The purpose of this study is to examine the impact of financial leverage on the profitability of listed steel companies in Vietnam using the financial data for the five-year period from
INTRODUCTION OF THE RESEARCH AND OVERVIEW OF THE
Introduction to the research
The steel industry is one of the essential industries for the development of any country Products of the steel industry are currently considered irreplaceable in many other industries such as construction, production, shipbuilding, etc Many of the developed countries have identified the steel industry as a key economic sector and made substantial investments in it
As a provider of input material for various industries, the growth of the steel industry and steel companies is heavily dependant on the growth of the overall economy During the Great Recession, the global steel demand had decreased 7.4% from 2007 to 2009, explained by the lower level of activities from the construction and production in the middle of the crisis After that, the rebound of the global economy has led to growth for the steel industry since 2010, yet this growth is considered not as robust and sustainable as for the period before the recession
Another notable characteristic of the steel companies is their heavy investment in non-current assets, which may include the factories, machinery, mining equipment, etc Joy Pathak (2010) indicated that there is a positive correlation between the proportion of non-current assets and the financial leverage of a company Therefore, it is predictable that steel companies usually have a higher proportion of debt in their financial structure than that of companies in the industries that do not require substantial investments in fixed assets Furthermore, the interest rate at which the company is able to borrow extra funds is determined by many other factors such as the fiscal policy, monetary policy,
The high level of financial leverage as well as the dependence on the overall economic status leads to serious concern to steel companies: How to manage its capital structure in general and its financial leverage in particular to maintain a high level of profitability? Keeping the financial leverage at a high level may help the company utilise the benefit of tax shield created and reduce the cost of capital; however, this may lead to
2 heavy financial distress costs, especially during the slow-growing periods, in which the companies may encounter to liquidity problems The inability to pay back the principal or interest of debt and to restructure the debts is likely to lead to the bankruptcy of the company
The purpose of this study is to examine the impact of financial leverage on the profitability of listed steel companies in Vietnam using the financial data for the five years from 2015 to 2019 More specifically, the following research questions will be addressed:
1 Does the financial leverage usage policy of a company affect its profitability ratios (return on assets, return on equity, return on sales, and return on capital employed)?
2 If yes, is the correlation positive or negative?
All of this is to generate recommendations for processes relevant to determining an optimal capital structure for a steel company or making decisions regarding the method of raising additional funds for listed steel companies in Vietnam.
Overview of the steel industry in Vietnam
1.2.1 The historical development of the steel industry in Vietnam
The construction of Thai Nguyen Iron and Steel Complex in the early 1960s marked the beginning in the history of the Vietnamese steel industry, with the production of the first cast iron batch three years later However, due to tremendous difficulties in the socio- economic conditions then, the Complex could only produce their first processed product – rolled steel – in 1975, when Gia Sang Rolled Steel Factory was built with the help of East Germany The total designed capacity of the Complex was 100,000 tonnes per annum then
In the Southern region, after the Resistance War Against America, in 1976, the Southern Black Metallurgical Company was established on the basis of taking over iron and steel factories in Bien Hoa (VICASA) and Ho Chi Minh City (VIKIMCO) with the total designed capacity of 80,000 tonnes per annum
Despite having factories with a total capacity of about 180,000 tonnes per annum, the steel industry of Vietnam did not recognise any considerable growth during the post-
3 war reconstruction period (1976 – 1989) This reflected a part of a negative-to-slow growth in both total national output as well as in agricultural and industrial production, caused by both bureaucratic mismanagement and post-war embargo from other countries In this period, the majority of steel demand is fulfilled by imported products from The Soviet Union and other Eastern European countries Total production remained at 40,000 – 85,000 tonnes per annum, significantly lower than the designed capacity
After the Economic Reforms (Doi Moi) in 1986, the steel industry became a focus of the industrial sector and since then experienced a more robust growth than the previous periods The establishment of the Vietnam Steel Corporation in 1990 had marked a milestone for the development of the steel industry The mission of the corporation was to ensure the congruence in the management of steel companies, which were mostly state- owned enterprises then From 1990 to 2000, many investment projects and joint ventures with foreign partners were implemented, notably the establishment of four joint ventures in 1996: Vietnamese – Japanese Steel Company (Vinakyoei), Vietnamese – Australian Steel Company (Vinausteel), Vietnamese – Korean Steel Company (VPS) and Vietnamese – Singaporean Steel Company (Nasteel) Total national production capacity surged to over 450,000 tonnes per year in 1995 and 1,570,000 tonnes per year in 2000, satisfying nearly
With the growth of the Vietnamese economy during the first decade of the twenty- first century, the steel industry also witnessed rapid development in this period Due to the impacts of economic integration policies, Vietnam has become one of the potential destinations to attract many investment projects from foreign partners Accordingly, the demand for construction steel as well as the steel used in other industries had surged, with a compound annual growth rate of 9.1 percent per annum The supply of some particular products increases rapidly and could almost replace imported products However, Vietnam still had to import some products, especially flat bars which could not be produced domestically then and accounted for 70% of the total value of imported steel products
After the year 2010, despite being adversely affected by the Financial Crisis and Great Recession, Vietnamese steel companies still experienced a growth rate of 7-8% per annum in the period 2010 – 2015 The total production capacity for construction steels had
4 met total domestic demand However, some kinds of raw steel materials and some particular types of products still could not be produced domestically and still have to be imported In 2019, the total amount of steel production reached 25.2 million tonnes, indicating a 4.4% increase in comparison with the year 2018 (Vietnam Steel Association,
The number and size of steel quantities also rapidly increase along with the development of the industry As of 2016, there are more than 100 steel companies in Vietnam (Vietnam Steel Association, 2020) with 26 listed companies on Hanoi Stock Exchange, Ho Chi Minh City Stock Exchange, and UPCoM (Cophieu68.vn, 2020)
1.2.2 Characteristics of steel companies in Vietnam
Firstly, steel companies require large capital investment with a slow payback period
The steel industry is often considered as one of the industries that require the largest amount of initial investment for construction of factories and purchase of machinery and equipment Some notable steel projects with large investments include Son Duong Formosa Ha Tinh Iron and Steel Complex (10.5 billion USD – phase 1) and Hoa Phat Dung Quat Iron and Steel Factory (2.3 billion USD – phase 1 and 2.6 billion USD – phase 2) Even small and medium sized projects still require the initial investment from tens to hundreds of millions of USD The construction period, however, is often lengthy, with only limited capacity expected to be delivered at the beginning of the production phase, leading to a long payback period Furthermore, the amount of working capital required is often high, as the value of inventory and amount receivables are both high Therefore, it is necessary for steel companies to maintain an adequate amount of long-term capital Besides owners’ equity, steel companies usually have to finance their investments and operations with debts
Secondly, steel companies are exposed to high business risk
Steel companies are exposed to high business risks from some factors Large investment in fixed assets leads to a high fixed depreciation charge to the company’s operating expenses or higher operating leverage, which causes high sensitivity of the
5 company’s profit to the sales level Prices of inputs for the production process (including the price of iron ore, scrapped steel, fuel) and foreign exchange rate can fluctuate wildly, affecting the costs of goods sold, especially for companies that are dependant on sources of imported materials The level of sales may be affected by the cycle of other industries such as construction and production Lastly, if a company wants its products to be outstanding in terms of quality and production cost, it has to invest heavily in state-of-the- art technologies, which again create a risk regarding the planning and implementation of these technologies
With the high business risk arising from the nature of the business, steel companies have to make decisions on an appropriate level of using financial leverage (which makes the company exposed to financial risk), hence keep the total risk at a reasonably low level that is acceptable for the shareholders
Thirdly, steel companies can take advantage of the economies of scale
In general, the steel production process consists of two main phases: refining and rolling Only companies with advantages on the material sources and capital investment are able to carry out both these phases leverage the economies of scale and gain a high profit margin Small and medium sized producers usually lack necessary equipment and expertise to carry out both two phases; they only participate in one phase or some small parts of each phase, with little value added Another disadvantage of small steel companies is that they can also face difficulties while raising funds for investments and operation if they cannot prove the potential profitability and value added by the additional funding
THEORETICAL BACKGROUND AND LITERATURE REVIEW
Overview of financial leverage and key profitability ratios
Financial leverage is a term used to characterise the capital structure of a company
It is usually calculated as:
Financial leverage = Total liabilities / Total assets (2.1)
The financial leverage reflects the relationship between the liabilities and the owners’ equity in a company, or the management’s policy of using debt to finance for the business This ratio usually differs between companies and industries and is also greatly affected by external factors of the economy Whether there is an ‘optimal’ financial leverage for each company is still a controversial issue and has been extensively studied recently Table 2.1 demonstrates the difference between the use of financial leverage between industries in the U.S economy in 2008
Table 2.1 Financial leverage of six industries in the U.S., 2008
Industry Financial leverage (total liabilities/ total assets) (%)
Source: MSN Money; http://moneycentral.msn.com
Brigham & Houston (2007) identified a checklist for capital structure decisions which includes the factors that are generally considered by a firm when making financing decisions These factors are:
Sales stability A company with a stable level of sales can safely take on more debt and incur higher interest charges than those with unstable sales in nature
Asset structure Companies that have a high proportion of fixed assets for general use are accessible to a greater level of loans as their assets are considered to be more suitable as security for loans and can make better collateral then assets used for specific purposes
Operating leverage Other things equal, companies with a lower level of operating leverage are exposed to a lower level of business risk, hence are able to employ higher financial leverage
Growth rate Other things equal, companies that are growing at a higher pace are usually more reliant on external capital, however, they also face higher uncertainty, which leads to more cautious use of debt
Taxes Interest expenses provide a ‘tax shield’ for companies that use debt to finance their business, which will be most valuable to companies with a high tax rate
Control and management attitudes It depends on the control position of the management and their ‘risk appetite’ to determine a company’s policy on the use of debt The preference may vary from situation to situation For example, if a company is facing solvency problems, its manager may want to reduce the use of debt to avoid the risk of default, which is likely to lead to their loss of jobs
Lender and rating agency attitudes Lenders and rating agency usually have their own criteria when assessing the possibility that a company can pay back their principal and interest on time and in full The risk of being downgraded in credit rating or risk of default of loan contract conditions may influence a company’s financing decision
Market conditions The condition of the economy and the financial market may largely impact a company’s financial leverage For example, a higher interest rate may imply a higher fixed interest income, which prevents a company from borrowing from a bank or issuing additional bonds on the market
The ‘bottom line’, or the net income of a company, is one of the most important indicators that attracts the attention of various stakeholders, as it reflects the net impacts of
8 all financing policies and operating decisions Profitability ratios are usually used to measure the profitability of a company, or how efficiently a firm uses its assets and manages its operation
Return on equity (ROE) is the ratio of net income to total owners’ equity of the company It measures how effectively the company is generating income from the equity investments of its shareholders It is calculated as:
ROE = Net income / Average total equity (2.2)
Mathematically, ROE presents the number of dollars generated by a company for every dollar on equity Normally, it can be used to compare a company to its competitors and the overall industry
Return on assets (ROA) is the ratio of net income to total assets of the company It gives an idea as to how efficient a company’s management is using its assets to generate earnings It is calculated as
ROA = Net income / Average total assets (2.3)
Higher ROA indicates more asset efficiency, therefore the higher ROA the better ROA is also highly dependent on the industry, so it is used mostly for comparison against a company’s historical data or against a similar company in the same industry
It is noticeable that total assets equal total liabilities plus total equity and the total liabilities of a company cannot be less than zero, therefore the denominator in formula (2.2) is smaller than the denominator in formula (2.3), hence ROA is lower than ROE This fact reflects the use of financial leverage in a company
Return on sales (ROS), or more commonly known as the net profit margin, is calculated by dividing net income by sales
ROS represents the number of dollars generated by a company for every dollar in sales Other things held constant, a higher return on sales is often considered better However, stakeholders need to be aware of the possibility that the company is sacrificing its sales quantity to get a higher ROS For example, if products are sold at a very high price, the company may get a high return on each sale but fail to make many sales This strategy may result in a high ROS, however, low sales and low net income
Return on capital employed (ROCE) is a ratio used to measure the capital efficiency of a company, that is, how well a company is generating profit from its capital ROCE can be calculated as
ROCE = Earnings before interest and tax / Average capital employed (2.5)
Unlike the three previous ratios, ROCE uses the earnings before interest and tax (EBIT) instead of net income after-tax to measure the profitability of the company The
‘capital employed’ in the denominator represents the long-term financing of the company, which can be calculated by subtracting current liabilities from total assets, or by adding long-term liabilities and owners’ equity The formula takes into account the use of both debt and equity, therefore help neutralise financial analysis for companies with significant debt (which might possess a relatively high ROE) Generally, companies with higher ROCE are considered more favourable by investors as they can employ capital in an efficient manner and generate higher profit.
Literature review on the relationship between financial leverage and profitability 9 1 Modigliani and Miller (M&M) Theory
Modigliani and Miller (1958) were amongst the first people to conduct researches on the capital structure of companies and their impacts on the valuation and profitability of a company The most simple M&M no-tax model was built on the assumption that companies are operating in a perfectly efficient financial market, i.e
There are no corporate taxes;
The trading of securities is executed without any transaction costs;
There are no financial distress costs;
Debts at risk-free rate are available and ready for loan; and
In this no-tax model, M&M Proposition I implies that the value of the company is independent of its capital structure As the level of financial leverage (or the use of debt) increases, the cost of debt remains constant, and the cost of equity rises in such a way that keeps the weighted average cost of capital constant M&M Proposition II no-tax model
10 further developed the conclusion of Proposition I, providing a formula indicating the positive linear correlation between the required rate of return of the company’s assets (RA), its cost of debt (RD) and its debt-equity ratio (D/E):
In other words, in a perfect capital market with no corporate taxes, there is no
‘optimal’ financial leverage for a company to maximise its value However, the required rate of return of the owners’ equity is positively correlated to the debt-equity ratio
When taking corporate taxes into consideration, Modigliani and Miller (1963) made a comparison between a leveraged company and an unleveraged company with identical assets In this follow-up study, they took account of the benefits from the tax shield created by the use of debt As the interest paid on debt is tax-deductible, it can lower the amount of corporate tax paid by the company, and create an interest tax shield (the tax saving attained by a company from its interest expense) Assuming the debt is perpetual and a constant amount of tax shield is generated perpetually, M&M Proposition I with corporate taxes states that the value of a leveraged company exceeds the value of an unleveraged company with identical assets by the present value of the interest tax shield:
Whereas: VL, VU is the value of the leveraged and unleveraged companies, respectively;
TC is the tax rate and D is the amount of debt of the leveraged company An empirical result by Kester (1986) approved this theory: benefit from the tax shield might increase the company value by an average of 7 percent Furthermore, as the deductible interest expense creates a tax shield for the company, the real cost of debt is now only (1 – TC) times of the cost of debt should there be no corporate tax Taking this into account, the weighted average capital cost of a leveraged company can be calculated as:
This implies the formula of M&M Proposition II with corporate taxes on the required rate of return for equity of a leveraged company:
RE = RA + (RA – RD) × (D/E) × (1 – TC)
The company is, therefore, better off with debt, and the optimal capital structure is
Regardless of the existence of corporate taxes, the required rate of return on equity (i.e the cost of equity) is positively correlated with the debt-equity ratio, and hence, the financial leverage of the company This might encourage companies to increase the use of debt to utilise the benefit created by the tax shield
However, the underlying assumptions of M&M Theory are often considered unrealistic Therefore its application to practical situations is often limited In reality, when the company uses a high level of debt, financial distress costs will arise The term financial distress costs refer generically to the direct and indirect costs associated with going bankrupt or avoiding a bankruptcy filing Direct bankruptcy costs are directly associated with bankruptcy, such as legal and administrative expenses Indirect bankruptcy costs are the costs of avoiding a bankruptcy filing incurred by a financially distressed company, for example, a higher cost of debt due to a company’s high risk of default, a higher price or shorter payment terms from suppliers who will have concerns about the risk that a company with high financial leverage may not be able to pay its suppliers, etc When the financial distress costs exceed the benefits generated by the interest tax shield, the value of the company starts to decline This is a limitation of the M&M Theory and has initiated other studies on the capital structure of a company
On the basis of M&M Theory, Klaus and Litzenberger (1973) took into consideration the impacts of corporate taxes and financial distress costs to determine the optimal capital structure for a company Their static trade-off theory states that the company should gear up to take advantage of the tax shield, but only to the point that the tax benefit from an extra dollar in debt is exactly equal to the cost that comes from the increased probability of financial distress, i.e the marginal benefits exceed the marginal costs of financial distress After this point, the market value of the firm will start to fall and its weighted average cost of capital will start to rise
When an unleveraged company starts to raise fund by issuing debt instruments, the company faces little solvency problems and benefit from the interest tax shield and the lower cost of debt in comparison with equity, and the benefits at this point outweigh the
12 financial distress costs However, at a higher level of financial leverage, the company might face a higher risk of going bankrupt, and an extra dollar in debt will result in higher financial distress costs than a benefit The shareholders might also require a higher rate of return as their equity is exposed to greater financial risk The static trade-off theory implies that there is a point where the marginal cost and marginal benefit from an extra dollar in debt, and at this point, the optimal capital structure of a company is established
The aforementioned theories consider the effects of tax and financial distress costs when using debt to finance for a company in three different cases The simplest case, M&M Theory without tax, implies that the financial leverage does not affect a company’s value When taking corporate tax into consideration, M&M Theory illustrates the positive correlation between the level of financial leverage and the company’s value as the company can benefit from the interest tax shield, hence encourage companies to use debt as much as possible In both of these cases, the required rate of return of equity is positively correlated with the debt-equity ratio The most complex case where both corporate tax and financial distress costs are taken into account is considered in the static trade-off theory, implying the company should only borrow to the extent that the marginal financial distress cost of debt does not outweigh its marginal benefit from tax shield These theories seek an optimal capital structure to finance the company’s investments and operations However, there is another theory that negates the existence of such capital structure and focuses on the preference of management towards the different sources of finance, the Pecking order theory
The Pecking order theory is an alternative to the Static trade-off theory Initiated by Donaldson (1961) and further developed by Myers and Majluf (1984), the theory implies that the company will seek additional finance in an order of preference, or ‘pecking order’, which is (1) internal sources of finance (e.g retained earnings), (2) debt and (3) new equity share issuance
Internal financing is often preferred as raising cash through securities can be expensive and take a lot of effort The company also does not have to live up to the demands
13 of external finance providers If a firm is very profitable and has a strong cash flow situation, it might need no borrowing at all and eventually end up with little or no debt
Comparing between issuance of debt and new equity, debt is often preferred A reason is the information asymmetry: managers of a company usually know a lot of information that is not known to the public, so the issuance of securities has a signaling effect on the fund providers Debt issues can be a sign of confidence, that the company has the capability of making sufficient profits to fulfil their obligations, and that shares are overvalued Managers can anticipate this great potential from internal information By contrast, equity issues can be a sign showing that managers know the information which makes them believe that the company’s share is currently overvalued
EMPIRICAL MODELS, HYPOTHESIS AND RESEARCH METHOD 18 3.1 Empirical models
Hypothesis
Based on the overview of theoretical matters and literature review, the author proposes the following research hypothesis
Hypothesis 1: Financial leverage is negatively correlated with ROA
Theoretically, for two companies with the same assets value, the higher the level of financial average, the more interest expense it has to pay There has to be an increase in profit to compensate for the net effect of the interest expense to the company’s bottom line (after taking into account the interest tax shield), otherwise, the profit will decrease, and ROA will fall Many empirical studies supported this hypothesis, including Ebaid (2009) for Egyptian companies, Sheikh and Wang (2013) for non-financial listed companies in Pakistan, Ahmad et al (2015) for cement companies in Pakistan, Nhu (2017) for construction companies in Vietnam, Dahiru et al (2016) for manufacturing companies in Nigeria, Cong et al (2019) for real estate companies in Vietnam
Hypothesis 2: Financial leverage is negatively correlated with ROE
The Pecking order theory indicated that if a company is profitable enough and generates a sufficient amount of cash internally, it will prioritise using internal finance to fund its investments and operations rather than using external finance like debt The use of financial leverage also brings about financial distress cost to the company, increasing its financial risk In a developing market like Vietnam where the interest rate is rather high, it may lead to difficulties for companies fulfil the financial obligation to the lenders Empirical studies with similar results might include the studies of Shubita and Alsawalhah
(2012) for listed companies in Jordan, Duong and Hung (2014) for listed companies in Vietnam, Nhu (2017) for construction companies in Vietnam However, this result contradicts the studies of Gill (2011) for public companies in the U.S., Ebrati et al (2013) for listed companies in Iran, and Cong et al (2019) for real estate companies in Vietnam
Hypothesis 3: Financial leverage is negatively correlated with ROS
Berzkalne (2014) identified a negative correlation between financial leverage and ROS for companies in Latvia The study of Nhu (2017) on listed construction companies in Vietnam made the same conclusion, while Cong et al (2019) found a different result: there was no statistically significant correlation between the two variables for real estate companies in Vietnam
Hypothesis 4: Financial leverage is negatively correlated with ROCE
There were only a few previous researches on the relationship between financial leverage and ROCE Cong et al (2019) did not find any statistically significant impact of financial leverage on ROCE of listed real estate companies in Vietnam.
Research method
Data used in this research are collected from the audited financial statements of listed steel companies on the Ho Chi Minh City Stock Exchange (HOSE), Ha Noi Stock Exchange (HNX) and UPCOM As of 31 December 2019, there were a total of 26 listed steel companies in Vietnam, in which 9 were listed on HOSE, 4 were listed on HNX and
13 were listed on UPCOM (cophieu68.vn, 2020) Name of these companies are provided in the table below:
Table 3.2 List of Vietnamese listed steel companies as at 31 December 2019
Exchange English Name Vietnamese Name
1 DTL HOSE Dai Thien Loc Joint Stock
Công ty Cổ phần Đại Thiên Lộc
2 HMC HOSE Ho Chi Minh Metal
Công ty Cổ phần Kim khí Thành phố Hồ Chí Minh - VNSTEEL
3 HPG HOSE Hoa Phat Group Công ty cổ phần Tập đoàn
4 HSG HOSE Hoa Sen Group Công ty Cổ phần Tập đoàn
5 NKG HOSE Nam Kim Steel Joint Stock
Công ty Cổ phần Thép Nam Kim
6 POM HOSE Pomina Steel Corporation Công ty Cổ phần Thép
7 SMC HOSE SMC Trading Investment
Công ty Cổ phần đầu tư thương mại SMC
8 TLH HOSE Tienlen Steel Corporation
Công ty Cổ phần Tập đoàn thép Tiến Lên
9 VIS HOSE Vietnam - Italy Steel Joint
Công ty Cổ phần Thép Việt Ý
10 KKC HNX KKC Metal Joint Stock
Công ty Cổ phần Kim khí KKC
11 KMT HNX Central Vietnam Metal
Công ty Cổ phần Kim khí Miền Trung
12 SSM HNX Steel Structure Manufacture
Công ty Cổ phần Chế tạo kết cấu thép VNECO.SSM
13 VGS HNX Vietnam Germany Steel
Công ty Cổ phần Ống thép Việt Đức VG PIPE
14 BVG UPCOM Bac Viet Steel Joint Stock
Công ty Cổ phần Thép Bắc Việt
15 DNS UPCOM Da Nang Steel Joint Stock
Công ty Cổ phần Thép Đà Nẵng
16 DNY UPCOM Dana - Y Steel Joint Stock
Công ty Cổ phần Thép Dana
17 HLA UPCOM Huu Lien Asia Corporation Công ty Cổ phần Hữu Liên Á Châu
18 TDS UPCOM Thu Duc Steel Joint Stock
Công ty Cổ phần thép Thủ Đức
19 TIS UPCOM Thai Nguyen Iron and Steel
Công ty Cổ phần Gang thép Thái Nguyên
20 TNB UPCOM VNSTEEL - Nha Be Steel
Công ty Cổ phần Thép Nhà
21 TNS UPCOM Thong Nhat Flat Steel Joint
Công ty Cổ phần Thép Tấm
22 TTS UPCOM Thai Trung Steel Joint Stock
Công ty cổ phần Cán thép Thái Trung
23 TVN UPCOM Viet Nam Steel Corporation Tổng công ty Thép Việt
24 VCA UPCOM VNSTEEL - VICASA Joint
Công ty Cổ phần Thép VICASA - VNSTEEL
25 VDT UPCOM Binh Tay Steel Wire Netting
Công ty Cổ phần Lưới thép Bình Tây
Meca Vneco Investment and Electricity Construction Joint Stock Company
Công ty Cổ phần Đầu tư và Xây dựng điện Mê Ca Vneco
Source: Websites cophieu68.vn, vndirect.com
The categorisation of companies was based on the data from website cophieu68.vn After identifying listed steel companies, the author collected the audit financial statements of these companies for a period of five years from 2015 to 2019 and calculated the required input information for the research The result is then compared against the key financial ratios calculated by websites cophieu68.vn and vietstock.vn to check whether there were any mistakes or mismatch The detailed data for each company in each year can be found in Appendix A
After confirming the accuracy of the data, the author uses the Interquartile rule to detect outliers in the data set following these steps:
Step 1: Identify the first quartile Q1 and the third quartile Q3 of each of the data set of the companies: ROA, ROE, ROS, ROCE The first quartile (Q1) is the value such that a quarter of values in the data set are smaller than Q1, while the third quartile (Q3) is the value such that a quarter of values in the data set are larger than Q3
Step 2: Calculate the interquartile range: IQR = Q3 – Q1
Step 3: Calculate the lower and upper bound of the valid range This follows a rule of thumb that identified values that are more than 1.5 times of IQR lower than Q1 or higher than Q3 can be considered as outliers of the data set The formulae are as follows:
Step 4: Remove values that are outside of the valid range, that is, smaller than the lower bound or higher than the upper bound If a value is identified as an outlier, other data of the same company in the same year will also be removed so as to guarantee the same number of observations for both models
After identifying and removing the outliers from the data set, the author was left with 100 observations for each model
In this research, the author uses descriptive analysis and regression analysis to process the data
This method is used to quantitatively describe and summarise basic information about the characteristics of the data set The author uses Microsoft Excel Office 365 to conduct descriptive statistics analysis with the following steps:
Step 1: Calculate basic quantitative characteristics of each data set (each variable is counted as one data set) such as mean, standard error, median, mode, standard deviation, sample variance, range, etc This provides an overview of the current situation of companies in this research regarding their use of financial leverage and their profitability
Step 2: Calculate the correlation to understand the interdependence between variables It is important to note that a high correlation does not necessarily mean a causal relationship between the two variables
After conducting descriptive statistics analysis, the author conducted regression analysis with linear models mentioned in part 3.1 to quantify the relationship between variables in the empirical models The steps conducted in STATA are as follows:
Step 1: Choose the regression method used;
Step 2: Test for possible problems with the chosen model after step 1 (multicollinearity, heteroskedasticity, etc.) and make corrections if necessary;
Step 3: Estimate the value of the regression coefficient of independent variables in the empirical models;
Step 4: Test the statistical significance of the model
From the result of regression analysis, the author will make comments regarding the results and recommendations to the companies in the research
EMPIRICAL RESULTS ON THE IMPACT OF FINANCIAL LEVERAGE
Descriptive statistic analysis
Table 4.1 summarises some key quantitative characteristics regarding the use of financial leverage and profitability ratios of listed steel companies in Vietnam in the period from 2015 to 2019 Figure 4.1 presents the trend of the average of key profitability ratios during the period
ROA ROE ROS ROCE FL SIZE SG
Variance 0.0025 0.0131 0.0008 0.0140 0.0310 2.9795 0.0851 Coefficient of variation 1.4795 1.4733 2.0172 0.8080 0.2867 0.1179 2.4418 Kurtosis 0.7267 0.2055 1.0444 -0.4364 -0.2894 3.2280 3.9967 Skewness 0.4317 -0.3240 -0.3234 0.1786 -0.6175 -1.2487 0.8055 Range 0.2637 0.5388 0.1619 0.5247 0.7753 10.5174 2.1620 Minimum -0.0852 -0.2258 -0.0752 -0.1104 0.1859 6.8373 -0.7958 Maximum 0.1785 0.3130 0.0867 0.4143 0.9612 17.3548 1.3662 Source: Compiled by the author with Microsoft Excel Office 365
Figure 4.1 Average profitability ratios of listed steel companies in Vietnam, 2015 – 2019 (unit: %)
It can be seen from the Figure that steel companies in Vietnam witnessed greater business performance in the period of 2015 to 2017 with improving key profitability ratios The growth, however, was slower in the second year, signalling a downturn in the following period Especially in 2018, the average ROE and ROCE decreased sharply from 14% and 20% to 6.7% and 13.7% respectively Overall, at the end of the five year period, all the average profitability ratios were lower in comparison with the beginning of the period According to the Vietnamese Steel Association, due to the fall in global demand and the fluctuation of steel price, revenue and profit of most steel companies declined in the years 2018 and 2019, leading to a downward trend in profitability
The average ROE of listed steel companies in this research during the period from
2015 to 2019 is about 7.8%, which is substantially lower than the average of other industries According to the website cophieu68.vn, steel is one of the five industries with the lowest ROE amongst 25 industries of listed companies in 2019 The same result applies to ROA as the average ROA of steel companies in 2019 was only higher than the average ROA of service companies and mineral companies The mean ROA amongst 100 observations is only 3.4% ROA and ROE also have a similar coefficient of variation at about 1.47, which, as a rule of thumb, can be considered as highly variable ROA ranges from negative 8.52% to positive 17.85%, while ROE ranges from negative 22.58% to
26 positive 31.3% This reflects the great difference between listed steel companies A report from website vietnambiz.vn indicated that big corporations such as Hoa Phat Group, Hoa Sen Group and VNSteel have much greater financial performance during the period, especially when it comes to a difficult time as in the years 2018 and 2019
ROS, or net profit margin, has the lowest mean value amongst the four profitability ratios, at 1.41%, and ranges from negative 7.52% to positive 8.67% This ratio stayed at a very low level, even in the year 2016 and 2017 when the market condition was favourable to steel companies This is due to the nature of steel industry where profit depends largely on the fluctuating material price and a substantial amount of depreciation costs (Vietinbank, 2019) Meanwhile, ROCE has the highest mean value of 14.63% and the lowest coefficient of variation amongst the four ratios
Figure 4.2 Average financial leverage of listed steel companies in Vietnam, 2015 – 2019 (unit: %)
Figure 4.2 exhibits the trend of average financial leverage of 100 observations during the study period It can be seen that the average financial leverage remained stable over five years, at about 60% However, the use of debt varies from company to company, with a minimum total debt to assets ratio of 18.59%, when the maximum is 96.12%, which means that the debt value is 24 times the equity value
Table 4.2 The correlation between dependent and independent variables
ROE ROCE ROA ROS FL SIZE SG
SG 0.2021 0.1337 0.0909 0.1731 0.1720 0.1522 1.0000 Source: Compiled by the author with Microsoft Excel Office 365
Correlation analysis is used to evaluate the strength of the relationship between two quantitative variables The correlation coefficient ranges between -1 and +1, the magnitude of which indicates the strength of association In Table 4.2, profitability ratios of the companies can be considered as highly and positively associated This can be explained as both these ratios are indicators of a company’s financial performance during the study period The strongest correlation is between ROE and ROA, with a correlation coefficient of 0.889 Meanwhile, the association between these ratios and the financial leverage is weaker and negative
The financial leverage has negative correlations with all four profitability ratios, however, these correlations are not strong
Control variables have weak correlations with other variables When most correlations are positive, there is a weak negative correlation between the size of the company (represented by the natural logarithm of sales value) and the ROA.
Regression analysis
There are a few regression methods to process panel data that are collected for different units in different time The most simple method is the ordinary least square method (OLS), which assumes no difference between the intercept of the model (β0) for each unit However, this method is usually considered as oversimplified and may create inaccurate results The author decides not to use the OLS method in this research
Other commonly used regression methods take into account the effect of individuality on the intercept of the model, including the fixed effects model (FEM) and the random effects model (REM) FEM assumes that the unobserved time-invariant individual effects, denoted by ɛi, have no correlation with the dependent variables across all time period, while REM assumes the contrary Hausman test helps in determining which between random effect model (REM) and fixed effects model (FEM) is the most appropriate for the study data The hypotheses of Hausman test are:
H0: ɛi and the independent variables are not correlated (therefore REM is more appropriate)
H1: ɛi and the independent variables are correlated (therefore FEM is more appropriate)
The detailed test result is presented in Appendix B Summary of the results of Hausman test is presented in Table 4.3 All the p-values are smaller than the critical value of 0.05 (at a confidence level of 95 per cent) It rejects the null hypothesis for all data sets, which means that ɛi and the independent variables are correlated, and FEM is the more appropriate method between FEM and REM
Table 4.3 P-value of Hausman test
Model 1: ROAi,t = β0 + β1FLi,t + β2SIZEi,t + β3SGi,t + ui,t 0.0036 FEM
Model 2: ROEi,t = β0 + β1FLi,t + β2SIZEi,t + β3SGi,t + ui,t 0.0001 FEM
Model 3: ROSi,t = β0 + β1FLi,t + β2SIZEi,t + β3SGi,t + ui,t 0.0000 FEM
Model 4: ROCEi,t = β0 + β1FLi,t + β2SIZEi,t + β3SGi,t + ui,t 0.0036 FEM
4.2.2 Testing for possible problems with the model
4.2.1.1 Testing for heteroscedasticity – Modified Wald test
The Modified Wald test is a statistical test used in STATA that helps to determine the groupwise heteroscedasticity in the residuals of a fixed effect regression model If a model fails the Modified Wald test, the FEM estimator is still consistent and unbiased but it is no longer the most efficient in the class of linear estimators The hypotheses of White test are:
H0: Variance of the errors in the model is constant (homoscedastic)
H1: Variance of the errors in the model is not constant (heteroscedastic)
At a confidence level of 95%, if P-value of the test is smaller than 0.05, then the null hypothesis is rejected and the model is determined to have heteroscedasticity Otherwise, the error terms are deemed to have constant variance (homoscedasticity)
The detailed test result is presented in Appendix C Table 4.5 summarises the result of Modified Wald test for four models
Table 4.4 P-value of Modified Wald test
Model 1: ROAi,t = β0 + β1FLi,t + β2SIZEi,t + β3SGi,t + ui,t 0.0000 Heteroscedasticity Model 2: ROEi,t = β0 + β1FLi,t + β2SIZEi,t + β3SGi,t + ui,t 0.0000 Heteroscedasticity Model 3: ROSi,t = β0 + β1FLi,t + β2SIZEi,t + β3SGi,t + ui,t 0.0000 Heteroscedasticity Model 4: ROCEi,t = β0 + β1FLi,t + β2SIZEi,t + β3SGi,t + ui,t 0.0000 Heteroscedasticity Source: Compiled and summarised by the author with STATA
All four is determined to have heteroscedasticity This problem can be dealt with by using robust standard errors The original OLS method assumes that errors are both independent and identically distributed, robust standard errors relax either or both of those assumptions In STATA, the author uses the robust option to relax the assumption that the errors are identically distributed The coefficient estimates do not change in comparison with the original way, but the standard errors and significance tests will change
4.2.1.2 Testing for autocorrelation – Wooldridge test
Autocorrelation, or serial correlation, refers to a high degree of correlation between different values of the same variables across different observations of the data Autocorrelation will make the estimators of the model no longer BLUEs (Best Linear Unbiased Estimator), and the estimation of confidence intervals and hypothesis testing will not be reliable Drukker (2003) introduced the application of a testing method by Wooldridge (2002) in STATA to test for autocorrelation in a FEM or REM model The hypotheses of Wooldridge test are:
H0: There is no autocorrelation or serial correlation in error terms
H1: There is autocorrelation or serial correlation in error terms
The detailed test result is presented in Appendix D Table 4.5 summarises the result of the Wooldridge test for four models
Table 4.5 P-value of Wooldridge test
Model 1: ROAi,t = β0 + β1FLi,t + β2SIZEi,t + β3SGi,t + ui,t 0.0002 Autocorrelation Model 2: ROEi,t = β0 + β1FLi,t + β2SIZEi,t + β3SGi,t + ui,t 0.0000 Autocorrelation Model 3: ROSi,t = β0 + β1FLi,t + β2SIZEi,t + β3SGi,t + ui,t 0.0003 Autocorrelation Model 4: ROCEi,t = β0 + β1FLi,t + β2SIZEi,t + β3SGi,t + ui,t 0.0002 Autocorrelation Source: Compiled and summarised by the author with STATA
The test results show that there is autocorrelation problem in all four models This problem can also be dealt with by using robust standard error (Dong & Minh, 2012) with the robust option in STATA
4.2.1.3 Testing for multicollinearity – Variance Inflation Factor
Multicollinearity refers to the case in which two or more independent variables in the regression model are highly correlated, making it difficult or impossible to isolate their individual effects on the dependent variable The variance inflation factor (VIF) identifies correlation between independent variables and the strength of that correlation The VIF for a dependent variable is calculated from the R-sqaured value obtained by regressing that variable on the remaining explanatory variables:
A value of 1 indicates that there is no correlation between this independent variable and any others VIFs between 1 and 5 suggest that there is a moderate correlation, but it is not severe enough to warrant corrective measures VIFs greater than 5 represent high levels of multicollinearity where the coefficients are poorly estimated, and the p-values are questionable
A high level of multicollinearity does not necessarily result in a biased or inefficient estimator, but the estimator might become extremely sensitive to any changes in data
Another consequence is that the standard errors of the affected coefficients tend to be large, and the test of the statistical significance of that variable might be untrustworthy
The value of the variation inflation factor of each variable in each model are presented in Table 4.6 and Appendix E None of the observed VIF exceeds 5 Hence there is no multicollinearity problem with the models
Dependent variable FL SIZE SG
Source: Compiled and summarised by the author with STATA
4.2.2.1 Model 1 – ROA (ROA i,t = β 0 + β 1 FL i,t + β 2 SIZE i,t + β 3 SG i,t + u i,t )
The result in Appendix F shows a negative impact of financial leverage on ROA The coefficient of FL in model 1 is estimated to be -0.3444, which means one percent increase of financial leverage will lead to an average decrease of 0.3444 percent of ROA This result is statistically significant at the confidence level of 95% The R-squared is 0.4738, which means about 47.38 percent of the change of ROA can be explained by the change of variables in the model So for listed steel companies in Vietnam, the more debt they use, the lower their ROA becomes This result is similar to the research hypothesis and the empirical results in the studies of Ebaid (2009), Sheikh and Wang (2013), Ahmad et al (2015), Nhu (2017), Dahiru et al (2016) and Cong et al (2019)
The coefficient of the variable SIZE is -0.0386, with a P-value of 0.03, which means that this variable has a statistically significant negative impact on the ROA of the company Meanwhile, the coefficient of the variable SG is 0.0615, with a P-value of 0.012, which means that the sales growth percentage has a statistically significant positive impact on the ROA
4.2.2.2 Model 2 – ROE (ROE i,t = β 0 + β 1 FL i,t + β 2 SIZE i,t + β 3 SG i,t + u i,t )
CONCLUSION AND RECOMMENDATION
Summarisation of empirical results
From the research results in chapter 4, it is noticeable that, on average, listed steel companies in Vietnam is currently maintaining a rather high level of financial leverage (at about 60%), while having lower profitability than most of the other industries on the stock exchange The regression analysis also identifies negative impacts of using financial leverage on all four profitability ratios (ROA, ROE, ROS, ROCE) The magnitude of coefficients of the independent variable in four models ranges from about 0.17 to 0.60, indicating that one percent of increase in total debt to assets ratio can result in a decrease of 0.17 to 0.60 percent of these ratios ROE is the most sensitive to a change in financial leverage as one percent of increase of financial leverage will lead to a decrease of 0.6082 percent of it Meanwhile, almost half of the change in ROA (47.38%) can be explained by the change of financial leverage, sales and sales growth (determined by the R-squared) For other profitability ratios, the R-squared of the respective regression model is at a lower level, from approximately 0.24 to 0.36
Table 5.1 summarises the key regression results of the four models at a confidence level of 95%
Table 5.1 Key regression analysis results
R-squared Impact of FL to dependent variable
Source: Compiled and summarised by the author with STATA
Limitations of the research
The steel industry is an industry with high barriers for entrance, so currently, there are not many companies listed on the Vietnamese stock exchange As a result, the number of observations in the research is limited
Moreover, due to different reasons, some of the data were significantly different from others and need to be removed from the samples to avoid the bias for the analysis Many companies have made losses during the study period, causing the profitability ratios to have negative values, which is unusual However, the identification and removal process was only based on a rule of thumb, which can lead to a certain degree of inappropriateness in handling these data and affect the accuracy of the empirical results.
Recommendation
5.3.1 Recommendation to listed steel companies
One of the significant findings of the descriptive analysis is the dependence of steel companies on debt the main component of the capital structure According to the Pecking order theory, this is evidence of companies lacking the capability to fund for their own investment and operation, having to accept higher financial risk and face difficulties in raising additional capital The empirical results of the research also show that the increasing level of financial leverage can make negative impacts on the profitability of these companies Therefore, it is necessary for listed steel companies in Vietnam to restructure its capital with a strategic direction of increasing the proportion of equity and decreasing the proportion of liabilities In order to achieve this target, companies may consider carrying out some particular activities:
Manage its cash flow efficiently and establish a payment plan for substantial liabilities Both long-term and short-term liabilities such as debts from financial institutions, payables to the State, payables to employees, payables to suppliers, etc should be reviewed, analysed and managed regularly and paid on time to avoid any penalties, loss of reputation or other negative effects to the operation
Determine the safety threshold for the use of debt of the company to avoid excessive financial risk Besides some common ratios such as total debt to assets ratio, interest coverage rate, one of the examples for such a threshold is the Altman Z-score, developed by Edward Altman based on research on 66 companies in the United States in the 1960s to predict the probability of that company going bankrupt after two years The original Z-score formula was as follows:
X 1 = working capital / total assets Measures liquid assets in relation to the size of the company
X 2 = retained earnings / total assets Measures profitability that reflects the company's age and earning power
X 3 = earnings before interest and taxes / total assets Measures operating efficiency apart from tax and leveraging factors It recognizes operating earnings as being important to long-term viability
X 4 = market value of equity / book value of total liabilities Adds market dimension that can show up security price fluctuation as a possible red flag
X 5 = sales / total assets Standard measure for total asset turnover (varies greatly from industry to industry)
The Z-score is an indicator of the company’s probability of going bankruptcy:
If Z > 2.99, then the company is in the "safe" zone – low probability of bankruptcy
If 1.81 < Z < 2.99, then the company is in the "grey" zone
If Z < 1.81, then the company is in the "distress" zone, with signs indicating high probability of bankruptcy
However, the Z-score formula was determined for companies in a different country in a different time period Companies should only base on the concept of building a linear combination of business ratios to determine its appropriate formula or use a more relevant formula to evaluate the default risk for their company
Appraise and evaluate carefully each investment project of the company, especially for companies with a high growth rate and ambitious expansion plan These companies might compromise their financial stability to find all possible ways to raise funds for new investment projects, even with high risk or uncertainty Expansion plans might require a substantial amount of additional capital which cannot be met with retained earnings of the company and the investment of current shareholders, so debt instruments are often used as a quick and convenient way to
37 fund the projects However, this will result in higher debt to asset ratio, costing companies annual interest charges and increase the financial distress costs This follows the Pecking order theory and the static trade-off theory, as the use of debt amongst steel companies is already at a high level Therefore, each project should be carefully appraised in terms of net present value, internal rate of return, payback period, etc when taking into account risk and uncertainty factors throughout the life cycle of the project
Reorganise the operation and business, especially for companies that are making losses The loss will result in a decrease in retained earnings of the company, hence lower the equity value and increase the financial leverage as well as a financial risk These companies need to manage their cost more efficiently by measuring performance of each division or business sector, and try to reduce the cost or cease the operation of divisions and sectors that are proved to be inefficient or constantly loss-making At the same time, sales and marketing should also be focused in order to boost the revenue if possible In case companies face liquidity risk as it is unable to perform obligations to their creditors, debts should be restructured by negotiating for extension of payment periods, lowering of interest rate, or conversion of debt to equity to avoid the bankruptcy scheme
Diversify means of fundraising to get access to cheap capital Currently, there are only a few common ways for a company in Vietnam to raise additional funds such as a loan from banks and issuance of equity This is partly due to the under- development of the legal framework for the capital market, the limited capability and knowledge of both companies and investors, the small size of the companies and the habit of using banking credit Issuance of corporate bonds is an example of an effective way for companies to call for funding, yet it has not been common amongst Vietnamese listed steel companies This method is only used by Hoa Phat Group and Vietnam Steel Corporation with a small group of targeted investors Besides that, financial lease, private equity and venture capital can also be considered as alternative ways of companies to get access to capital at a lower cost
The State plays a crucial role in creating a supportive political, legal and economic environment for the companies Especially for an important industry like the steel industry, it is necessary to take further steps to support the businesses as it helps to provide input materials for various other industries The State might create favourable conditions for steel companies such as providing subsidised loans, lowering the tax rates or promoting commercial activities to help companies attract domestic and international customers For large projects relating to the extraction of iron ore or construction of huge industrial complex, the choice of contractors and investors should be carried out conscientiously to avoid inefficient operation and waste of natural resources, which eventually reduces the profitability of companies
Another important role of the State is the development of the capital market, in particular, legal aspect The capital market in Vietnam is not as well developed as in many other countries with few financial products being traded legally There needs to be a legal framework so that companies and investors can officially raise additional finance via various kinds of financial instruments that are appropriate with their financial strategy The State should promote the stable development of the capital market to simulate the flow of money in the economy
Particularly for listed companies whose stocks are traded on official stock exchanges such as HNX and HOSE, the stock exchange can be the main route to raise additional equity for the companies The State Securities Commission of Vietnam and stock exchanges need to improve their role in monitoring and managing listed companies to ensure the accuracy, transparency, and timeliness of information provided to the investors, which will help investors in their decision making and also companies to call for equity financing more easily i
Appendix A: Value of variables in the models (number of observation: 100)
Company Year ROA ROE ROS ROCE FL SIZE SG
BVG 2015 -0.0287 -0.2258 -0.0752 0.0395 0.8644 12.3211 0.2216 BVG 2016 -0.0126 -0.0766 -0.0362 0.0284 0.7677 11.7047 -0.4601 BVG 2017 0.0068 0.0291 0.0115 0.0567 0.7661 12.0412 0.4001 BVG 2018 0.0092 0.0393 0.0096 0.0683 0.7634 12.5639 0.6865 BVG 2019 0.0017 0.0065 0.0042 0.0710 0.6923 12.4604 -0.0983 DNS 2015 -0.0557 -0.1865 -0.0373 -0.0612 0.7273 13.7247 -0.1996 DNS 2016 0.0902 0.2851 0.0476 0.3565 0.6401 13.9613 0.2669 DNS 2017 0.0454 0.1240 0.0215 0.1925 0.6279 14.1202 0.1722 DNS 2018 0.0323 0.0870 0.0147 0.1653 0.6300 14.1985 0.0815 DNS 2019 -0.0731 -0.2166 -0.0464 -0.1104 0.6985 13.8064 -0.3244 DNY 2015 0.0036 0.0230 0.0048 0.0599 0.8555 14.3600 -0.0787 DNY 2016 0.0076 0.0520 0.0092 0.0739 0.8501 14.5016 0.1520 DNY 2017 0.0311 0.1909 0.0298 0.1301 0.8234 14.6767 0.1914 DTL 2015 -0.0265 -0.0809 -0.0342 -0.0195 0.6887 14.4323 -0.1183 DTL 2016 0.0652 0.1922 0.0553 0.2669 0.6339 14.8751 0.5571 DTL 2017 0.0819 0.1948 0.0636 0.2534 0.5236 14.9680 0.0973 DTL 2018 -0.0066 -0.0151 -0.0050 0.0449 0.6002 15.0563 0.0923 DTL 2019 -0.0500 -0.1326 -0.0559 -0.0462 0.6458 14.7371 -0.2733 HMC 2015 -0.0343 -0.1102 -0.0164 0.0063 0.6786 14.5297 -0.3005 HMC 2016 0.0767 0.2038 0.0265 0.2810 0.5630 14.6747 0.1561 HMC 2017 0.0880 0.2207 0.0292 0.2909 0.6297 14.8339 0.1725 HMC 2018 0.1008 0.2418 0.0257 0.3601 0.5271 15.1575 0.3821 HMC 2019 0.0124 0.0295 0.0025 0.1187 0.6296 15.3182 0.1743 HSG 2015 0.0665 0.2468 0.0374 0.2924 0.6917 16.6747 0.1639 HSG 2016 0.0048 0.0155 0.0841 0.0328 0.6645 16.7000 0.0256 HSG 2017 0.0107 0.0326 0.0509 0.0448 0.7589 17.0793 0.4614 HSG 2018 0.0119 0.0350 0.0119 0.0454 0.7576 17.3548 0.3171 HSG 2019 0.0191 0.0531 0.0129 0.0597 0.6825 17.1490 -0.1860 KKC 2017 0.1071 0.1795 0.0515 0.2683 0.1932 12.6330 -0.3726 KKC 2018 -0.0279 -0.0528 -0.0115 -0.0215 0.4921 12.7557 0.1306 KKC 2019 -0.0332 -0.0767 -0.0093 0.0162 0.4662 13.2064 0.5695 KMT 2015 0.0076 0.0279 0.0017 0.1154 0.7766 14.4588 0.4828 KMT 2016 0.0098 0.0440 0.0031 0.2252 0.7763 14.3395 -0.1124 KMT 2017 0.0181 0.0976 0.0053 0.3767 0.8399 14.6236 0.3285 KMT 2018 0.0105 0.0634 0.0032 0.3684 0.8274 14.7516 0.1366 ii
KMT 2019 0.0103 0.0573 0.0040 0.3219 0.8132 14.4199 -0.2823 NKG 2015 0.0387 0.2200 0.0219 0.1857 0.8209 15.5648 -0.0145 NKG 2017 0.0854 0.3130 0.0561 0.2603 0.7110 16.3507 0.4121 NKG 2018 0.0063 0.0194 0.0039 0.0909 0.6342 16.5109 0.1737 NKG 2019 0.0058 0.0158 0.0039 0.0826 0.6259 16.3150 -0.1779 POM 2015 0.0033 0.0116 0.0028 0.0953 0.6669 16.0987 -0.0923 POM 2016 0.0425 0.1193 0.0324 0.1737 0.6207 16.0452 -0.0521 POM 2017 0.0946 0.2303 0.0614 0.2898 0.5600 16.2465 0.2229 POM 2018 0.0463 0.1205 0.0322 0.1645 0.6543 16.4158 0.1845 POM 2019 -0.0271 -0.0846 -0.0258 0.0109 0.7038 16.3000 -0.1093 SMC 2017 0.0557 0.2756 0.0218 0.4143 0.7606 16.3535 0.3403 SMC 2018 0.0339 0.1371 0.0102 0.2591 0.7453 16.6168 0.3013 SMC 2019 0.0181 0.0699 0.0059 0.2138 0.7374 16.6390 0.0225 SSM 2015 0.0849 0.1564 0.0512 0.2303 0.3791 12.4801 0.1907 SSM 2016 0.0796 0.1262 0.0513 0.1957 0.3587 12.3001 -0.1648 SSM 2017 -0.0649 -0.1338 -0.0407 -0.0853 0.6318 12.4642 0.1784 SSM 2019 0.0593 0.1586 0.0279 0.2412 0.6893 10.4279 -0.7958 TDS 2015 0.1060 0.2299 0.0207 0.3673 0.4718 14.2750 -0.1490 TDS 2016 0.0905 0.1670 0.0204 0.2018 0.4440 14.3522 0.0803 TDS 2017 0.1783 0.2992 0.0356 0.3220 0.3673 14.5222 0.1853 TDS 2018 0.0670 0.1262 0.0138 0.1441 0.5398 14.7262 0.2263 TDS 2019 0.0542 0.1066 0.0140 0.1220 0.4314 14.5595 -0.1536 TIS 2015 0.0408 0.0085 0.0076 0.0521 0.7636 15.8823 0.1535 TIS 2016 0.0183 0.0754 0.0240 0.0686 0.7502 15.9647 0.0859 TIS 2017 0.0094 0.0422 0.0103 0.0545 0.8100 16.0903 0.1337 TIS 2018 0.0028 0.0152 0.0026 0.0475 0.8230 16.2075 0.1243 TIS 2019 0.0040 0.0210 0.0039 0.0516 0.7988 16.1605 -0.0459 TLH 2015 -0.0852 -0.1757 -0.0482 -0.1094 0.5361 15.0937 -0.0362 TLH 2017 0.1289 0.2365 0.0697 0.3580 0.4567 15.4192 0.2298 TLH 2018 0.0296 0.0534 0.0144 0.1308 0.4347 15.5993 0.1973 TLH 2019 -0.0445 -0.0931 -0.0271 -0.0407 0.5910 15.5011 -0.0936 TNB 2015 0.1739 0.2826 0.0457 0.3547 0.2388 14.0263 0.0629 TNB 2016 0.0277 0.0453 0.0062 0.0701 0.5108 14.2040 0.1945 TNB 2017 0.0023 0.0053 0.0005 0.0576 0.6074 14.3560 0.1641 TNB 2018 0.0285 0.0791 0.0068 0.1573 0.6652 14.5570 0.2226 TNB 2019 0.0345 0.0992 0.0097 0.2155 0.6376 14.4896 -0.0652 TNS 2018 0.0000 -0.0008 0.0000 0.0290 0.9612 14.2946 0.0671 TTS 2016 0.0338 0.2379 0.0867 0.1646 0.8233 13.1372 0.1715 TTS 2017 0.0185 0.1071 0.0195 0.1241 0.8129 13.9985 1.3662 iii
TTS 2018 0.0038 0.0260 0.0024 0.1041 0.7862 14.7336 1.0857 TTS 2019 0.0087 0.0605 0.0036 0.1226 0.7858 15.2171 0.6218 TVN 2015 0.0070 0.0205 0.0101 0.0581 0.5082 16.6543 -0.3227 TVN 2016 0.0532 0.1056 0.0468 0.1508 0.4848 16.6975 0.0442 TVN 2017 0.0466 0.0854 0.0393 0.1284 0.4240 16.8013 0.1094 TVN 2018 0.0330 0.0570 0.0230 0.0931 0.4174 17.0210 0.2457 TVN 2019 0.0193 0.0396 0.0123 0.0833 0.5750 17.3425 0.3792 VCA 2015 0.0911 0.1842 0.0273 0.2863 0.3526 14.1532 -0.1419 VCA 2016 0.0910 0.1343 0.0216 0.1717 0.2900 14.1597 0.0066 VCA 2017 0.1785 0.2677 0.0352 0.3479 0.3659 14.4543 0.3426 VCA 2018 0.0619 0.1236 0.0128 0.1871 0.5911 14.7367 0.3262 VCA 2019 0.0452 0.1063 0.0114 0.1924 0.5544 14.6103 -0.1187 VDT 2015 0.0861 0.1135 0.0389 0.1473 0.2468 11.4038 -0.1003 VDT 2016 0.1034 0.1358 0.0430 0.1729 0.2310 11.4447 0.0417 VDT 2017 0.0868 0.1213 0.0378 0.1574 0.3305 11.4864 0.0426 VDT 2018 0.0880 0.1197 0.0313 0.1539 0.1859 11.6753 0.2080 VDT 2019 0.0977 0.1238 0.0302 0.1605 0.2376 11.6866 0.0113 VES 2019 -0.0018 -0.0027 -0.0365 0.0199 0.3293 6.8373 0.1004 VGS 2015 0.0365 0.0894 0.0134 0.1661 0.5132 15.0476 0.2622 VGS 2016 0.0625 0.1465 0.0179 0.2211 0.6161 15.3308 0.3274 VGS 2017 0.0439 0.1216 0.0119 0.2165 0.6593 15.6039 0.3140 VGS 2018 0.0281 0.0729 0.0064 0.1511 0.5586 15.7499 0.1572 VGS 2019 0.0473 0.1139 0.0110 0.1962 0.6059 15.7378 -0.0121 VIS 2015 -0.0258 -0.0826 -0.0167 0.0394 0.8074 14.9478 -0.1755 VIS 2016 0.0327 0.1139 0.0195 0.2030 0.7668 15.1345 0.2053 VIS 2017 0.0153 0.0493 0.0071 0.1555 0.8074 15.6246 0.6326 iv
Appendix B Result of Hausman Test for four models
Appendix C Result of Modified Wald test for four models
Appendix D Result of Wooldridge test for four models
Model 4 viii Appendix E Result of VIF calculation for four models
Model 1 ix Model 2 x Model 3 xi Model 4 xii Appendix F Result of robust regression for four models
Model 1 xiii Model 2 xiv Model 3 xv Model 4 xvi
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