessity to explore additional factors affecting equity structure, such as the impact of the Vietnamese stock exchange's development on capital mobilization and the dilution effects of increasing share capital on debt and equity determinants.

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?

1 Abor J 2005, ‘The Effect of Capital Structure on Profitability: Empirical Analysis of Listed Firms in Ghana’, Journal of Risk Finance, Vol 6(5), pp

2 Acs, A 1992, ‘Small business economics: A global perspective’, Challenge 35(6), pp 38-44

3 Ang, J 1991, ‘Small business uniqueness and the theory of financial management’, Journal of Small Business Finance 1, pp 1-13

4 Antoniou, A., Guney, Y & Paudyal, K 2006, ‘The determinants of debt maturity structure: Evidence from France, Germany and the UK’, European Financial Management 12(2), pp 161-194

5 Antoniou, A., Guney, Y & Paudyal, K 2008, ‘The determinants of capital structure: Capital market-oriented versus bank-oriented institutions’, Journal of Financial and Quantitative Analysis 43(1), pp 59-92

6 Asteriou, D & Hall, S 2007, Applied econometrics: A modern approach using Eviews and Microfit, Basingstoke, Palgrave Macmillan

7 Ayyagari, M., Beck, T & Demirguc-Kunt, A 2005, ‘Small and medium enterprises across the globe: A new database’, World Bank Policy Research Working Paper 3127

8 Baltagi, B 2008, Econometric analysis of panel data, John Wiley & Sons, 4th ed

9 Beck, T., Demiguc-Kunt, A & Maksimovic, V 2002, Financing patterns around the world: The role of institutions, SSRN

10 Beck, T., Demiguc-Kunt, A & Maksimovic, V 2008, ‘Financing patterns around the world: Are small firms different?’, Journal of Financial Economics

11 Berger, A & Udell, G 1995, ‘Relationship lending and lines of credit in small firm finance’, Journal of Business, pp 351-381

12 Bevan, A A And Danbolt, J 2000, ‘Capital structure and its determinants in the United Kingdom: A decompositional analysis’, Working Paper, Dept of

Accounting and Finance, University of Glasgow

13 Booth, L, Aivazian, V., Demirguc-Kunt, A & Maksimovic, V 2001, ‘Capital structures in developing countries’, The Journal of Finance 56, pp 87-130

14 Bowman, J 1980, ‘The Importance of a Market Value Measurement of Debt in Assessing Leverage’, Journal of Accounting Research 18, pp 242-54

15 Brealey, R.A and Myers, S.C 2003, Principles of Corporate Finance, 7th ed., McGraw Hill

16 Brush, C & Chaganti, R 1998, ‘Business without glamour? An analysis of resources on performance by size and age in small service and retail firms’,

Journal of Business Venturing 14, pp 233-257

17 Cressy, R & Olofsson, C 1997, ‘European SME financing: An overview’,

18 Demirguc-Kunt, A & Maksimovic, V 1996, ‘Stock market development and financing choices of firms’, World Bank Economic Review, pp 341-371

19 Demirguc-Kunt, A & Maksimovic, V 1999, ‘Institutions, financial markets and firm debt maturity’, Journal of Financial Economics 54, pp 295-336

20 Fan, J., Titman, S & Twite, G 2006, ‘An international comparison of capital structure and debt maturity choices’, under third round review at the Journal of Finance

21 Frank, M and V Goyal 2003, ‘Testing the Pecking Order Theory of Capital Structure’, Journal of Financial Economics 67, pp 217–48

22 Gianetti, M 2003, ‘Do better institutions mitigate agency problems? Evidence from corporate finance choices’, Journal of Financial and Quantitative Analysis 38, pp 185-212

23 Hall, G., Hutchinson, P & Michaelas, N 2004, ‘Determinants of the capital structures of European SMEs’, Journal of Business Finance and Accounting

24 Hamilton, R & Fox, M 1998, ‘The financing preferences of small firm owners’, International Journal of Entrepreneurial Behaviour and Research

25 Harris, M., Raviv, A 1991, ‘The theory of capital structure’, Journal of Finance 46 (1), pp 297-355

26 Harvey, C., Lins, K & Roper, A 2004, ‘The effect of capital structure when expected agency costs are extreme’, Journal of Financial Economics 74, pp 3-30

27 Holmes, S & Kent, P 1991, ‘An empirical analysis of the financial structure of small and large Australian manufacturing enterprises’, Journal of Small Business Finance 1, pp 141-154

28 Kumar, K., Rajan, R & Zingales, L 2001, ‘What determines firms size?’,

University of Chicago, CRSP Working Paper No 496

29 Levy, B 1993, ‘Obstacles to developing indigenous small and medium enterprise: An empirical assessment’, the World Bank Economic Review 7(1), pp 65-68

30 Lửửf, H 2004, ‘Dynamic optimal capital structure and technical change’,

Structural Change and Economic Dynamics 15(4), pp 449-468

31 Michaelas, N., Chittenden, F and Poutziouris, P 1999, ‘Financial Policy and Capital Structure Choice in U.K SMEs: Empirical Evidence from Company Panel Data’, Small Business Economics 12: pp 113 – 130

32 Modigliani, F and Miller, M.H 1958, ‘The Cost of Capital, Corporate Finance, and the Theory of Investment’, American Economic Review 48, pp

33 Myers, S C 1977, ‘Determinants of Corporate Borrowing’, Journal of Financial Economics 25, pp 25–43

34 Myers, S C 1984, ‘The Capital Structure Puzzle’, Journal of Finance 39, pp 575-592

35 Nguyen, T D K & Ramachandran, N 2006 ‘Capital Structure in Small and Mediumsized Enterprises’, ASEAN Economic Bulletin, 23(2), 192-211

36 Ozkan, A 2002, ‘The determinants of corporate debt maturity: Evidence from

UK firms’, Applied Financial Economics 12, pp 19-24

37 Psillaki, M 1995, ‘Credit rationing and small and medium sized firms: A tentative of linkage’, Revue Internationale PME 8, pp 67-90

38 Rajan, R G., and L Zingales 1995, ‘What do we know about capital structure? Some evidence from international data’, Journal of Finance 50(1), pp 1421-1460

39 Rajan, R & Zingales, L 1998, ‘Financial dependence and growth’, American

40 Ratha, D., Mohapatra, S and Suttle, P 2003, ‘Capital structure and performance in Developing countries’, Global Development Finance, pp 109-

41 Robichek, A A., & Myers, S C 1966, ‘Problems in the Theory of Optimal Capital Structure’, Journal of Financial and Quantitative Analysis 1(2), pp 1-

42 Stohs, M H., and D C Mauer 1996, ‘The Determinants of Corporate Debt Maturity Structure’, Journal of Business 69, pp 279-312

43 Stulz, R 1990, ‘Managerial discretion and optimal financing policies’,

Journal of Financial Economics 26, pp 3-27

44 Titman, S 1984, ‘The Effect of Capital Structure on a Firm’s Liquidation Decision’, Journal of Financial Economics 13, pp 137–151

45 Titman, S 2001, ‘The Modigliani and Miller theorem and market efficiency’,

Working paper, National Bureau of Economic Research

46 Titman, S and Roberto W 1988, ‘The Determinants of Capital Structure’,

47 Van der Wijst, N & Thurik, R 1993, ‘Determinants of small firm dent ratios:

An analysis of retail panel data’, Small Business Economics 5, pp 55-65

48 Vu, N.N 2003 ‘Determinants of capital structure for listed firms in the Vietnam stock Exchange Market’, Danang University Press, Danang,

49 Wei Xu, Xiangzhen Xu, Shoufeng Zhang 2005, ‘An Empirical Study on Relationship between Corporation Performance and Capital Structure’, China-

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

_cons | -1.181748 4328016 -2.73 0.006 -2.030024 -.3334726 -+ - sigma_u | 14383665 sigma_e | 09960351 rho | 67589318 (fraction of variance due to u_i)

_cons | 2795246 2277873 1.23 0.220 -.1669302 7259794 -+ - sigma_u | 06662179 sigma_e | 05774169 rho | 57104218 (fraction of variance due to u_i)

_cons | -1.465162 3909832 -3.75 0.000 -2.231475 -.6988486 -+ - sigma_u | 12538959 sigma_e | 09275327 rho | 64633442 (fraction of variance due to u_i)

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

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 Hochiminh City
Chuyên ngành	Banking and Finance
Thể loại	master thesis
Năm xuất bản	2011
Thành phố	Ho Chi Minh City

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