Chart 3.1 Average total assets of 23 food manufacturing companies listed on HOSE 48 Chart 3.2 Assets structure chart of 23 enterprises in the food processing industry 49 Chart 3.3 EPS, R
LITERATURE REVIEW
THEORETICAL FRAMEWORK
Capital structure is defined as the combination of the market value of debt and equity that a company uses in its operations Through many years of research, the concept of capital structure has been described by several researchers as follows: According to A Gill et al (2011), capital structure is defined as a combination of debt and equity that the company uses in its operations Capital structure decisions are important because of the need for companies to maximize profits and are beneficial for determining a company's ability to cope with a competitive environment Along with this viewpoint, there are studies by Ross and colleagues (2003) J Abor (2005) believes that the capital structure of an enterprise is a combination of many different types of capital A business can choose from a multitude of outcomes, but it will try to find the right tool to maximize valuable benefits in the overall field of professional business According to Lim (2012) capital structure is the way a business generates money to finance its operations and the way it dictates the financing options the business chooses for its ending balance sheet It represents a business's total capital in terms of debt and equity combined to fund its operations Saleem (2013) has not given a definition of capital structure as the different main financing options of assets used by a business The combination of debt and equity to finance a business's long-term assets is referred to as the business's capital structure Debt and equity are basic components of a business's capital configuration
In essence, the authors' concepts of collaborators are not much different An enterprise's collaborators include two parts: equity and liabilities, which are closely related to each other when the enterprise mobilizes and uses capital
Commonly used indicators to measure enterprise collaborators are:
Debt ratio, or total debt to total assets, represents the level of use of loan capital by the enterprise, indicating how much of the enterprise's assets are invested in debt According to Tran and Le (2022), debt ratio helps evaluate financial status: Ability to ensure debt payment and risks of the enterprise Debt ratio depends on the business line and field in which the enterprise is operating
A debt ratio greater than 50% means that the company's assets are invested in more debt If the debt ratio is less than 50%, the enterprise's assets are financed mainly by equity The smaller the debt ratio, the less likely a business will have financial difficulties, because it is less dependent on debt to invest in business activities Enterprises with high debt ratios will have difficulty mobilizing additional loans to meet capital needs
Business debt includes short-term debt and long-term debt Therefore, the debt structure is shown:
Short-term debt to total debt =𝑆ℎ𝑜𝑟𝑡−𝑡𝑒𝑟𝑚 𝑑𝑒𝑏𝑡
Long-term debt to total debt =𝐿𝑜𝑛𝑔−𝑡𝑒𝑟𝑚 𝑑𝑒𝑏𝑡
While long-term debt offers more stability than short-term debt, it can also increase a company's overall financial risk in the long run This is because a higher proportion of long-term debt means larger repayments looming in the future, which can put a strain on the company's finances
Total debt to Total equity (DE) =𝑇𝑜𝑡𝑎𝑙 𝑑𝑒𝑏𝑡𝑇𝑜𝑡𝑎𝑙 𝑒𝑞𝑢𝑖𝑡𝑦 Total debt to Total equity (DE) = 𝑇𝑜𝑡𝑎𝑙 𝑑𝑒𝑏𝑡
Total debt to equity ratio shows the ratio between the two basic sources of capital that businesses use to finance their operations These two sources of capital have distinct characteristics and the relationship between them is widely used to evaluate firms' collaborators (Tran and Le, 2022) The larger the debt to equity, the more important the loan source plays in the enterprise's operations If debt to equity is greater than 1, meaning debt accounts for a larger proportion than equity and vice versa
The ratio itself reflects the proportion of equity in the total capital of the enterprise The larger the value of this ratio, the higher the level of financial autonomy with equity of the enterprise, so the risk of the enterprise is lower If this rate is greater than 50%, it means that the enterprise's capital is largely funded from the capital contributed by shareholders
Financial performance is the yardstick by which a company's ability to generate revenues from its primary business assets is gauged It serves as a comprehensive assessment of a firm's financial robustness within a specific timeframe This evaluation delves into the effectiveness and efficiency with which a company not only generates revenues but also handles its assets, liabilities, and the financial concerns of its stakeholders Furthermore, revenues denote the aggregate income derived from the sale of goods or services within the company's operational sphere (IAI, 2016)
Didin (2018) discussed that financial performance is a measure of how much a company's ability to create profit, profit, or revenue This can be viewed from the financial statements The financial statements are financial records covering cash flows, balance sheets, profit-loss and capital changes that become information for corporate managers in taking the company's financial policy The financial statements are the financial condition of a company comprising the balance sheet of profit/loss calculation, and other financial information, such as cash flows and retained earnings (Didin, 2017)
Siminica & Stefan (2011) state that “The profitability of an activity is expressed in its ability to generate income to cover the expenses of those activities and lead to the achievement of net income, regardless of type or quantity Its nature is that it is classified in microeconomic levels depending on its profit level According to Bauer (2004), the profit margin of an enterprise is measured by the profit of the enterprise during the years of operation From a theoretical perspective, it is said that the more financial leverage businesses use, the higher their profits will be because of the benefits gained from tax shields, or in other words, the more profits they use, the more debt they should use In the study of Addae and colleagues (2013), the authors pointed out that profit margin constitutes a measure of asset creation for shareholders and can be replaced by other terms such as enterprise value, stock value shareholders, shareholder wealth Therefore, managers should make decisions about collaborators that bring the greatest benefit to shareholders Wrong decisions about capital structure will affect the profit margin of the enterprise, thereby reducing shareholder value
1.1.2.2 Indicators to measure financial performance
Financial performance is a measure of how much a company's ability to create profit, profit, or revenue according to Didin (2018) Maximizing profit or profit margin is the top goal of any business Therefore, evaluating and measuring profitability in the past, present and predicting the future plays an extremely important role Accordingly, profit can be measured through a number of indicators as follows
Return on total assets (ROA)
In the work of Hamid and colleagues (2015), the authors pointed out that profitability is also known as financial performance, and it is closely related to the capital structure of a firm In particular, ROA is the most effective and available financial measure to evaluate the performance of a business It fundamentally evaluates business performance, thereby showing income statement performance and the assets needed to run a company
According to David Lindo quoted by Siminica & Stefan (2011),
"Return on assets (ROA) is a general-purpose financial index used to measure the relationship of profits earned to the investment assets needed to earn profits The ROA percentage is the basis that can be used to measure the required profit contribution from new investments." Research by Tailab
(2014) also believes that ROA is a good proxy for profits because it is related to the business's return on underlying assets According to Phan Duc Dung
(2008), return on assets ratio is a financial ratio used to measure the profitability per dong of assets of a business This ratio is calculated by taking the net profit (or profit after tax) of the business in the reporting period (can be 1 month, 1 quarter, half a year, or 1 year) divided by the total value of the business's assets in the same period Data on net profit or profit after tax are taken from the business income statement, while asset value is taken from the balance sheet Phan Thanh Hiep, (2016) also advocates that a business's profit margin is measured by ROA
The rate of return on assets is calculated using the formula:
This ratio shows how much profit will be generated for each dong invested in the total assets of the enterprise, thereby reflecting the profitability of the assets or the frequency of exploitation of the assets of the enterprise The higher the ROA, the more effective the enterprise is in its ability to manage and use assets as well as its business strategy On the contrary, if ROA is low, the business is making a loss, managers need to reevaluate and come up with new solutions, promptly adjust business strategies and improve the exploitation of the company's resources However, to evaluate whether the ROA index is good for a business or not, the analyst must also consider other factors such as the industry in which it operates, its competitors as well as the company's past business results
EMPIRICAL RESEARCH
In Vietnam there have been many research projects on the relationship between capital structure and business performance in many different industries and fields
Le & Phung (2013) found that collaborators have a negative and statistically significant impact on the operations of businesses listed on the
Ho Chi Minh City Stock Exchange during the 4-year period from 2008 to
2011 In the authors' study, Bui & Nguyen (2016) about 427 enterprises listed on HSX and HNX in the period 2010-2014, operating efficiency is measured by Return on equity ROE and Q ratio of Tobin'sQ The variables are ratio of short-term debt to total assets (STD), ratio of long-term debt to total assets (LTD), ratio of total debt to total assets Company size (SIZE), Growth in total assets (GROWTH) have an impact on both ROE and Tobin's
Q variables Two variables SIZE and GROWTH have a positive and statistically significant relationship with ROE In contrast, LTD and TD have a significant negative impact on ROE The STD variable has a negative relationship with ROE and is not statistically significant For the independent variable Tobin's Q', the variables SIZE and STD have a positive and statistically significant relationship The remaining variables are all statistically significant and have a negative relationship to Tobin's Q
Tran Thi Bich Ngoc, Pham Hong Trang (2016), researched the impact of capital structure on operational efficiency of enterprises in the processing and manufacturing industry listed on the Ho Chi Minh City Stock Exchange, using panel data with a sample of 68 enterprises in the Ho Chi Minh City Stock Exchange period 2009-2013 and operating efficiency indicators according to accounting books (ROE and ROA) and operating efficiency indicators according to market value (Tobin'Q index) This study uses panel data regression method and concludes that capital structure has a clear and negative influence on business performance On the other hand, growth opportunities have a positive impact on ROA and ROE but negatively on Tobin's Q index In addition, business scale also has a negative and statistically significant impact on the operating efficiency index according to the market value of the company DN Tran & Nguyen (2020) also presented research results showing that collaborators have a negative impact on the operational efficiency of businesses in the research sample Research results on 34 listed enterprises in the Energy sector by Tran & Nguyen (2020) show that explanatory variables including short-term debt to total assets (SDR), long-term debt to total assets (LDR), total debt to total assets (TDR) all impact in the opposite direction to business performance, consistent with the characteristics of Vietnam's Energy industry The scale control variable firm size (SIZE) has a positive effect on business performance Large-scale businesses will quickly access advanced science and technology as well as be able to take advantage of their reputation in the market to easily access investment capital from outside as well as sell more easily Similarly, other control variables such as tangible assets (TANG) and growth (GROW) also have a positive impact on business performance On the contrary, liquidity ratio (LIQ) positively affects business performance
BV Thanh (2022) researched the impact of capital structure on the performance of listed enterprises on the Vietnamese stock market The author uses a pooled regression model (Pooled) that uses the least squares (OLS) method to estimate the overall relationship between capital structure representative variables that impact the operating efficiency of the enterprise The author conducted research with 87 companies (excluding businesses in the financial and real estate sectors) listed on the Ho Chi Minh City Stock Exchange from 2009 to 2019 Research results show that capital structure factors include Total debt and short-term debt have negative impacts on business performance The proportion of short-term debt in total debt has a positive impact on business performance Control factors such as scale (SIZE), asset turnover (TURN), growth (GR), have the same impact on business efficiency Enterprises operate more efficiently during stable economic times However, Doan Ngoc Phuc (2014), researched the impact of capital structure on business performance of enterprises after equitization in Vietnam using a data source of 217 enterprises Companies listed on the HOSE and HSX in the period 2007-2012 showed results showing that long- term debt has a positive impact on ROA and ROE, while short-term debt and total debt have a statistically significant negative impact on the operating efficiency of the company Enterprises after equitization are measured by ROA and ROE The author uses independent variables including short-term debt, long-term debt, total debt and dependent variables to measure operating efficiency including ROA and ROE
Nguyen Hai Nam and his colleagues also used ROA and ROE as indicators for the profitability of real estate companies in their research in
2023 For the capital structure of the firm, the authors use long-term and short-term debt ratios The final result of this research shows that the short- term debt ratio has a negative impact on both ROA and ROE while the long- term debt ratio has the opposite result
According to Modigliani & Miller (1963), capital structure is the combination of debt and equity that a company uses in its operations According to this view, capital structure affects the value of a firm through a company's combination of debt, equity, or a mixture of securities and capital In other words, the use of loan capital will increase the value of the business and at the same time interest expenses will be deducted directly from the tax payment amount and thus the tax payment amount will be less than for businesses that do not use loan capital
Research by Saeed & Badar (2013) shows the impact of using leverage in capital structure on business performance They applied research on all food sector companies listed on the Karachi stock exchange The results show that long-term debt has a positive and significant impact on company performance, while, in the short term, debt has a significant negative impact on business performance
Mumtaz & Zanetti (2013) study in Pakistan shows that the relationship between a company's capital structure and performance is negative Salteh et al (2012) found a strong positive relationship between capital structure and ROE but a negative relationship with ROA and EPS Ahmad et al.'s (2012) research results on the impact of capital structure on operating businesses show that each type of debt has a significant negative relationship with ROE, while ROA has a positive relationship extreme short-term debt and total debt Kwanum (2012) investigated the relationship between capital structure and performance of manufacturing companies listed on the Nigerian Stock Exchange They found that short-term debt and long-term debt have an insignificant negative relationship with ROA In addition, the study concluded that capital structure is not the main determinant of business performance Onaolapo & Kajola (2010) found that capital structure has a significant negative impact on financial firm performance
Studies in the past regarding the impact of capital structure on a firm’s performance have been broadly applied to various sectors of the economy with varying research results Empirical studies have identified different viewpoints of the researchers on capital structure and firm performance Previous researchers investigated and observed a significant positive relationship between capital structure and a firm’s performance
Several studies have found that capital structure has a positive impact on firm performance in financially or economically developed countries However, in developing countries, evidence has shown that the relationship between leverage and performance is significantly negative Furthermore, both positive and negative effects of capital structure on business performance were identified Arindam Bandyopadhyay and Nandita Malini Barua (2016) empirically investigate the linkage of corporate sector performance with the capital structure and macroeconomic environment Using a balanced panel data of 1594 Indian corporate firms over 14 years
(1998 to 2011), they found empirical evidence to support the hypotheses relating to the relevance of asymmetric information, agency cost, trade off theory, signaling and liquidity aspects in determining firm's capital structure decisions in emerging market economy It is found that macroeconomic cycle significantly influences corporate financing decisions and hence performance
Research of Mahfuzah and colleagues (2012) on the relationship between capital structure and company performance This study is conducted using panel data procedure for 237 Malaysian listed companies on the Bursa Malaysia Stock Exchange for the period 1995-2011 The study used four measures including ROE, ROA, Tobin's Q ratio and EPS as dependent variables The results of operating efficiency measured by ROE, ROA and EPS have an inverse relationship with the indicators representing capital structure such as short-term debt, long-term debt, and total debt as independent variables Tobin's Q coefficient has a significant positive relationship with short-term debt and long-term debt, and for total debt, there is also a significant negative relationship with the company's performance as analyzed above The article was researched by khaslau Currently, the socio-economic context has changed a lot Sudiyatno and colleagues (2012) also pointed out that the relationship between financial leverage and corporate value with the representative variable Tobin'Q is in the same direction But unlike Zeb and Rasid (2016), this study believes that the use of debt will increase shareholder value, however, the use of debt must be within a reasonable ratio to increase business value On the contrary, if too much debt is used, it can cause owners do not achieve the desired results as well as reduce opportunities for managers However, this study also found that increased debt use will reduce business performance measured by ROA The decrease in ROA when using more debt is explained because the return on capital increased when using debt is lower than the cost of using debt
In other to study capital structure, it should be made known that each type of capital has its benefits and bottlenecks, and a significant part of wise corporate stewardship and management involves attempting to find the perfect capital structure regarding risks or reward payoff for shareholders Several, relevant works of literature on how capital structure impacts firm performance have been reviewed For analysis, there are many variables or elements in a capital structure choice and structure purposes of debt such as the long-term and short-term debt maturity mixture which will impact a firm’s performance Thus, examining the impact of capital structure variables on firm performance will provide evidence for a corporation’s performance as a result of the impact of capital structure Zeitun and Tian
(2007) investigated the effect which capital structure has had on corporate performance using a panel data sample representation of 167 Jordanian companies during 1989-2003 Their results showed that a firm’s capital structure had a significantly negative impact on the firm’s performance measures, in both the accounting and market’s measures They also found that the short-term debt to total assets (STDTA) level has a significantly positive effect on the market performance measure (Tobin’s Q) The Gulf Crisis 1990-1991 was found to have a positive impact on Jordanian corporate performance while the outbreak of Intifada in the West Bank and Gaza in September 2000 had a negative impact on corporate performance
Business size can also play an essential role in determining the relationship between leverage and firm performance, regardless of the country’s degree of development Ibhagui and Olokoyo (2018) examined the empirical links between leverage and firm performance by means of a new threshold variable, firm size They ask whether there exists an optimal firm size for which leverage is not negatively related to firm performance Accordingly, with panel data of 101 listed firms in Nigeria between 2003 and 2007, they explore whether the ultimate effect of leverage on firm performance is contingent on firm size; that is, whether the type of impact that leverage has on the performance of a firm is dependent on the size of the firm Their results show that the negative effect of leverage on firm performance is most eminent and significant for small-sized firms and that the evidence of a negative effect diminishes as a firm grows, eventually vanishing when firm size exceeds its estimated threshold level They find that this result continues to hold, irrespective of the debt ratios utilized Their results show that the effect of leverage on Tobin’s Q is positive for Nigeria’s listed firms However, in their new finding, it is evident that the strength of the positive relationship depends on the size of the firm and is mostly higher for small-sized firms When the firm was significantly large, however, the impact tended to be favorable Furthermore, Jaisinghani and Kanjilal (2017) discovered that, for firms that are smaller than the cut-off value of size, high level of investments in marketing is associated with improved firm performance However, for the firms that are larger than the cut-off value of size, high level of investment in marketing is associated with reduced firm performance
RESEARCH GAP
First of all, the latest research about the impact of capital structure on financial performance of firms in manufacturing industry in Vietnam was more than ten years ago, so the data of this research is more up-to-date, and it also reflect a very unstable period of time
Next, the relationship between capital structure and financial performance of firms has been done for decades, and the result of those research was conflicted since some said that there is a positive impact between capital structure and profitability, some said the opposite, some even concluded that there was no impact between capital structure and financial performance of a firm
Because of these two reasons, the author has proposed a research direction on testing the influence of capital structure on performance of firms in manufacturing industry that listed on HOSE in the period of 2019 to 2023.
DATABASE AND RESEARCH METHOD
DATABASE
To complete the thesis on the impact of capital structure on the business performance of the Food Manufacturing industry in Vietnam, the author conducted research using panel data with 23 listed companies in the
Ho Chi Minh Stock Exchange (HOSE) from 2019-2023, respectively equivalent to 115 observations
The author collects secondary data from audited consolidated financial statements and annual reports of companies of manufacturing industry that listed on the Ho Chi Minh Stock exchance within 10 years from
2014 to 2023 However, there are missing data of some companies from the period of 2014 to 2018 because some of them were founded in the later year, such as BAF Therefore, the author decided to reduce the data from 10 years to only 5 years, from 2019 to 2023 The author took these data from Vietstock.vn and financial statements, cash flow statements, and notes to audited financial statements published on the official website of the company In addition, the endogenous variables of each company are also carefully calculated through Excel software based on the above data
The study chose to use the data from 2019-2023 because, in this period, Vietnam signed many Free trade agreements (FTA) with other countries around the world, namely the Comprehensive and Progressive Agreement for Trans-Pasific Partnership (CPTPP), European Union - Vietnam Free Trade Agreement (EVFTA), Regional Comprehensive Economic Partnership (RCEP), This has created many challenges and opportunities for Vietnam's food industry in competing in domestic and international markets.
RESEARCH METHODOLOGY
The author carries out quantitative research methods with secondary data taken from financial statements of seafood enterprises that have been publicized and audited on the Stock Exchange Quantitative methods are used to clarify the influence of capital structure on business performance, specifically as follows:
First, the author performs descriptive statistics on the data, this step helps us have an overview of the variables in the model and research The results obtained are values such as the average value, standard deviation, minimum value, and maximum value of the dependent variable, and independent and control variables From there, we have an overview of the data included for research
Next, the author conducts correlation analysis to see if the variables included in the model have a strong relationship with each other, determine the level of correlation in the same direction or the opposite direction, strong or weak correlation between the independent variable and the dependent variable In addition, we can consider the phenomenon of multicollinearity when we see the case of independent variables with high correlation coefficients When there are signs of doubt, we check to see if the model has multicollinearity If there is multicollinearity, it will affect the regression results of the model Therefore, we need a way to handle multicollinearity in the model to have accurate and reliable regression results
Third, the author performs regressions of the models in turn Pooled OLS, FEM, REM model Next, the author proceeds to choose between the two OLS and FEM models using the F-test, which is based on the assumption that there is no difference between the intercept in time units Use the Hausman test to choose between the two models FEM and REM
After choosing the appropriate model, the author regressed the model and checked whether the model was suffering from heteroskedasticity or autocorrelation If so, the author uses the GLS method to overcome defects in the model
The regression process, model selection, overcoming defects and data processing for analysis are all performed by the author himself on STATA14 software.
RESEARCH MODEL AND HYPOTHESIS
In this research, to evaluate the impact of capital structure on profitability, students use the dependent variables ROA - rate of return on total assets and ROE - rate of return on equity The two indicators ROE and ROA show the relationship between profits and actual production costs and demonstrate the business level of business leaders in using those factors In short, profitability indicators will show whether a business's financial situation is good or not and how the business manages and uses its assets effectively This is also the indicator that previous studies have used such as Salim and Yadav (2012), Hasan et al (2014), Semuel and Widjojo (2016), Singh & Bagga (2019), Doan Ngoc Phuc (2014)
The rate of return on total assets and the rate of return on equity is calculated using the formula:
EPS is also an indicator representing the financial performance of a company, according to Tandelilin (2007) It shows the amount of net profit the company is ready to share with all shareholders of the company Vietnam's stock market has the potential to develop further when the total number of private accounts of individuals and organizations in the country by the end of 2023 according to the State Securities Commission is 7,246,977 accounts, an unprecedented number and modest compared to other countries Because of this, an indicator that reflects the amount of profit for each shareholder like EPS will be suitable to represent a firm's financial performance
Moreover, Mahfuzah and Yadav (2012) also measured the firm’s performance by using EPS in their research of Malaysian listed companies The result shows that capital structure had a negative relationship in 3 sectors consumer, construction, and industrial products On the other hand, in the same research, it’s been shown that in the plantation and trading sector, there is a positive relationship between EPS and total debt - a variable used to represent capital structure
The earnings per share is calculated using the formula:
Based on the research of Allozi and Obeidat in 2016, Total debt-to- total assets and total debt-to-total equity are used to represent the capital structure of a firm The results revealed that all leverage ratios showed no significant relationship with the stock return, similar to Mahfuza et al (2012) and Pouraghanjan et al (2012) who also state that the higher the total debt to total assets, the lower the performance of the companies
To calculate these ratios, we use the formula:
Total debt to Total assets (DA) = 𝑇𝑜𝑡𝑎𝑙 𝑑𝑒𝑏𝑡
Total debt to Total equity (DE) = 𝑇𝑜𝑡𝑎𝑙 𝑑𝑒𝑏𝑡
In this study, the firm's size is taken as the logarithm of total assets of each research enterprise at the end of each fiscal year
Firm size is used to classify different businesses in the same field Larger firms have a greater variety of capabilities and can enjoy economies of scale; these can impact positively on performance (Penrose, 1959) Additionally, Zeitun and Tian (2007), and Agiomirgianakis et al (2006) argue that the size of the company can affect the profitability of the business; therefore, large companies have an advantage in resources, power, and reputation in the market to improve business performance as well as increase business profitability Nevertheless, given the Vietnam overall scene, it is likely that market power arguments concerning size are likely to dominate over coordination failure issues, and size and profitability are expected to display a positive relationship
A variable that also has attributes in controlling industry-related and business-cycle factors is Liquidity (LIQ), in this case, the cash assets ratio, calculated by the formula:
Cash requirements may be conditioned by industry practices, but also by the overall economic climate, since in lean times cash-flow crises can arise Additionally, Liquidity also helps capture firm-specific attributes, since the ability to manage working capital and acquire a greater quantity of cash balances relative to current liabilities reflects superior skills which are also likely to be reflected in a firm’s ability to generate relatively greater profits since a lesser cost burden with respect to the use of short-term finance is faced (Majumdar and Chhibber, 1997)
Referred to Singh and Bagga (2019), Tax, which was described by the ratio of tax to earnings before interest and tax (EBIT), is used as a control variable
It has been shown in the same research that TAX has a significant positive influence on return on equity It may be because with an increased tax rate, the quantum of tax shield will increase for a given amount of interest on debt This further results in an increase in return to equity shareholders
Lastly, the growth rate of the company is the last control variable of the model There are many conflicting studies on the impact of revenue growth rate on the profit margin of enterprises While the research results of Tran Trong Huy & Nguyen Thi Ngoc Han (2020) and Bui Van Thuy & Nguyen Thi Ngoc Diep (2016) suggest that the growth rate of enterprises has a positive impact on profitability, the research of Onaolapo and Kajola in 2010 showed no correlation between enterprise growth rate and profit margin In today's increasingly fierce competitive environment, each business must always ensure that its business activities are increasingly developed to be able to stand firm in the market Growth is one of the basic conditions for businesses to achieve their goals throughout their business life Growth helps businesses accumulate capital and invest in facilities to continue expanding business activities while building reputation with customers as well as with suppliers and investors Therefore, growth is an opportunity to develop, expand operations, and increase profits
To understand the impact of capital structure on financial performance, the author applies panel regression to match each variable's characteristics The regression method of array data in this study has been commonly used in many other studies such as Hai Nam Nguyen et al (2023); Singh et al (2019); Mahfuzah and Yadav (2012) Therefore, the authors used a panel regression model based on the variables in the table as
𝑅𝑂𝐴 𝑖,𝑡 : Return on Total assets of Firm i in year t
𝑅𝑂𝐸 𝑖,𝑡 : Return on Equity of Firm i in year t
𝐸𝑃𝑆 𝑖,𝑡 : Earnings per share of Firm i in year t
𝐷𝐴 𝑖,𝑡 : Total debt to Total assets of Firm i in year t
𝐷𝐸 𝑖,𝑡 : Total debt to Total equity of Firm i in year t
𝑆𝐼𝑍𝐸 𝑖,𝑡 : Firm’s size of Firm i in year t
𝐿𝐼𝑄 𝑖,𝑡 : Quick ratio of Firm i in year t
𝑇𝐴𝑋 𝑖,𝑡 : Tax to EBIT of Firm i in year t
𝐺𝑅𝑂𝑊 𝑖,𝑡 : Growth rate of Firm i in year t
Debt can help businesses increase capital to invest and develop their business, however, if debt management is ineffective, businesses may encounter financial difficulties and affect their ability to pay debts in the future If the debt ratio is too high, it will lead to a high financial risk for the business, the business may go bankrupt or have to sell assets to pay debt
Therefore, to ensure business efficiency, debt management and capital structure are very important From a financial perspective, studies by
Mahfuzah & colleagues (2012); Pouraghajan & colleagues (2012) pointed out that this indicator has a negative impact on business performance This brings us to the main hypothesis of this research
H1: Capital structure (DA and DE) has a negative impact (-) on firm’s financial performance (ROA, ROE, and EPS)
Table 2.1: Description of variables and expected result
Definition and measure Expected result
ROA Return on Total assets
DA Total debt to Total assets
DE Total debt to Total Equity
Source: Compiled by the authors
RESEARCH RESULT
CURRENT STATUS OF FOOD MANUFACTURING
INDUSTRY COMPANIES ON THE HOSE STOCK EXCHANGE
According to the website finance.vietstock.vn, there are a total of 96 companies in the food manufacturing industry on the Vietnamese stock market, of which the number of companies listed on HOSE is 23 Prominent businesses among them include Masan Group Corporation (HOSE: MSN), Viet Nam Dairy Products Joint Stock Company (HOSE: VNM), KIDO Group Corporation (HOSE: KDC), Bibica Corporation (HOSE: BBC),…
Revenue, profit, and growth rate
Table 3.1 Average net profit after tax of 23 food manufacturing companies listed on HOSE
Source: Compiled by the authors
Table 3.1 shows us that the average after-tax profit of food processing enterprises fluctuates quite strongly Starting with a decrease in 2019-2020, from 962 billion VND to 734 billion VND This is the starting point of the covid-19 epidemic, causing all business activities to be affected By 2021, we can see revenue increase sharply at 51.5% compared to 2020 At this time, businesses have begun to adapt to the epidemic situation, and the government has also continuously offered support packages as well as both workers and consumers being vaccinated resulted in increased industry revenues
However, we see a sharp decline from 1,1113,085 million VND in
2021 to only 624,190 million VND in 2023, nearly 50% The after-tax profits of most businesses in this industry tend to decrease, partly due to the pressure of rising commodity prices due to the Russia-Ukraine war, and maritime crises that make import and export more difficult
Compared to the same period last year, some large food manufacturing enterprises recorded the following developments in profit after tax: BBC (- 50.15%), KDC (- 63.92%), MSN (- 60%.67%), and VNM (+ 5.15%) According to their ordinary information disclosure, Masan Group Corporation explain the reason for the loss is mostly came from the increase in selling expenses due to promoting sales activities and in 2022, the business recorded a profit arising related to the merger with a new subsidiary In the case of Bibica, the 64% loss in profit after tax was cause by the fact that the company claimed to do assets transaction in the same period Kido Group explains that the cause of this loss comes from fluctuations in the world and domestic economies, market impacts, and reduced consumer purchasing power; Fluctuations in the supply chain, raw material prices combined with inflation affect business management costs
Table 3.2 Statistical results of ROA, ROE (average in 5 years)
As we can see in the table 3.2, the changing trend of ROA and ROE is quite similar to the change in profit after tax, peaked in 2021 and hit bottom in 2023 However, there are some firms have the opposite trend, in this case, is Bentre Aquaproduct Import and Export JSC (HOSE: ABT) and Vinamilk ROA and ROE of these 2 companies is 18% and 26% for Vinamilk, and 10% and 13% for ABT In the case of Vinamilk, this can be explained by the fact that their net revenue is remain unchanged, while the total equity and total assets of the company has decreased when compared to the previous year On the other hand, even though have the ROA and ROE ratio higher than the average of 2023, but when compared to the previous years, these 2 ratios of Bentre Aquaproduct had decreased Net revenue of the company had decreased 14% Not only the company, but this phenomenon is also shown throughout the fish and seafood industry According to Directorate of Fisheries, in 2023, exploited aquatic product output is estimated to reach 3,856.5 thousand tons, down 0.5% compared to
2022 Of which, fish is estimated to reach 2,981.2 thousand tons, down 0.4%, shrimp is estimated to reach 144, 5 thousand tons, down 0.8% Marine fisheries output reached 3,643.9 thousand tons, down 0.7% compared to the previous year, of which fish was estimated at 2,846.1 thousand tons, down 0.5%, shrimp was estimated at 135.1 thousand tons, decrease 1.5% The seafood industry is affected by the prolonged Russia-Ukraine conflict, the fighting between Israel and Hamas, and the unstable situation in the Middle East, causing the world economy to recover slower than forecast, with inflation in many countries countries and territories, people in other countries tightened their spending, global consumer demand decreased sharply, including seafood products, leading to difficulties in export activities
Chart 3.1 Average total assets of 23 food manufacturing companies listed on HOSE
Source: Compiled by the authors
The average total assets of 23 corporations of the industry reaches 15,033 billion VND in 2023 This number has maintained its upward trend throughout the research's study period, increasing about 9% each year
With table 3.4, we can see that both current assets and non-current assets of 23 companies in HOSE share the same increasing trend as the total assets Moreover, currents assets aways lower proportion than the non- currents assets, except the year of 2021 2021 is all so the only year that the non-current assets slightly decrease, from 6,658,315.13 to 6,561,364 million VND, making the percentage of current assets bigger than its of non-current assets
Chart 3.2 Assets structure chart of 23 enterprises in the food processing industry
Source: Compiled by the authors
In the research of Nguyen Thi Tran Nhan Ai (2011), she stated that the characteristic of assets of the food processing industry is that short-term assets account for a larger proportion in the period of 2008 to 2010 However, this research shows the opposite statement, when in the period of
2019 to 2023, the average current assets of food manufacturing companies listed in HOSE is lower than non-currents assets This phenomenon happened because the big companies such as MSN accounts for a large proportion in 23 companies The non-current assets of these companies account for about 60-70% of the assets structure, and nearly half of it came from fixed assets On the other hand, most of the companies on this list share the same statement as Nguyen Thi Tran Nhan Ai (2011) that the characteristic of assets of the food processing industry is that short-term assets account for a larger proportion The reason why businesses in this industry use a lot of short-term assets is due to industry characteristics: large warehouses of raw materials to meet seasonal factors, sales on deferred payment
Current assets Non-current assets
Table 3.3 Summarize the number of businesses in the research sample according to debt to assets ratio
Source: Compiled by the authors
The table 3.3 shows the trend of using debt of food processing enterprises in the sample period 2019 - 2023 and tends to increase gradually when considering the DA index Most businesses are located in areas with a debt-to-total asset ratio of 0.1 to 0.5, however, 2020, 2022 and 2023 are the only 3 years where there are more than 2 businesses with DA greater than 0.5 Especially after 2021, there are 1 company has the DA ratio larger than 0.7, which is ANGIMEX or An Giang Import – Export Company (HOSE: AGM) This happened due to an advance debt that is difficult to recover, leading to a loss of assets and equity of the company, according to the firm explanation As we can see in the table 3.4, the average debt to total assets of this sample is very stable, moving between 30.59% and 35.47%
Table 3.4 Average debt to total assets and debt to equity of 23 companies in each year from 2019 to 2023
Source: Compiled by the authors
However, if we look in the debt-to-equity ratio, we can see some significant changes From 2020 to 2022, the average DE ratio is quite stable in the range of 0.5 to 1, even though in 2022, the average DE seem quite high compared to other two The two abnormal years are 2019 and 2022, when the average DE is 1.35 and 2.68, respectively This mean that in the year of 2019, the average debt of 23 firms was 1.35 time bigger than the average Equity If we look in the table 3.5, we can see that in the year of
2019, sixteen out of 23 companies have the DE ratio in the range of 0 to 1 This mean that the proportion of debt of the other seven was greater than those who had the DE smaller than 1
In the year of 2023, the abnormal average DE came from ANGIMEX, which we just had talked about That loss causes the ownership’s equity of AGM went from about 292 billion VND to only about 21,83 billion VND, which 93% loss This led to the DE of 5,567.77, which mean that the debt of AGM in 2023 is more than five and a half thousand time its Equity Beside this case of AGM, according to table 3.5, most of the companies in this sample still have the DE ratio smaller than 1
Table 3.5 Summarize the number of businesses in the research sample according to debt-to-equity ratio
Source: Compiled by the authors
ANALYZE AND EVALUATE RESEARCH RESULTS
From the collected data, the author conducted descriptive statistics through STATA 14 software, performing descriptive statistics on 9 variables in the research model, specifically 3 dependent variables ROA, ROE, and EPS; 2 independent variables DA and DE, and the control variables are SIZE, LIQ, TAX, and GROWTH with 115 observations, the author obtained the results in the following table:
Table 3.6: Descriptive statistics Variable Obs Mean Std Dev Min Max ROA 115 0521478 0621267 -.153 258 ROE 115 0785391 1994709 -1.662 387 EPS 115 2199.127 3330.319 -12866.08 13996.45
DE 115 1.268087 4.417457 0 45.202 SIZE 115 15.17701 1.570815 12.211 18.809 LIQ 115 0036957 0110598 0 112 TAX 115 1489826 1340848 -.237 934 GROW 115 0699043 2894172 -.77 1.067
From the statistical descriptive results obtained, the author draws several comments:
Firstly, the average ROA, ROE, and EPS in the period 2019-2023 are very low 5.21%, 7.85%, and 7.134 respectively This result shows that the average seafood industry in this period developed not very well As we can see, the minimum values of all 3 variables are negative, showing that the industry was suffering losses in some years This can be explained by the fact that the Covid-19 pandemic in this period had hindered the ability to operate of the industry However, the maximum number of these variables is very high, nearly 5 times higher than the average This might imply that the profitability of the industry is significantly impressive in a normal condition Moreover, the number of observation of EPS is dropped to 109 due to the negative value of EPS before logarithm
Second, the ratio of total debt to total assets and the ratio of total debt to equity has an average value of 0.32 and 1.268 DA ranges from 0 to 0.798, while DE ranges from 0 to 45.202 The odd number of DE’s maximum value means that in that company, in a specific year, the total debt is 45 times bigger than equity, implying that the firm is heavily in debt, and/or the owner’s equity of the firm is extremely low
The third is the SIZE variable, encoded as the logarithm of total assets SIZE ranges from 12.221 to 18.809, with an average value of
15.17701 The standard deviation of this control variable is 1.57
Fourth is the LIQ variable, the firm’s liquidity has an average value of 0.3% and a standard deviation of 0.011
The fifth is the TAX variable, having an average value of 0.1489 The standard deviation of this control variable is 0.1340
Finally, the GROW control variable, the firm’s revenue growth rate, has an average value of nearly 7% and ranges from the smallest value of -
0.77 to the largest value of 1.067
Table 3.7: Correlation coefficient between variables in the research model
ROA ROE EPS DA DE SIZE LIQ TAX GROW
The author uses STATA 14 software to run the correlation matrix between variables in the model Using correlation analysis helps the author examine the direction of the relationship between the independent variable and the dependent variable in the model The result in Table 3.7 shows that there is a negative correlation between the capital structure (DA and DE) and financial performance (ROA, ROE, and EPS), similar to the previous research such as Nguyen et al (2023), and Abor (2005) Liquidity and tax rates also share the same result while SIZE and growth rate shows a positive correlation
Besides, the correlation coefficient results indicate the extent to which multicollinearity occurs in the research model The matrix table has a total of 36 pairs of correlations of independent variables, some of which have a high level of correlation higher than the remaining pairs of the model but still lower than 70% so their influence is insignificant However, there isn’t any pair of correlation that is higher than 70% so multicollinearity may not occur
Multicollinearity testing is used to see whether independent variables are correlated with each other, especially in this study, the author checks the multicollinearity phenomenon of the variables DA, DE, SIZE, LIQ, TAX, and GROW Because if multicollinearity appears in the model, it will cause the regression results to be misleading Therefore, before conducting regression to ensure the accuracy of the regression results, multicollinearity must be checked through the Pearson correlation test method, variance inflation factor (VIF)
Table 3.8 Variance magnification factor VIF of the model
Table 3.8 of the model's variance magnification coefficient shows the coefficient and correlation level of the independent variables and control variables in the model The results of table 3.8 show that the VIF of all 6 variables is less than 2, this proves that the model is unlikely to have multicollinearity
3.2.4 Regression results of the effect of capital structure on financial performance
After checking multicollinearity between independent variables and control variables and concluding that multicollinearity is unlikely to occur, the author used STATA 14 software to regression the built model
Table 3.9: Multivariate regression results of the influence of capital structure on ROA of food manufacturing enterprises on HOSE
To test the compatibility between the OLS model and the random effects model (FEM), the author uses the F test The F test results show that
Prob>F = 0.00% is less than 5%, so accepting the random effects model is more appropriate for research than the OLS model The author continues to conduct Hausman tests for the two models FEM and REM With Prod > chi2
= 5.19% greater than 5%, the author concludes that the random effects model (REM) is most appropriate
Table 3.10: Hausman test for model 1
GROW 0470269 0451106 0019163 b = consistent under Ho and Ha; obtained from xtreg
B = inconsistent under Ha; efficient under Ho; obtained from xtreg Test: Ho: difference in coefficient not systematic
H0: The model does not have heteroscedasticity
H1: The model has the phenomenon of heteroskedasticity
Table 3.11: Result of the heteroskedasticity test for model 1
Breush and Pagan Langrangian multiplier test for random effect ROA [id,t] = Xb + u[id] + e [id,t]
Test: Var(u) = 0 chibar2(01) Prob > chibar2 64.08 0.0000
Table 3.11 shows that the P-value = 0.00% is less than 5% Therefore, we can reject H0 as a model without heteroskedasticity and accept hypothesis H1 as a model with heteroskedasticity
H0: The model does not have autocorrelation
Table 3.12: Result of the autocorrelation test for model 1
Wooldridge test for autocorrelation in panel data
The author used the Wooldridge test to test the autocorrelation phenomenon of the REM model According to the results in the table, Prob
> F = 0.73% is less than 5%, we can reject the hypothesis H0 that the model does not occur autocorrelation and accept the hypothesis H1 that the model occurs autocorrelation
After testing the heteroskedasticity and autocorrelation phenomenon of the model, the author found that the research model had both defects Therefore, to overcome the phenomenon of heteroscedasticity and autocorrelation, the author uses the generalized least squares (GLS) technique to make the research model more reliable
Table 3.13: Result of Generalized least squares (GLS) for model
Cross-sectional time-series FGLS regression
Common AR(1) coefficient for all panels (0.4402)
Number of obs Number of groups Time periods
= 0.000 ROA Coef Std Err z P>|z| [95% Conf interval]
Based on the model using GLS technique, it can be seen that there is a negative relationship between two variables representing the capital structure of the enterprise, which is DA and DE, and ROA with a significance level of 1% This is consistent with the research of Nguyen Hai Nam et al (2023), Shubita and Alsawalhah (2012) Based on research results, the author found that business size (SIZE) has a positive relationship with ROA, which shows that the larger the company is, the higher its profitability However, the P-value of the Size variable is very large (0.517), so business size is not related to the company's financial performance Firm’s cash ratio (LIQ) has a negative effect on ROA, however, similar to SIZE, the P-value
= 0.375, which is higher than the significance level of 10%, so is not related to ROA
In this model, Tax has a negative effect to the profitability of the firm with the significance level of 1% This result is contrary to the research of
Singh and Bagga in 2019 when their final result shows that Tax has a positive impact on ROA Similar to Salim and Yadav (2012), revenue growth rate of a firm has a positive on the return on total assets ratio
After checking multicollinearity between independent variables and control variables and concluding that multicollinearity is unlikely to occur, the author used STATA 14 software to regression the built model
To test the compatibility between the OLS model and the random effects model (FEM), the author uses the F test The F test results show that Prob>F = 0.00% is less than 5%, so accepting the random effects model is more appropriate for research than the OLS model The author continues to conduct Hausman tests for the two models FEM and REM With Prod > chi2
= 0.00 smaller than 5%, the author concludes that the fixed effects model (FEM) is most appropriate
Table 3.14: Multivariate regression results of the influence of capital structure on ROE of food manufacturing enterprises on HOSE
Source: STATA 14 Table 3.15: Hausman test for model 2
TAX -.0897319 -.1068334 0171016 0.19136.5 GROW 1616969 1526483 0090486 b = consistent under Ho and Ha; obtained from xtreg
B = inconsistent under Ha; efficient under Ho; obtained from xtreg
Test: Ho: difference in coefficient not systematic
H0: The model does not have heteroscedasticity
H1: The model has the phenomenon of heteroskedasticity
Table 3.16: Result of the heteroskedasticity test for model 2
Modified Wald test for groupwise heteroskedasticity in fixed effect regression model
Table 3.16 shows that the P-value = 0.00% is less than 5% Therefore, we can reject H0 as a model without heteroskedasticity and accept hypothesis H1 as a model with heteroskedasticity
H0: The model does not have autocorrelation
Table 3.17: Result of the autocorrelation test for model 2
Wooldridge test for autocorrelation in panel data
The author used the Wooldridge test to test the autocorrelation phenomenon of the FEM model According to the results in the table, Prob
> F = 0.18% is less than 5%, we can reject the hypothesis H0 that the model does not occur autocorrelation and accept the hypothesis H1 that the model occurs autocorrelation
After testing the heteroskedasticity and autocorrelation phenomenon of the model, the author found that the research model had both defects Therefore, to overcome the phenomenon of heteroscedasticity and autocorrelation, the author uses the generalized least squares (GLS) technique to make the research model more reliable
Table 3.18: Result of Generalized least squares (GLS) for model
Cross-sectional time-series FGLS regression
Common AR(1) coefficient for all panels (0.4402)
Time periods Wald chi2 (6) Prob > chi2
ROA Coef Std Err z P>|z| [95% Conf interval]
This table of this model shows the same result as the outcome of the first model where DA, DE, LIQ and TAX has a negative effect on ROE, while SIZE and GROW has a positive impact on financial performance of a company However, the P-Value of LIQ is still higher than 10%, so LIQ in this model is still not statistically significant, while the P-value of SIZE is less than 1%, meaning that it is statistically significant
After checking multicollinearity between independent variables and control variables and concluding that multicollinearity is unlikely to occur, the author used STATA 14 software to regression the built model
Table 3.19: Multivariate regression results of the influence of capital structure on EPS of food manufacturing enterprises on HOSE
To test the compatibility between the OLS model and the random effects model (FEM), the author uses the F test The F test results show that
Prob>F = 0.00% is less than 5%, so accepting the random effects model is more appropriate for research than the OLS model The author continues to conduct Hausman tests for the two models FEM and REM With Prod > chi2
= 13.61% greater than 5%, the author concludes that the random effects model (REM) is most appropriate
Table 3.20: Hausman test for model 3
GROW 2662.707 2574.977 87.73022 29.53826 b = consistent under Ho and Ha; obtained from xtreg
B = inconsistent under Ha; efficient under Ho; obtained from xtreg Test: Ho: difference in coefficient not systematic
H0: The model does not have heteroscedasticity
H1: The model has the phenomenon of heteroskedasticity
Table 3.21: Result of the heteroskedasticity test model 3
Modified Wald test for groupwise heteroskedasticity in fixed effect regression model
Table 3.21 shows that the P-value = 0.00% is less than 5% Therefore, we can reject H0 as a model without heteroskedasticity and accept hypothesis H1 as a model with heteroskedasticity
H0: The model does not have autocorrelation
Table 3.22: Result of the autocorrelation test model 3
Wooldridge test for autocorrelation in panel data
The author used the Wooldridge test to test the autocorrelation phenomenon of the REM model According to the results in the table, Prob
> F = 2.49% is less than 5%, we can reject the hypothesis H0 that the model does not occur autocorrelation and accept the hypothesis H1 that the model occurs autocorrelation
CONCLUSION AND RECOMMENDATION
CONCLUSION
With data collected from 23 food manufacturing enterprises listed on
Ho Chi Minh City Stock Exchange (HOSE) during the 5-year period 2019-
2023, including 115 observation samples, the author analyzed how capital structure have an impact on the enterprise's financial performance The thesis uses the dependent variables ROA, ROE and EPS to represent profit margins, the independent variables are debt ratio (DA) and debt on equity rario (DE) to represent capital structure and some control variables such as scale (SIZE), revenue growth (GROWTH), liquidity (LIQ), and the tax rate (Tax) Through regression of 3 models Pooled OLS, REM and FEM along with the use of Hausman test and F-test steps, the fixed effects multiplicative model is selected as the most suitable model for the thesis
After overcoming model defects by estimating GLS, the author showed that at the 1% significance level, profit margin is negatively affected by the variables DA, DE, LIQ and TAX, in which LIQ not statistically significant to profitability represented by all three variables On the contrary, the variables SIZE and GROWTH have a positive relationship with profitability at the 10% level This result accepts all research hypotheses, except the case of LIQ
Research results show that businesses should consider and allocate debt and equity ratios in accordance with the business's capabilities to bring about the highest operational efficiency in production and business activities
From the above conclusion, the author proposes a number of solutions and recommendations.
RECOMMENDATION
First, the result of this research shows that the debt ratio has a negative impact on profitability variables, which brings us to the first recommendation: building a reasonable capital structure that brings optimal profit and loss efficiency to the company The results of the model suggest that reducing the debt ratio will increase the profitability of the business This is true because the debt ratio of the companies in the research sample has a high average debt ratio However, enterprise profits can still be increased if the enterprise uses debt at a certain level, but when overused leverage, the cost of financial distress also increases, leading to a decrease in profits Therefore, businesses in the industry need to balance the proportion of debt and equity at a safe level, providing a reasonable collaborator for the business to maximize profits Cash flow allocation is also very important Enterprises need to set a clear purpose for what capital is used for and set specific targets for capital use efficiency Businesses need to carefully calculate costs and implement capital management to avoid waste and minimize unnecessary costs Businesses should review the ratio between debt and equity, which on average is quite high Having too much debt will cause the cost of debt to increase, which will also cause profits to decrease, or in other words, the benefits of shareholders in the business will decrease Businesses need to increase equity capital to reduce interest costs and increase the ability to participate in larger projects However, to increase equity capital, businesses need to have a clear and effective financial plan to attract many domestic and foreign investors After the COVID-19 epidemic and many of negative event such as war, the food manufacturing industry is facing a difficulty in maintaining their operational performance To promote economic recovery, banking system has given out many loan interest rate reductions, and many preferential credit programs and products This might be good for the current situation, but for the long-term goal, companies should review the ratio between debt and equity, keepings it in a safe ratio
Second, scale of a firm is a factor that is positively correlated with profitability Therefore, expanding scale will bring many opportunities for the business itself Businesses can expand their business scale through investment cooperation or finding new markets Enterprises can cooperate with partners in the food processing industry and outside the industry to share risks and increase the financial capacity of the enterprise Investment cooperation not only helps businesses learn how to manage and access partners' resources, but also helps businesses access new technology courses to support their production and business activities The larger the operating scale of an enterprise, the more investors are interested in it, as well as increasing its reputation in the market, making it easier for businesses to mobilize capital
Thirdly, the research results show that increasing the revenue growth rate contributes to improving the profitability of enterprises Therefore, businesses need to focus on the quality of their products, stay ahead of the digital transformation trend, and promote the application of science and technology to operational processes to enhance reputation and build brand image in the market, thereby attracting many domestic and foreign customers and investors, gaining loyal customer files, contributing to revenue growth for the company In addition, businesses also need to research competitors in the market through the application of analytical models such as SWOT model and CPM model to grasp the strengths and weaknesses of competitors, thereby building plans and strategies appropriate business, improve and develop your business's products and services, enhance your competitive advantage as well as your company's revenue and profits
Lastly, companies need to promote the search for new markets to access many sources of customers and investors to promote growth in revenue and capital of enterprises in the food manufacturing industry in general and smaller branches in particular Expanding the consumer market not only helps businesses sell more goods but also helps businesses solve the inventory problem Since Vietnam has joined in many FTA in recent years, the opportunity to access foreign markets is huge, especially developed countries This also means that we have to improve production processes as well as improve workers' skills in order to meet the strict standards of the foreign market.
LIMITATIONS OF THE STUDY
The thesis uses data samples from 23 food manufacturing enterprises listed on the Vietnam stock market on the HOSE exchanges during the 5- year period 2019-2023 This research sample is quite small compared to the current market, so it may not be comprehensive and representative of the entire industry In addition, the thesis only focuses on research on food manufacturing enterprises, so it will not be typical of the entire economy or another industry Therefore, the author proposes the next research direction to expand the research sample in both space and time The authors can prolong the research period and consider studying Logistics enterprises listed on both UPCOM and HNX exchanges, from which the results on the impact of collaborators on the profit margin of enterprises will be more generalizable
About the research model, the author only uses 3 dependent variables: return on assets (ROA), return on equity (ROE) and earnings per share (EPS) To measure a firm’s financial performance, many different indicators can be used, such as Tobin’s Q, when it can be used to reflect the value of a firm on the market, while ROA, ROE and EPS only based on the book value
The thesis conducted research using only the ratio of debt to total assets and total debt to equity as a proxy for the capital structure The research did not show depth due to the lack of short-term debt to assets and long-term debt to assets ratios Therefore, future studies can add these measurement variables to make the research results more in-depth and clearer.
FUTURE RESEARCH DIRECTIONS
First, we should increase the dependent variable The author suggests that future research should use more dependent variables representing operational efficiency to implement the model This will make the model more reliable
Second, Research should be carried out over a longer period of time and expanded to many industries
Third, the result cannot show the best capital structure for the industry or give any review and explanation on how and why a particular company is using a specific capital structure in a specific time In order to do so, we can do interview with CEO and CFO of these companies This method can give us data from a practical perspective and let us see the thought process in practical, not just in theory
1 Cao T T T (2022) “Ảnh hưởng của cấu trúc vốn đến khả năng sinh lời của các doanh nghiệp ngành Xây dựng – Bất động sản niêm yết trên Sở Giao dịch chứng khoán Hà Nội” Graduation thesis Banking Academy of Vietnam
2 Le C T (2023) “Ảnh hưởng của cấu trúc vốn đến khả năng sinh lời của các doanh nghiệp Logistic niêm yết trên thị trường chứng khoán Việt Nam” Graduation thesis Banking Academy of Vietnam
3 Ngo N T L (2018) “Cấu trúc vốn ảnh hưởng đến khả năng sinh lời của các doanh nghiệp ngành Xây dựng – Bất động sản niêm yết trên sở giao dịch chứng khoán Thành phố Hồ Chí Minh” Master thesis
Ho Chi Minh City University of Foreign Language – Information Technology
4 Nguyen T T N A (2011) “Tác động của cấu trúc tài chính đến hiệu quả tài chính của doanh nghiệp trên thị trường chứng khoán TP.HCM – Trường hợp ngành công nghiệp chế biến thực phẩm” Master thesis University of Economics Ho Chi Minh City
5 Nguyen H N., Cao T N L and Hoang T N A (2023)” Tác động của cấu trúc vốn tời khả năng sinh lời của các doanh nghiệp thuộc nhóm ngành bất động sản được niêm yết trên sàn chứng khoán HOSE và HNX” Tapchicongthuong.vn Available at: https://tapchicongthuong.vn/tac-dong-cua-cau-truc-von-toi-kha- nang-sinh-loi-cua-cac-doanh-nghiep-thuoc-nhom-nganh-bat-dong- san-duoc-niem-yet-tren-san-chung-khoan-hose-va-hnx-102494.htm
6 Nguyen Q (2021) “Dabaco: Năm 2020 biên lợi nhuận gần 26%, lãi ròng vọt lên 1.400 tỷ đồng” VnEconomy Availiable at: https://vneconomy.vn/dabaco-nam-2020-bien-loi-nhuan-gan-26-lai- rong-vot-len-1400-ty-dong.htm
7 Nguyen T T V and Ha M T (2019) “Lý thuyết về cấu trúc vốn và vận dụng trong xây dựng cấu trúc vốn của doanh nghiệp niêm yết trên thị trường chứng khoán Việt Nam” Tapchicongthuong.vn Available at: https://tapchicongthuong.vn/ly-thuyet-ve-cau-truc-von- va-van-dung-trong-xay-dung-cau-truc-von-cua-doanh-nghiep-niem- yet-tren-thi-truong-chung-khoan-viet-nam-64334.htm
8 Tran T M and Le T H P (2022) “Phân tích ảnh hưởng của cấu trúc vốn đến hiệu quả tài chính của các công ty cổ phần tại Việt Nam: tổng quan nghiên cứu” Tapchcongthuong.vn Available at: https://tapchitaichinh.vn/phan-tich-anh-huong-cua-cau-truc-von- den-hieu-qua-tai-chinh-cua-cac-cong-ty-co-phan-tai-viet-nam-tong- quan-nghien-cuu.html
9 Truong T H Y (2023) “Ảnh hưởng của cấu trúc vốn đến hiệu quả hoạt dộng của các doanh nghiệp Thủy sản được niêm yết tại Việt Nam” Graduation thesis Banking Academy of Vietnam
10 Vietdata (2023) “KIDO - Tham vọng giành lại vị trí dẫn đầu trong thị trường bánh kẹo Việt” Available at: https://www.vietdata.vn/vi/post/kido-tham-v%E1%BB%8Dng- gi%C3%A0nh-l%E1%BA%A1i-v%E1%BB%8B-tr%C3%AD- ng%C3%A0nh-b%C3%A1nh-k%E1%BA%B9o
1 Abor J (2005) “The effect of capital structure on profitability: an empirical analysis of listed firms in Ghana” The Journal of Risk Finance, 6(5), 438–445 doi:10.1108/15265940510633505
2 Anuar H and Chin O (2016) “The Development of Debt-to-Equity Ratio in Capital Structure Model: A Case of Micro Franchising” Procedia Economics and Finance Volume 35, page 274-280 Available at: https://doi.org/10.1016/S2212-5671(16)00034-4
3 Arhinful R., Mensah L., and Owusu-Sarfo J S (2023) “The impact of Capital Structure on the Financial Performance of Financial Institution in Ghana” International Journal of Finance and Banking
Research Volume 9, issue 2 Available at: 10.11648/j.ijfbr.20230902.11
4 Gadzo S G., Gatsi J G and Kportorgbi H (2013) “The effect of Corporate Income Tax on Financial Performance of Listed Manufacturing Firms in Ghana” Research Journal of Finance and
Accounting Available at: https://www.researchgate.net/publication/317786265_The_Effect_o f_Corporate_Income_Tax_on_Financial_Performance_of_Listed_M anufacturing_Firms_in_Ghana
5 Lombardi M., O’Connor S J., Carroll N., Szychowski J M and Borkowski N (2021) “The relationship of Debt Ratio and Financial Performance for Large Not-for-Profit Health System” Journal of
Health Care Finance Available at: https://www.researchgate.net/publication/357717378_The_Relation ship_of_Debt_Ratio_and_Financial_Performance_for_Large_Not- for-Profit_Health_Systems
6 Salim M and Yadav R (2012) “Capital Structure and Firm Performance: Evidence from Malaysian Listed Companies” Procedia - Social and Behavioral Sciences Available at: https://doi.org/10.1016/j.sbspro.2012.11.105
7 Singh N P., Bagga M (2019) “The effect of Capital Structure on Profitability: An Empirical Panel Data Study” Sage Journals Available at: https://doi.org/10.1177/2278682118823312
8 Siminica M and Stefan E O (2011) “Study on the evolution of profitability of Romanian companies listed on Bucharest stock exchange during the economic and financial crisis” Annal s of the university of Craiova Economic Siences
9 Siminica M., Circiumaru D and Simion D (2012) “The Correlation between the Return on Assets and the Measures of Financial Balance for Romanian Companies” International Journal of Mathematical Model and Methods in Applied sciences Volume 6, issue 2
APPENDIX 1: List of 23 manufacturing companies that listed on HOSE
1 AAM Mekong Fisheries Joint Stock Company
2 ABT Bentre Aquaproduct Import And Export JSC
3 ACL Cuu Long Fish Joint Stock Company
4 AGM An Giang Import - Export Company
6 APC An Phu Irradiation Joint Stock Company
7 ASM Sao Mai Group Corporation
BAF BAF Viet Nam Agriculture Joint Stock
DAT Travel Investment And Seafood Development
13 FMC Sao Ta Foods Joint Stock Company
LAF Long An Food Processing Export Joint Stock
17 LSS Lam Son Sugar Joint Stock Corporation
19 NAF Nafoods Group Joint Stock Company
20 PAN The Pan Group Joint Stock Company
21 SBT Thanh Thanh Cong - Bien Hoa JSC
23 VNM Viet Nam Dairy Products Joint Stock Company
NHẬN XÉT CỦA GIẢNG VIÊN HƯỚNG DẪN
(Đánh giá năng lực chuyên môn, năng lực nghiên cứu của sinh viên trong quá trình viết KLTN Đánh giá nỗ lực và hiệu quả công việc, sự thường xuyên liên lạc của sinh viên với GVHD Đồng ý/không đồng ý cho sinh viên được bảo vệ KLTN)
(Ký & ghi rõ họ tên)