The impacts of capital structure on firm’s operational efficiency

From that, the author examine the impact of some factors such as company’s size, asset structure, total asset growth and short - term liquidity ratio to operational efficiency as well a[r]

Object 142

The impacts of capital structure on firm’s operational efficiency

Group sciences: Nguyễn Thị Quỳnh Thi Trịnh Thu Ngân

Nguyễn Thị Phương Thảo Phí Minh Huệ

Class: AC2015C

INTRODUCTION

Most of enterprises attach much importance to analysis and evaluation the impact of capital structure ontheir operational efficiency The purpose of this study is to find out how capital structure effect on firm’s operation efficiency of joint stock companies listed on Vietnam Stock Market.Company’s size, asset structure, total asset growth and short - term liquidity ratio are controlled variables used in the research.The research uses data from 150 companies on the period from Quarter of 2016 to Quarter of 2017 based on Pooled OLS regression model The result shows that capital structure have negative effect on firm’s operational efficiency, while short-term liquidity, asset structure and total asset growth rate have no effect on firm’s performance There is only one factor has the same direction with enterprise’s performance, that is size of company

1 Thesis statement

This thesis studies how capital structure does affect the operational efficiency of companies listed on the Vietnamese stock market Most of companies want to maximize profit and move toward added corporate value Therefore, for an enterprise, it is very important to improve operational efficiency and make decision in selection of investment opportunities

This thesis consists chapters

Chapter I: Literature Review and Theoretical Framework of Capital Structure and Operational Efficiency

Chapter II: Data and Research Methodology Chapter III: Results and Experimental Discussion Chapter IV: Conclusions and Recommendations Rationale

expectations are identical, the value of company is independent from capital structure The study above contributed to the formation of modern capital structure theories In fact, successful managers who can determine the optimal capital structure by minimize company financial cost and maximize company profit As can be seen, capital structure affects on firm’s operational efficiency

However, the empirical evidence about relationship between capital structure and firm’s performance in Vietnam is not much and has some limitations

Within the scope of personal knowledge, author has implemented the topic “The impacts of capital structure on firm’s operational efficiency” From that, the author examine the impact of some factors such as company’s size, asset structure, total asset growth and short - term liquidity ratio to operational efficiency as well as the influence of capital structure on performance of companies that listed on Hanoi Stock Exchange and Ho Chi Minh City Stock Exchange

3 Significance

The finding of this study shows the empirical evidence on the relationship between capital structure and firm's operational efficiency in Vietnam The author provides some evidence to prove that capital structure have negative effect on company’s performance This study also suggests some recommendation for Competent Authorities and Enterprises

CHAPTER1: LITERATURE REVIEW AND THEORETICAL FRAMEWORK OF CAPITAL STRUCTURE AND OPERATIONAL EFFICIENCY

An overview of Capital structure

balance between ideal debt- to- equity range and minimizes the firm’s cost capital Therefore, the firm must consider risk, tax position financial flexibility and managerial conservatism or aggressiveness because these factors are very crucial in determining capital structure target Moreover the operation conditions also may cause the actual capital structure differ optimal capital structure

The important decision for any firm is a decision about appropriate capital structure It is not only because of the need maximizes profit for organization, but also is for organization’s complete ability The first opinion about capital structure was researched by Modigliani and Miller[CITATION Mod58 \n \t \l 1033 ] It refers that optimal capital structure exist which balance the risk of bankruptcy with the tax savings of debt Once established, this capital structure should provide greater return for stock holder than they would receive from all-equity firm

In theory, the modern financial technical would allow manager to calculate relation between debt and equity of each firm However, in practice, there are many studies which found that the most companies not have optimal capital structure Capital structure is the combination of the debt and equity of a firm It can also be known as the way that a firm finances it through some combinations of debt and equity The various composition of a firm’s capital structure according to Inanga and Ajayi[CITATION Ina99 \n \t \l 1033 ] may be classified into parts Those are equity capital, preference capital and long- term loan (debt) capital Equity capital is the contributed capital, money originally invested in the business in exchange for share of stock, and retained profits, profit from past years that have been kept by the company to strengthen the balance sheet, growth, acquisition and expansion business Preference capital is the mixture that combines the features of debentures and shares except the benefit Debt capital refers to the long- term debt that company used to finances its investment decisions while coming up with its principal and also paying back interest

Theoretical Framework of Capital Structure:

when Modigliani and Miller[CITATION Mod58 \n \t \l 1033 ] assumed the corporate income tax and they recognized that the value of a levered firm was equal to the value of an unlevered firm plus the present value of the "tax shield" The M&M theory considered the advantages of the tax shield However, the argument about maximizing debt ratio to benefit from tax shield was not appropriate because it did not care about the bankruptcy costs, the asymmetric information as well as the agency cost that might occur when lending From the limitations of M&M theory, many other investigations have been developed to build modern capital structure theories The ability of businesses fall into bankruptcy which depends on not only the business risk but also the policy of mobilizing, managing, operating and using capital of enterprises

The trade-off theory was developed by Kraus and Litzenberger[CITATION Kra73 \n \t \l 1033 ], considered that the capital structure of an enterprise affect in both cost and benefit sides A company would borrow until the marginal benefit of the tax reduction (tax shield from the debt) was equal to the increase in the present value of the bankruptcy costs

Another research, the pecking order theory, which was supported by Myers and Majluf [CITATION Mye84 \n \t \l 1033 ] when they examined the asymmetric information that existed between managers, shareholders, and investors This theory pointed out that there is no optimal capital structure for a company and that it explained the prioritization of capital sources and loans when business mobilize capital It means the companies would like to fund themselves firstly with internal resources, then with loans, and eventually with the equity provided by shareholders

Agency cost theory which provided by Jensen and Meckling [CITATION Jen76 \n \t \l 1033 ] is discussing the expenses that derived from the conflict of interest between principals (shareholders) and decision makers (agents) of firms (managers, board members, and so on), this occurred because of divergences in executive decisions This costs concurrent related to the argument between shareholders and creditors when the company's debt increased and shareholders received benefits in case of bankruptcy

1.1.1 The capital structure theory of Modigliani and Miller (M&M)

capital structure does not influence company's value This suggests that the valuation of a firm is irrelevant to the capital structure of a company Whether a firm is highly leveraged or has lower debt component in the financing mix, it has no bearing on the value of a firm

The capital structure theory of M&M is based on the following key assumptions: There are no taxes

Transaction cost for buying and selling securities as well as bankruptcy cost is zero

There is symmetry of information This means that an investor will have access to same information that a corporate would and investors would behave rationally

The cost of borrowing is the same for investors as well as companies

Debt financing does not affect debt on a company's earnings before interest and taxes Proposition 1: In the assumptions of “no taxes”, the capital structure does not influence the valuation of a firm In other words, raising the capital structure does not increase the market value of the company The profits are equal between the debt holders in the company and equity shareholders

Proposition 2: In the assumptions of "no bankruptcy costs", cost of capital effect on capital structure which refers that borrowing gives tax advantage, because the interest will deduct from the tax which result what is known as tax shields, which in turn reduce the cost of debt and then maximize the firm performance

1.1.2 Trade- off theory:

Trade-off theory aims to explain the fact that corporations usually are financed partly by debt and partly by equity An important reason why the businesses cannot get enough capital is that because of having benefits from the debt shield The use of debt finance also generates the highest cost This is financial exhaustion which has components: 1) costs that arise when financial exhaustion or bankruptcy; and 2) the possibility of financial exhaustion and bankruptcy These costs include direct costs and indirect costs

are always looking to optimize the total value of the business based on this balance to determine how much debt and how much equity to choose in their capital structure

Trade-Off theory suggested by Myers[CITATION Mye841 \n \t \l 1033 ] emphasize a balance between tax saving arising from debt, decrease in agency cost and bankruptcy and financial distress costs The Trade-Off theory is the oldest theory and connected to the theory from Miller and Modigliani on capital structure that emphasize on optimal capital structure Kraus and Litzenberger[CITATION Kra73 \n \t \l 1033 ] commented that the optimal financial leverage represents a trade- off between the tax benefits of debt and the cost of bankruptcy According to Myers[CITATION Mye841 \n \t \l 1033 ], an enterprise that follows the static tradeoff theory establishes a debt ratio based on target corporate value and gradually adjusts for that goal This target rate is determined by balancing the benefits from the tax shield for debt and bankruptcy costs

The Trade-off theory is an important one while studying the Financial Economics concepts The theory describes that the companies or firms are generally financed by both equity and debt The Trade-off theory of capital structure refers to the idea that a company chooses how much debt finance and how much equity finance to use by balancing the costs and benefits Trade-off theory of capital structure basically entails offsetting the costs of debt against the benefits of debt

1.1.3 Pecking Order Theory

In corporate finance, pecking order theory (or pecking order model) postulates that the cost of financing increases with asymmetric information

Financing comes from three sources, internal funds, debt and new equity When a company need cash for a new investment companies prioritize their sources of financing, first preferring internal financing, and then debt, lastly raising equity as a "last resort"

According to Myers[CITATION Mye841 \n \t \l 1033 ], due to adverse selection, firms prefer internal to external finance When outside funds are necessary, firms prefer debt to equity because of lower information costs associated with debt issues

subject to serious adverse selection problems while debt has only a minor adverse selection problem

If there is an inadequate amount of retained earnings, then debt financing will be used Thus, for a firm in normal operations, equity will not be used and the financing deficit will match the net debt issues

Managers are more understandable about the real value and the risk of business than investors that affect to decision of the investor.Company just issue stock when their stock value is higher than internal stock value Therefore, when the company announced information about issuing stock, they will send to investor a message about this problem Then, the market appear a lot of issuing information and it is a bad signal for development of enterprise and it leads to reduce stock price

Therefore, to avoid the reducing stock price, company will not issue the new equity

1.1.4 Agency cost theory:

Agency cost is a type of cost that arise when an organization gets a problem of lack of agreement between the purpose of manager and the owner and the information asymmetry Management can not make decision in profit of the owner or damage to those rights For example, instead of increase the value of company, manager use money for his purpose In order to solve this problem, the owner find the approach to control and ensure that manager actions are in line with their interests such as requirements on annual reports annual meeting, and bonus for directors based on management effectiveness Therefore, the conflict between manager and owner increase agency cost

The better the enterprise is managed, the lower agency costs .Using debt increase that leads to reduce agency cost As the debt ratio rises, manager will have to more cautions about new debt and using capital decision will helps they manage their organization more effective

the more asymmetric information between business and creditors, the more agency costs

For example, when shareholder and creditor want to invest in a project,they have to trade-off between risk and profit If it is high-risk project, it will create much profit for shareholder but it increase the probability to bankruptcy for the creditors

Agency costs theory illustrate that agency cost of equity has the positive related with the debt ratio, but agency costs of debt capital directly related to business financial leverage

Literature review:

Since M&M theory has been published, there are many researchers still studying the relationship between capital structure and operational efficiency Not only in foreign countries, but also in Vietnam, there are many articles on this subject

1.1.5 International literature review:

Phillips and Sipahioglu [CITATION Pau07 \n \t \l 1033 ]had a research about relationship between capital structure and operational efficiency To this research, they followed “Theoretical Framework of Capital Structure” of Franco Modigliani and Merton Miller (M & M)[CITATION Mod58 \n \t \l 1033 ] and using data from collected from 43 UK quoted organizations which possess an interest in owning and managing hotels to test this theory And their finding was similar to M&M theory, in other words, capital structure not related to operational efficiency

Moreover, Iorpev and Kwanum[CITATION Ior12 \n \t \l 1033 ]also study the relationship between capital structure and operational efficiency of manufacturing companies listed on the Nigerian Stock Exchange They covered a period of five years from 2005- 2009 The result concludes that capital structure is not a main determinant of firm’s operational efficiency

Abu-Rub and Abbadi [CITATION DrN12 \n \t \l 1033 ] have done the research by using panel procedure for a sample of 28 listed companies the Palestinian Stock exchange (PSE) over the period 2006 to 2010 This study established a model to measure the effect of capital structure on the bank efficiency measured by ROE, ROA, Total deposit to asset, total loans to assets and total loans to deposits were used to measure capital structure The result showed that capital structure had a positive impact on the firm’s operational efficiency

Using panel data consisting of 257 South African firms over the period 1998 to 2009, Fosu [CITATION Sam13 \n \t \l 1033 ] investigated the association between capital structure and firm performance To test the relationship, he used GMM regression approach and found a positive and significant relation between capital structure and firm’s operational efficiency

Pouraghjan et al ,[CITATION Abb12 \n \t \l 1033 ] also found a significant positive link between capital structure and firm performance in the Tehran Stock Exchange The main objective of this study is to investigate the impact of capital structure on the financial performance of companies listed in the Tehran Stock Exchange They collected data from 400 firms that belong to 12 industrial groups from 2006 to 2010 In this study, authors used ROA and ROE to measure financial performance of companies Results indicate that there is a strong negative and significant relationship between debt ratios and performance measures of Iranian firms (ROA and ROE)

Chinaemeram & Anthony[CITATION Chi12 \n \t \l 1033 ] researched about the impact of capital structure on the operational efficiency of Nigeria companies They used the income balance sheet data from 30 companies listed on the Nigeria stock exchange in the period from 2004 to 2010 and they used Pooled OLS method The result showed that capital structure had a negative impact on operational efficiency

Moreover, group of authors including Awais et al[CITATION Awa16 \n \t \l 1033 ] also found the similar conclusion In detail, they used a sample of 100 non-financial firms listed on the Karachi Stock Exchange during the 2004 – 2012 during the research The finding of research suggests that company’s financial manager should consider debt as a last alternative to finance their operation as it has a negative impact on the company performance

Kinsman and Newman[CITATION Kin98 \n \t \l 1033 ] studied the relationship between debt level (including three measures of debt level) and firm’s performance and detected diverse results This study found that earnings are negatively correlated with short-term debt, but are positive with long-term debt A similar result was found by Mesquita and Lara[CITATION Mes15 \n \t \l 1033 ] in Brazil

Tianyu[CITATION Tia13 \n \t \l 1033 ] examined the influence of capital structure on firm’s performance in both developed and developing markets A sample of 1200 listed firms in Germany and Sweden and 1000 listed firms in China for the period 2003-2012 has been used in his study Applying OLS regression method, he documented that capital structure has a significant negative effect on firm’s performance in China, whereas significant positive effect in two European countries, i.e., Germany and Sweden, before financial crisis in 2008

1.1.6 Vietnamese literature review:

Doan Ngoc Phuc [CITATION Doan \n \t \l 1033 ] studied and evaluated the effect off capital structure on businesses operational efficiency with the data source used include 217 companies that are listed on two exchanges in Ho Chi Minh and Ha Noi city (2007- 2012) The independent variable that used in this research is return of asset (ROA) and return of equity (ROE) The result showed that the long term debt had positive effect on ROA and ROE, while the short-term debt and total debt had negative effect on business operational efficiency

through years This research depend on the advanced panel threshold regression estimation developed in 1999 by Hansen that indicate whether there are positive or negative impacts of capital structure on firm operation The author concluded that the relationship between capital structure and firm value has a nonlinear relationship represent a convex Parapol shape He thinks that to ensure and enhance the firm value, the scope of optimal debt ratio should be less than 57.39%

Le & Phung[CITATION LeV13 \n \t \l 1033 ] study investigates the impact of capital structure on operational efficiency in all firms listed in Vietnamese Stock Exchange during the period from 2007 to 2011 They used return on assets (ROA), return on equity (ROE) and Tobin Q to measure the firm’s operational efficiency, while to measure capital structure they used short-term debt, long-term debt and total debt ratio They founds that capital structure has a significant negative impact on firm operational efficiency

Tran Hung Son[CITATION Tra08 \n \t \l 1033 ] studied about the capital structure and the effect on business operation The research using data from 50 non-financial companies that listed on the Ho Chi Minh City Stock Exchange and has the largest market value as of September 2008 The author uses the profit variable as the mean of ROA and ROE to measure the performance of the business The results show that the performance of an enterprise is positively correlated with the capital structure

Tran Thi kim Oanh [CITATION Tra17 \n \t \l 1033 ] studied about effect of capital structure on enterprises efficiency operation The author analyses data of 81 companies that were listed on the Vietnam Stock Exchange during 2009-2015 with table data processing technique and regression analysis The results show that company performance is influenced by capital structure, asset structure, solvency, CIT and risk

1.1.7 Literature review summary:

Table 1: Summary of the results of previous studies.

Author Result of the research Market

The capital structure have no effect on firm operation Paul A Phillips and

Mehmet A

Sipahioglu[CITATION Pau07 \n \t \l 1033 ]

Their finding was similar to M&M theory, in other words, capital structure not related to operational efficiency

US

Iropev and

Kwanum[CITATION Ior12 \n \t \l 1033 ]

The result concludes that capital structure is not a main determinant of firm’s operational efficiency

Nigeria

Jonchi Shyu[CITATION Jon13 \n \t \l 1033 ]

He found that the capital structure decisions of group‐affiliated firms are independent of firm performance

Taiwan

The capital structure has positive effect on firm operating

Abu-Rub and

Abbadi[CITATION DrN12 \n \t \l 1033 ]

The result showed that capital structure had a positive impact on the firm’s operational efficiency

Palestine

Fosu[CITATION Sam13 \n \t \l 1033 ]

He used GMM regression approach and found a positive and significant relation between capital

structure and firm’s

operational efficiency

South Africa

Pouraghjan et

al[CITATION Abb12 \n \t \l 1033 ]

He also found a significant positive link between capital

structure and firm

performance in the Tehran Stock Exchange

Tehran

Tran Hung Son [CITATION Tra08 \n \t \l 1033 ]

The results show that the

performance of an

enterprise is positively correlated with the capital structure

The capital structure has negative effect on firm operation

Chinaemeram &

Anthony[CITATION Chi12 \n \t \l 1033 ]

They used Pooled OLS method The result showed that capital structure had a

negative impact on

operational efficiency

Nigeria

Akeem et al[CITATION Law14 \n \t \l 1033 ]

They observed that capital structure measures (total debt and debt to equity ratio) are negatively related to firm performance

Nigeria

Awais et al[CITATION Awa16 \n \t \l 1033 ]

The finding of research suggests that company’s financial manager should consider debt as a last alternative to finance their operation as it has a negative impact on the company performance

Karachi

Le & Phung[CITATION LeV13 \n \t \l 1033 ]

They founds that capital structure has a significant negative impact on firm operational efficiency

Vietnam

Doan Ngoc

Phuc[CITATION Doan

\n \t \l 1033 ]

The result showed that the long term debt had positive effect on ROA and ROE, while the short-term debt and total debt had negative

effect on business

operational efficiency

There exist an optimal capital structure for each firm NguyenThanh Cuong

[CITATION Ngu14 \n \t \l 1033 ]

He thinks that to ensure and enhance the firm value, the scope of optimal debt ratio should be less than 57.39%

Vietnam

Other results

Kinsman and Newman [CITATION Kin98 \n \t \l 1033 ]

This study found that earnings are negatively correlated with short-term debt, but are positive with long-term debt

Banglade sh

Tianyu[CITATION Tia13 \n \t \l 1033 ]

Applying OLS regression method, he documented that capital structure has a significant negative effect on firm’s performance in China, whereas significant positive effect in two European countries, i.e., Germany and Sweden, before financial crisis in 2008

Germany, Sweden, China

Source: Author’s collection Relationship between capital structure and operational efficiency

The important issues of operational efficiency include profit maximization, maximizing return on assets, maximizing shareholders benefit

CONCLUSION CHAPTER 1

CHAPTER 2: DATA AND RESEARCH METHODOLOGY

2.1 Data Description

To identify the correlation between factors in Table and operational efficiency of firms, the author used raw data from enterprises that collected randomly from joint stock companies on the HNX and HOSE Therefore, it can represent enterprises’ diversification in the economy Besides, to show the novelty of this study, data was selected from last three quarters of 2016 to three next quarter of 2017 (from April, 2016 to September, 2017) Data is extracted from Finance.Vietstock.vn by authors Moreover, enterprises, which have not enough data to fill full variables in the model for the period from 2016 to 2017, will not be included in the model As the result, we have a sample of 150 joint stock companies (150x6 = 900 observations)

Some tables below show the results describing in detailed data that related to the variables in the study See more in APPENDIX A

Table 2: The averaged value of variables

Time TDR SIZE TANG GRO LIQ

Q2/2016 45.19% 13.42 22.77% 16.22% 3.23

Q3/2016 44.65% 13.44 23% 13.86% 3.00

Q4/2016 43.66% 13.46 22.75% 10.16% 3.29

Q1/2017 42.28% 13.47 22.50% 9.7% 4.19

Q2/2017 44.17% 13.65 22.36% 14.84% 3.05

Q3/2017 44.36% 13.64 22.38% 18.15% 3.33

2016, GRO tend to decrease, 16.22% to 10.66% On the other hand, upward trend of GRO is next period, from first quarter in 2017 to third quarter in 2017, 9.7% to 18.5%, so it is too difficult to evaluate with GRO However, only averaged value of variable is not meaningful for this research

Table 3:ROE Description

Date Mean MinimumROE Maximum

Q2/2016 3.87% -16.43% 58.4%

Q3/2016 3.99% -16.18% 50.63%

Q4/2016 4.17% -21.95% 44.47%

Q1/2017 3.24% -10.9% 21.82%

Q2/2017 3.03% -22.76% 25.34%

Q3/2017 2.27% -145.92% 37.11%

Table 4: TDR Description

Date Mean MinimumTDR Maximum

Q2/2016 45.19% 0.67% 94.27%

Q3/2016 44.65% 0.92% 93.91%

Q4/2016 43.66% 1.33% 93.8%

Q1/2017 42.28% 0.07% 94.21%

Q2/2017 44.17% 2.18% 95.94%

Q3/2017 44.36% 1.57% 96.28%

Source: Author’s calculation Table gives three important statistics (mean, minimum, maximum) of variable TDR in our analysis The mean ratio shows that an average firm uses 44 percent debt in its total assets in Vietnam It means that the companies use debt that are lightly lower than equity Third and fourth column give details of firm’s ratio in terms of minimum and maximum values respectively The ratios have slight fluctuation among the quaters Beside, the gap between minimum and maximum is big, about 94 percent In our analysis, the variable TDR seem to be normally distributed

1.2 Research model

Determination of regression model

According to Chinaemerem and Anthony [CITATION Chi12 \n \t \l 1033 ], the multivariate regression model described as follows:

ROEit = αi + β1TDRit + βxXit + σ1FLOOR + εit

In with:

- i = 1, 2, , N (N is the number of enterprises)

- t = 1, 2, , T (T is the observation time in the model)

- FLOOR is the dummy variables that identify stock exchange markets of enterprises in research If enterprises listed on Ho Chi Minh City Stock Exchange, the FLOOR equal to one (1) and FLOOR equals to zero (0) if enterprises listed on Hanoi Stock Exchange

- α is the estimated constant; βx&σ1 are estimated coefficients; ε is the residual Variables in the model

size (SIZE), asset structure (TANG), total asset growth (GRO) and short-term solvency (LIQ) In addition, dummy variable is a stock exchange in this model Table below will show all variable in a systematic way

Table 4: Variables of the model and Measurement Type of variable Sign Variable Formulary

Dependent Variable ROE Return on equity Profit after tax/ Average equity Independent

Variable TDR

Debt ratio to total

assets Total Debt/ Total Assets

Control Variable

SIZE Enterprise Size Total assets

TANG Assets structure Fixed assets/ Total assets GRO Total assets

growth rate

(Total assetsit – Total assetsit-1)/ Total assetsit-1

LIQ Short – term liquidity

Short – term assets/ Short – term liabilities

FLOOR Stock exchange market

Equal to if enterprises listed on Ho Chi Minh City Stock Exchange, and equal to if enterprises listed on Hanoi Stock Exchange

 Dependent variable:

As mentioned above, dependent variable is the return on equity (ROE) Major shareholders often use the ratio as a key factor because ROE will show exactly how much earned from one unit of capital In normal, investors analyze ROEs then they compare with other shares in similar industry on the market and decide typical shares.The higher value of ROE, the higher efficiency used of company equity.This means that the enterprise make effective control of balance between equity and external borrowings to gain more its competitive advantage in capital mobilization, as well as expanding its scale That is why, the higher value of ROE, the more attractive the stock is

 Independent variable

company to meet long-term obligations A ratio greater than shows that a considerable portion of debt is funded by assets, in other words, the company has more liabilities than assets A high ratio also indicates that a company may be putting itself at a risk of default on its loans if interest rates were to rise suddenly A ratio below translates to the fact that a greater portion of a company's assets is funded by equity.Like all other ratios, the trend of the total debt to total assets should also been evaluated over time This will help assess whether the company’s financial risk profile is improving or deteriorating For example, an increasing trend indicates that a business is unwilling or unable to pay down its debt, which could indicate a default at some point in the future

 Control variable:

Size of enterprise (SIZE)

Business size is also a key factor to evaluate the capacity of bankruptcy of the business Businesses with big size often have lower risk of bankruptcy because they have large and diversified assets, so the risk divided into several parts Moreover, large enterprises are often highly transparent, thus minimizing asymmetry information and reducing agency costs when they need a loan

Asset structure (TANG)

Asset structure is control value in model Formula of asset structure is fixed asset/total asset Asset structure is one of factor to evaluate financial health of a business

Total Asset Growth (GRO)

Total assets growth (GRO) is increase or decrease of asset over period To assets growth can be a negative number, that mean asset in this period is smaller than asset in previous period With investors, total assets growth is complicated ratio to evaluate enterprise That means positive number of total assets growth is not always good When analyzing asset growth rates, investors always see a lots of elements such as what is the purpose of asset growth, what type of asset is growing, where funds financed and so on If enterprises use mainly loan capital, we need to reconsider this company In detail, loans will have to pay both interest and principal, the use of investment loans will take many risk, and making the wrong investment decision can lead to heavy losses or bankruptcy

Using term liquidity ratio help investors see the ability of a firm to use short-term assets such as cash, inventory or receivables to pay off its short-short-term liabilities The higher the ratio, short-term assets is bigger than short-term liability, so we can believe that the firm can repay short-term debt However, if this ratio is too high, it is not good expression because too high ratio proves inefficiency capital structure On the other case, this ratio is less than one (1), we can believe that the firm is in a negative financial position, but we not said that the company goes bankrupt because there are many solutions to raise more capital

Dummy variable

Stock Exchange Market (FLOOR)

Table 5:Pearson correlation matrix between variables ROE

TDR

(%) SIZE

TANG (%)

GRO

(%) LIQ

FLOO R

ROE

TDR (%) -0.04407

SIZE

0.15221

1 0.213169

TANG

(%) 0.02838 0.056162

0.08351

2

GRO (%)

0.03088

7 0.108267

0.09381

3 -0.10864

LIQ

0.00400

6 -0.44975 -0.02575 -0.07344 -0.02593

FLOOR

0.11473

1 -0.13248

0.34984

1 0.06822 -0.10307

0.16158

2

All figures in the table are less than 0.8 That means there is no problem ofmulticollinearity

Estimation method

Pooled ordinary least square used in model to combine normally all observations Pooled OLS is the simplest approach because it ignores the time and space element of panel data

Through regression analysis, it is possible to measure that how independent variable affect the dependent variable in term of both magnitude and tendency Therefore, the author can provide the valid evidence for the issue in this research

Hypothesis construction

In this research, the author tries to find out the relationship between capital structure and firm’s operational efficiency Representing the capital structure is the ratio of total debt/ total assets (TDR) and representing for operational efficiency expressed through return on equity(ROE) Moreover, according to previous studies, firm’s operational efficiency affected by the variables including of size, asset structure, total asset growth rate, short-term liquidity

Hypothesis 1: The debt-to-asset has negative effect on operational efficiency of enterprises

According to Agency-Cost Theory enterprise operational efficiency is in opposite line with the debt-to-asset ratio

Hypothesis 2: Fixed assets (TANG) considered having no effect on operational efficiency of the business

Hypothesis 3: The size (SIZE) of the company has a positive relationship with firm’s operational efficiency

Hypothesis 4: Total assets growth (GRO) has no effect on firm’s operational efficiency

Hypothesis 5: Short-term liquidity (LIQ) has a same direction effect on firm’s operational efficiency

CONCLUSION CHAPTER 2

This chapter shows all the data and research methods about effect of capital structure on firm’s operational efficiency in this study Through the data of 150 joint-stock companies listed on two stock markets HOSE and HNX, 75 companies on HOSE and 75 companies on NHX on period of quarters from second quarter in 2016 to the third quarter in 2017, the author has collected 900 observations

CHAPTER 3: RESULTS ANALYSIS AND EXPERIMENTALDISCUSSION Research result:

3.1.1 Experiment 1

Regression Statistics

Multiple R 0.185237383

R Square 0.034312888

Adjusted R Square 0.027824509

Standard Error 7.434834083

Observations 900

Source: Author’s calculation

df SS MS F

Significance F Regressio

n 1753.94051 292.3234183

5.2883604

2 2.27194E-05 Residual 893 49362.14476 55.27675785

Total 899 51116.08527

Source: Author’s calculation Coefficient

s

Standard

Error t Stat

P-value

Lower 95%

Upper 95% Intercept -2.6044 1.6493

-1.5791 0.1147 -5.8414 0.6325

TDR (%) -0.0291 0.0125

-2.3293 0.0201 -0.0536 -0.0046

SIZE 0.5041 0.1291 3.9038 0.0001 0.2507 0.7575

TANG

(%) 0.0065 0.0121 0.5384 0.5904 -0.0172 0.0302

GRO (%) 0.0044 0.0043 1.0177 0.3091 -0.0040 0.0128

LIQ -0.0502 0.0465

-1.0815 0.2797 -0.1414 0.0409

FLOOR 0.9235 0.5507 1.6769 0.0939 -0.1573 2.0044

As we can see, because the value of P-value is higher than 0.1, the coefficient associated with variable GRO and variable TANG and variable LIQ continuously is not insignificant difference from Furthermore, variable TDR explains the relation on the opposite direction with variable ROE by negative coefficient (-0.029) When the capital structure increases 1%, the rate of return on equity decrease 0.029 Variable TDR has p-value is less than 0.05 so that the coefficient associated with TDR is strongly significant

Besides, the variables SIZE have the relation on the positive direction with variable ROE When the variable SIZE increases 1% then ROE increases 0.504

The dummy variable FLOOR still shows the result to encourage the enterprises to be listed in the stock exchange market of Ho Chi Minh City have operational efficiency more effective than the enterprises listed on the Hanoi stock exchange market

Because the indices of P-value have the small value and strong significant but the result with R square brings the value above zero (0) but too small (0.0343) So we cannot accept this model

On the other hand, the writer is giving the question, if we ignore one some variable how will the changing be In the next experiment, we will looking for the better results

3.1.2 Experiment 2

In the experiment, the author ignores the control of variable FLOOR, LIQ Regression Statistics

Multiple R 0.174214166

R Square 0.030350576

Adjusted R Square 0.026016947

Standard Error 7.441742652

Observations 900

Source: Author’s calculation

df SS MS F

Significance F

Regression

1551.40261

387.850653

7.00350160

5

Total 899 51116.08527

Source: Author’s calculation Coefficient

s

Standard

Error t Stat

P-value

Lower 95%

Upper 95% Intercept -3.4363 1.5903

-2.1608 0.0310 -6.5575 -0.3151

TDR (%) -0.0272 0.0110

-2.4658 0.0139 -0.0488 -0.0055

SIZE 0.5795 0.1186 4.8872 0.0000 0.3468 0.8122

TANG

(%) 0.0080 0.0120 0.6650 0.5062 -0.0156 0.0317

GRO (%) 0.0034 0.0043 0.8012 0.4233 -0.0049 0.0118 Source: Author’s calculation According to this model, in the regression statistics table, the author indicates that R-square equals to 0.0304 which explains 3.04% changing in variable ROE This value is quite low, it means this model is not effectiveness because the impact on ROE level is not high

The model represents that debt to total assets ratio (TDR) is negatively and significantly associated with return on equity ratio (ROE) with the capital structure increases 1%, the rate of return on equity decreases 1.4% Variable SIZE is strongly significant level because of the indices of P-value smaller than 0.05

Variable GRO and variable TANG are insignificant difference from zero because P-value equals 0.4 and 0.5, respectively These P-values are higher than 0.05 These variables not have explanation meaning to any effectiveness on the operational efficiency of the companies

In conclusion, the experiment shows that the operational efficiency is influenced by the capital structure TDR because P-value is 0.014 that smaller than 0.05 However, the author gives a question whether ignoring some variables, the result will be better Therefore, the experiment is ignored intercept and FLOOR

3.1.3 Experiment 3

Regression Statistics

Multiple R 0.440253488

R Square 0.193823133

Adjusted R Square 0.18910279

Standard Error 7.454269103

Observations 900

Source: Author’s calculation

df SS MS F Significance F

Regression 11956.62 2391.324 43.03564 8.39E-40

Residual 895 49731.68 55.56613

Total 900 61688.31

Source: Author’s calculatio Coefficient

s

Standar

d Error t Stat P-value

Lower 95%

Upper 95%

Intercept #N/A #N/A #N/A #N/A #N/A

TDR (%) -0.0362 0.0122 -2.9624 0.0031 -0.0602 -0.0122

SIZE 0.3787 0.0526 7.2000 0.0000 0.2755 0.4819

TANG (%) 0.0053 0.0120 0.4415 0.6589 -0.0183 0.0290 GRO (%) 0.0039 0.0043 0.9220 0.3568 -0.0044 0.0123 LIQ -0.0586 0.0456 -1.2836 0.1996 -0.1482 0.0310

Source: Author’s calculation According to the results of model, the author illustrates that R-square equals to 0.1938, it bigger than value in experiment (0.0304) From this value, the author explains 19.38% changing in variables ROE of the companies listed in both of security markets in HOSE and HNX

In the experiment 3, P-value of TDR variable equals to 0.0031 which smaller than 0.05 so it is strongly significant level Variable TDR has negative direction to variable ROE, when the rate of TDR rise to % then the rate of ROE falls to 0.31%

However, in this experiment, P-value of TANG and GRO equal to 0.7 and 0.4, respectively so these values are insignificant difference from zero

In the model, variable LIQ is insignificant difference from zero because P-value equals to 0.2 that higher than 0.05 The variable SIZE is smaller than 0.05, it explains that it has meaning to effectiveness on the operational efficiency of the companies

As we can see, in comparison among three experiments, when we ignore intercept in experience 3, the results is better than experiment and 2.The experiment shows the wishing results with R-square is above zero and P-value is under 0.05 However, the variable GRO is quite high, at 0.4, higher than 0.05 Therefore, the author decides to carry out the experiment to ignore variable GRO

3.1.4 Experiment 4

In this experiment, writer ignoring intercept and the control variable FLOOR , GRO Regression Statistics

Multiple R 0.439383079

R Square 0.19305749

Adjusted R Square 0.189239602 Standard Error 7.453645119

Observations 900

Source: Author’s calculation

df SS MS F

Significance F Regression 11909.38931 2977.347326 53.59102679 1.62933E-40 Residual 896 49778.9157 55.55682555

Total 900 61688.305

Source: Author’s calculation Coefficient

s

Standard

Error t Stat

P-value

Lower 95%

Upper 95%

Intercept #N/A #N/A #N/A #N/A #N/A

TDR (%) -0.0352 0.0122

-2.8956 0.0039 -0.0591 -0.0114

TANG

(%) 0.0039 0.0119 0.3279 0.7430 -0.0195 0.0274

LIQ -0.0585 0.0456

-1.2810 0.2005 -0.1480 0.0311 Source: Author’s calculation As we can see, R square in experiment is 0.193 that is smaller than experiment so we will choose experiment However, it means that is 19.3% changing in ROE by independent variables

The variable TDR affects on the negative side with ROE when the P-value is small which show by the coefficients is negative (smaller than zero (0)) So, if the capital structure of the enterprise increases 1%, the enterprise will operates less effectively and return on equity will decrease 0.0352

According to the results, variable TANG,variable SIZE and variable LIQ has the same direction with variable ROE

Besides, the rate of enterprise size (SIZE) shows the result that SIZE increases 1% then ROE increases 0.38 which has strong significant effects

Variable LIQ and variable TANG higher than 0.1,the coefficient associated with LIQ is not significant different

The author selects this experiment to identify result of the thesis because the value of R square is positive, at 0.193 and P – value (0.039) of variables are smaller than 0.05 Research discussion:

The result from four experiments illustrate how precision and significant of impact on all factors represent for the capital structure, business size, total assets growth, assets structure and the short-term liquidity to the operational efficient expectation of the enterprise, represent is the ROE

Capital structure(TDR)

Capital structure affects negatively on the operational efficiency This result is suitable with the Agency-Cost Theory that the author puts forward In condition of all the same factors, when the enterprise intensify using debt then the operational efficiency tends to increase thanks to the tax shield from interest This result is also supported by the studies of Chinaemeram & Anthony[CITATION Chi12 \n \t \l 1033 ], Akeem et al [CITATION Law14 \n \t \l 1033 ] and Le & Phung [CITATION LeV13 \n \t \l 1033 ]

Asset structure (TANG)

The result show that the asset structure rate is insignificant different from zero in all model 1, 2, and That mean the asset structure does not affect on firm’s operational efficiency This is suitable with the hypothesis by the author

Total assets growth (GRO)

The growth rate was insignificant different from zero in the above models Base on the result, it can be seen that the total assets growth (GRO) has no effect on firm’s operational efficiency

Size of enterprise (SIZE)

Variable SIZE represents to the size of enterprise Based on models above, variable SIZE shows that it has statistic means That lead to this result is same as hypothesis that is variable SIZE influences the same direction to the operational efficiency of enterprise

Short – term liquidity (LIQ)

the operational efficiency of enterprises will decrease On the other hand, if enterprises keep more highly liquidity assets as reserve, the ability of making profit will be small, especially cashes Therefore, there is a tendency of decreasing operational efficiency

CONCLUSION CHAPTER 3

Chapter is the research result The method used in the research is the statistic description and using the model Pooled OLS The author focuses on four models, the raw data is 900 observations that collected from 150 enterprises listed on the stock exchange markets HOSE and HNX Overall, results are consistent across all models First experiment has all variable in regression model Base on first model, author reject unnecessary variable and navigates and finds right model From the research result, author decided move to other experiment to find out suitable model

In the second experiment, we have variables, including TDR, SIZE, TANG and GRO Writer found that the capital structure and operational efficiency of enterprise have significant relationship (P-value = 0.0139)

In the next model, we have variables, including TDR, SIZE, TANG,GRO and LIQ Writer found that the capital structure and operational efficiency of enterprise have strongly significant relationship (p-value = 0.0031) This model is also main model of research

CHAPTER 4: CONCLUSIONS AND RECOMMENDATION Conclusion

Operational efficiency is the most concerned by manager in the company.Communication, management ability, teamwork, market fluctuation,risk are factors have the big effect on operational of enterprise Besides,the capital structure is an important factor related to operational efficiency which we mentioned in our research by figure in the financial report To achieve success and optimal profit, we need to have the appropriate capital structure.However, if we have the wrong estimate in the capital structure, the enterprise have to face with a lot of difficult and risk such as loss, evenbankruptcy Therefore, that is necessary to identify how the capital structure effect on company operation and their trend .It helps the investor, stakeholder,creditor and business have a right strategy in the market

Throughout 900 observations of 150 companies in quarters from quarter 2/2016 to quarter 3/2017 and regression analysis method Pooled OLS selected randomly.The independent variable is the capital structure (TDR), the control variables conclude the size of the company (SIZE); assets structure (TANG); total assets growth (GRO) and short-term liquidity (LIQ), the dummy variable is (FLOOR) By four model are tested ,the author accept the perfect model including variable TDR, variable TANG, variable GRO, variable SIZE and variable LIQ In this model, 19.38% the changing of ROE in the business and the value of P – value is definitely small are explained by the author In other words, the research results show that the business operational efficiency is influenced in the negative direction with capital structure (TDR), the opposite direction with assets structure (TANG) and total assets growth (GRO)

Recommendation

4.1.1 Recommendation for Competent Authorities

The competent authorities should raise the awareness of corporate finance managers about the importance of capital structure to business performance, in the process of open – door market economy for international integration, in particular Capital structure would be one of the important factors affecting to the business performance of the business

solvency, reduce risks for businesses, increase efficiency of The financial leverage , and the impact of the tax shield, helps enterprises improve business performance Strengthen training to improve the level of professionalism and management capacity for corporate financial managers, and at the same time pay attention to renovating the financial management, controlling activities using capital regularly, ensuring the efficiency of capital use to reduce financial risks and business risks

Vietnam's financial market is developing, businesses still have difficulty in accessing capital to expand production, improve operational efficiency Therefore, strengthening the belief of where the supply of capital by enhancing the reputation of the business through enhanced the ability to pay on time, and apply strategies to mobilize capital consistent with reality the operation of the business and the development of the financial market

The legal system for businesses, especially the income tax policy, needs to be revised and updated to suitable with the integration economy as there are many new forms of business today However, the tax law Vietnam should be built in a scientific and effective way to help businesses catch in time

4.1.2 Recommendation for Enterprises

The research result said that capital structure has negative relationship with operational efficiency of enterprise It is similar with some research before To increase the financial performance of a company, it is necessary to increase the size of equity on the property, increase the ratio of loans to total assets and reduce bad debt ratio In addition, when enterprises build the strategy to select the capital structure in order to enhance the operational efficiency, they need to notice the microeconomic and the macroeconomic of the market, the characteristics of Vietnamese economy and business risk of industry to forecast the growth target as well as make a decision on creating appropriate capital resources

Limitation

This study included only 900 indexes of 150 Joint Stock companies, researched in last three quarters in 2016 and first three quarters in 2017 Because of limited time and some difficulties during collected process, author could not expand the scope of observation

Moreover, due to limited information and data, it is not yet possible to measure the performance of companies with market-related variables

The study also failed to analyze the characteristics of each type of enterprise and the impact of the capital structure of each sector on companies operation

In addition, the variables considered are still mainly inside the business and lack of external variables

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APPENDIX A: VARIABLES DESCRIPTIVE STATISTIC Table 6: Variables descriptive statistic for Quarter

(from Quarter of 2016 to Quarter of 2017) ROE TDR (%) SIZE TANG (%) GRO (%) LIQ FLOO R Mean 3.4274 44.053 13.51 45 22.6263 13.823 3.348 0.5

Standard Error 0.2513 0.773

0.071

85 0.6947 1.9764

0.201

2 0.0167

Median 2.78 45.755

13.37

39 15.015 3.9757 1.66 0.5

Mode 0.28 61.73

14.63

02 0.02 4.43 1.26

Standard Deviation 7.5405 23.190 2.155 20.8402 59.291 6.036 0.5003 Sample Variance 56.858 537.79 4.646 434.315 3515.4 36.43 32 0.2503 Kurtosis 178.19 96 -0.8642 40.36 79 1.2229 140.52 88 41.94 13 -2.0045 Skewness -7.419 -0.0283

2.812 1.3213 10.108 5.772 2.55E-17

Range 204.32 96.21

35.63

72 94.24

1115.67

8 70.61

Minimum -145.92 0.07

4.122

8 0.02

-99.568

0 0.18

Maximum 58.4 96.28 39.76 94.26

1016.1

1 70.79

Sum 3084.6 39 39648 18 12163 20363.6 12441 49 3013 35 450

Count 900 900 900 900 900 900 900

7 Standard Error

0.531

7 1.8266 0.1411 1.7560 6.4661

0.588

0 0.0410

Median 3.32 46.825 13.3267 14.9 2.64 1.75 0.5

Mode 1.45 41.51 #N/A 3.41 #N/A 2.33

Standard Deviation

6.512

22.370

7 1.7277 21.5063 79.1935 7.2 0.5017 Sample Variance 42.41 31 500.44 2.9851 462.519 6271.61 61 51.85 0.2517 Kurtosis 35.21 93

-0.8909 5.3999 1.2559 75.2559

59.99 99 -2.0272 Skewness 4.121

-0.0426 -0.7039 1.3455 7.6699

7.259 78

4.38E-17

Range 74.83 93.6 14.7637 94.23

927.501

7 70.42

Minimum -16.43 0.67 4.1228 0.03

-99.5680 0.37

Maximum 58.4 94.27 18.8865 94.26

827.933

7 70.79

Sum 580.3 6779.0 2013.45 56 3415.58 2433.36 32 484.9 75

Table 8: Variable descriptive statistic for Quarter of 2016

ROE

TDR

(%) SIZE

TANG

(%) GRO (%) LIQ

FLOO R

Mean 3.9861 44.654 13.4423 23.0025 13.8604 2.9987 0.5

Standard Error 0.5327 1.8364 0.1415 1.7522 4.6152 0.4387 0.0410

Median 3.12 47.52 13.3825 15.585 2.4397 1.715 0.5

Mode -0.33 #N/A #N/A 38 0.14 1.31

Standard

Deviation 6.5239 22.4907 1.7331 21.4594 56.5239 5.3733 0.5017

Sample Variance

42.561

5 505.832 3.0035 460.5071 3194.9541 28.8727 0.2517

Kurtosis

26.071

6 -0.9194 5.3909 1.4072 55.9917

52.117

9 -2.0272

Skewness 3.8907 -0.0605 -0.6928 1.3627 6.8435 6.6755 4.38E-17

Range 66.81 92.99 14.8384 92.01 572 50.72

Minimum -16.18 0.92 4.1318 0.02 -37.72 0.23

Maximum 50.63 93.91 18.9702 92.03 534.28 50.95

Sum 597.91 6698.1

2016.350

1 3450.382 2079.053 449.8 75

Table 9: Variable descriptive statistic for Quarter of 2016 ROE

TDR

(%) SIZE

TANG (%)

GRO

(%) LIQ

FLOO R

Mean 4.1722 43.6635 13.4637 22.7451 10.1601 3.2893 0.5

Standard Error

0.527

1 1.8775 0.1427 1.7189 3.1489 0.42396 0.0410

Median 3.06 45.3 13.2557 15.4 3.8 1.665 0.5

Mode 3.12 30.46 #N/A #N/A -7.92 1.3

Standard Deviation

6.455

8 22.9945 1.7473 21.0517 38.5659 5.1924 0.5017

Sample Variance 41.6775 528.7464 3.0531 443.1753 1487.328

26.960

9 0.2517

Kurtosis

12.954

7 -0.8404 5.1890 1.2801 36.3380 23.3457 -2.0272

Skewness 1.9769 -0.0394

-0.6569 1.3359 5.2196 4.4842 4.380E-17

Range 66.42 92.47 14.857 92.34 362.706 37.5

Minimum -21.95 1.33 4.1504 0.02 -43.4161 0.36

Maximum 44.47 93.8

19.007

4 92.36 319.29 37.86

Sum 625.83 6549.53 2019.558 3411.7642 1524.0079 493.4 75

Table 10: Variable descriptive statistic for Quarter of 2016 ROE

TDR

(%) SIZE

TANG (%)

GRO

(%) LIQ

FLOO R

Mean 4.1722 43.6635

13.463

7 22.7451 10.1601 3.2893 0.5

Standard Error 0.5271 1.8775 0.1427 1.7189 3.1489 0.4240 0.0410

Median 3.06 45.3

13.255

7 15.4 3.8 1.665 0.5

Mode 3.12 30.46 #N/A #N/A -7.92 1.3

Standard

Deviation 6.4558 22.9945 1.7473 21.0517 38.5659 5.1924 0.5017

Sample Variance

41.677

5 528.7464 3.0531 443.1753 1487.328

26.960

9 0.2517

Kurtosis

12.954

7 -0.8404 5.1890 1.2801 36.3380

23.345

7 -2.0272

Skewness 1.9769 -0.0394

-0.6569 1.3359 5.2196 4.4842 4.38E-17

Range 66.42 92.47

14.857

0 92.34 362.7061 37.5

Minimum -21.95 1.33 4.1504 0.02 -43.4161 0.36

Maximum 44.47 93.8 19.0074 92.36 319.29 37.86

Sum 625.83 6549.53 2019.558 3411.7642 1524.0079 493.4 75

Table 11: Variable descriptive statistic for Quarter of 2017 ROE

TDR

(%) SIZE

TANG (%)

GRO

(%) LIQ

FLOO R

Mean 3.2423 42.2845 13.4692 22.4993 9.7017 4.1908 0.5

Standard Error 0.3101 1.9197 0.1440 1.6684 2.2907 0.6238 0.0410

Median 2.475 44.065 13.3131 15.1577 3.1098 1.71 0.5

Mode 7.23 50.44 14.2113 0.02 5.55 1.16

Standard

Deviation 3.7979 23.5116 1.7640 20.4332 28.0547 7.6394 0.5017

Sample Variance

14.423

8 552.795 3.1116 417.5144 787.0660

58.361

0 0.2517

Kurtosis 4.5096 -0.8692 4.7794 1.3985 15.5962

23.900

4 -2.027

Skewness 0.9116 -0.0197 -0.6105 1.3580 3.0418 4.5572

4.38E-17

Range 32.72 94.14 14.8012 91.5 227.97 57.52

Minimum -10.9 0.07 4.2468 0.02 -30.28 0.48

Maximum 21.82 94.21 19.0480 91.52 197.69 58

Sum 486.34 6342.67

2020.38

6 3374.892 1455.253 628.62 75

12: Variable descriptive statistic for Quarter of 2017 ROE

TDR

(%) SIZE

TANG (%)

GRO

(%) LIQ

FLOO R

Mean 3.0253 44.1681 13.6529 22.3615 14.8447 3.0467 0.5

Standard Error 0.4170 1.9418 0.2327 1.6716 3.7145 0.3454 0.0410

Median 2.73 45.8 13.4225 14.71326 5.8145 1.545 0.5

Mode 0.32 63.39 14.6302 10.57 0.58 1.16

Standard

Deviation 5.1070 23.7826 2.8494 20.4724 45.4925 4.2301 0.5017

Sample Variance

26.081

1 565.6140 8.1191 419.1197 2069.571

17.894

0 0.2517

Kurtosis

11.002

5 -0.8912 47.8068 1.1795 43.1806

10.625

2 -2.0272

Skewness

-0.2415 -0.0470 4.8771 1.3063 5.8324 3.27 4.38E-17

Range 48.1 93.76 35.5236 92.15 439.71 22.46

Minimum -22.76 2.18 4.2364 0.02 -38.51 0.18

Maximum 25.34 95.94 39.76 92.17 401.2 22.64

Sum

453.79

9 6625.21

2047.940

4 3354.2212 2226.7037 457 75

Table 13: Variable descriptive statistic for Quarter of 2017 ROE

TDR

(%) SIZE

TANG (%)

GRO

(%) LIQ

FLOO R

Mean 2.2694 44.3575

13.635

4 22.3788 18.1541 3.3303 0.5

Standard Error 1.0748 1.9771 0.2267 1.6679 6.9970 0.4826 0.0410

Median 2.51 45.49 13.4654 14.635 7.0278 1.57 0.5

Mode 4.6 31.13 14.6735 1.65 0.3278 1.18

Standard

Deviation 13.1639 24.2139 2.7762 20.4281 85.6956 5.9105 0.5017 Sample Variance

173.287

7 586.3112 7.7074 417.3060 7343.7418 34.9336 0.2517

Kurtosis 109.6119 -0.7929 31.9597 1.1266 125.4816

27.513

0 -2.0272

Skewness

-9.5342 0.0412 3.2353 1.2654 10.7610 4.9119 4.4E-17

Range 183.03 94.71 32.3583 91.06 1050.07 42.98

Minimum

-145.92 1.57 4.1817 0.02 -33.96 0.47

Maximum 37.11 96.28 36.54 91.08 1016.11 43.45

Sum 340.41 6653.62

2045.316

1 3356.8223

2723.109

2 499.54 75

APPENDIX B: TEST MODEL’S RERULTS

NOTE: Model 1, model 4, model and model are chosen as the studied models in chapter 3, respectively model 1, 2, and

INTERCEPT

Model Model Model Model

Model Variable

TDR (%)

0.0201*

* 0.00905*** 0.0108** 0.0139**

0.1339

-2.3293 -2.61585 -2.5527 -2.4658 -1.5

SIZE

0.0001* **

0.0000009017* **

0.00000059372* **

0.0000012116* **

3.9038 4.94668 5.02965 4.8872

TANG

(%) 0.5904 0.54196 0.606 0.5062

0.2900

0.5384 0.61 0.51591 0.665

1.0585

GRO (%) 0.3091 0.41659 0.4233

0.2338

1.0177 0.81272 0.8012

1.1912

LIQ 0.2797 0.3567 0.362

-1.0815 -0.922 -0.912

FLOOR 0.0939* 1.6769

R Square 0.0343 0.0313 0.0305 0.0304

WITHOUT INTERCEPT

Model Model Model

Variable

TDR (%) 0.01127** 0.0031*** 0.0039***

-2.53933 -2.9624 -2.8856

SIZE 0.0000000663637** * 0.0000000000012759* ** 0.000000000000715** *

5.45435 7.2 7.283

TANG

(%) 0.69458 0.6589 0.743

0.39278 0.4415 0.3279

GRO (%) 0.25187 0.3568

1.14655 0.922

LIQ 0.15986 0.1996 0.2005

-1.4067 -1.2836 -1.281

FLOOR 0.041**

2.0456

R Square 0.19757 0.19382 0.193

CORRELATION Model +

ROE

TDR

(%) SIZE

TANG

(%) GRO (%) LIQ

FLO OR ROE TDR (%) -0.04406 SIZE 0.15221 08 0.21316 94 TANG (%) 0.02837 97 0.05616 25 0.083512 GRO (%) 0.03088 68 0.10826 67 0.093812 -0.108643 36 LIQ 0.00400 56 -0.44975 -0.025753 44 -0.073437 73 -0.025932 14

07

0.13248

16 17 91

0.103072

14 82

Model +

ROE TDR (%) SIZE TANG (%) GRO (%)

LI Q ROE TDR (%) -0.0440651 SIZE 0.1522108 0.2131693 TANG (%) 0.0283797 0.0561624

7 0.08351228

GRO (%) 0.0308868

0.1082666

5 0.09381264 -0.1086434

LIQ 0.0040056 -0.4497506 -0.02575344 -0.07343773 -0.02593214 Model +

ROE TDR (%) SIZE TANG (%) LIQ

ROE

TDR (%) -0.0440651

SIZE 0.15221084 0.21316936

TANG (%) 0.0283797 0.05616247 0.083512275

LIQ 0.0040056 -0.4497506 -0.02575344 -0.07343773 Model

ROE TDR (%) SIZE TANG (%) GRO (%)

ROE

TDR (%) -0.0440651

SIZE 0.15221084 0.21316936

TANG (%) 0.0283797 0.05616247 0.0835123

GRO (%) 0.0308868 0.10826665 0.0938126 -0.10864336 Model

ROE TDR (%) TANG (%) GRO (%)

ROE

TDR (%) -0.0440651

TANG (%) 0.02837969 0.056162474

8 Objectives: To map and explain the development of capital structure and firm operational efficiency in Viet Nam company during recent periods In detail, writer wants to study relationship between capital structure model and operational efficiency Then writer will help Viet Nam joint stock companies to find suitable capital structure

9 Method, Scope of study:

9.1 Method: Takes data in financial statements from about 120 Viet Nam joint stock companies during quarter ( 4/2016 – 9/2017)