Factors Affecting The Profitability Of Chemical Manufacturing Enterprises Listed On The Vietnam Stock Exchange.pdf

INTRODUCTION

THE NECESSITY OF THE RESEARCH

Profitability is defined as an ability to make profit from all the business operations of an organization or an enterprise with efforts of the business’s management (Dave, 2012) Therefore, it is one of the most important criteria when evaluating a business’s potential from investors and business managers’ perspective To help them get a clear picture and make suitable decisions, the author need to figure out the economic factors affecting the profitability, and that is the aim of this study

In the present age, the chemical industry receives a lot of attention from the State and businesses because it plays an essential role not only to the domestic economy but also to the country's industrialization and modernization As a matter of fact, according to the Ministry of Industry and Trade, the average total amount of Vietnam’s chemical output makes up approximately 11% of the GDP of Vietnam and 13% - 14% of the total industry Moreover, in recent years, the growth of the export turnover of the chemical industry have increased dramatically to over 30% thanks to the innovative technology during the 4.0 industrial revolution, except for the slumps in 2019 and 2020 because of COVID-19 epidemic

Figure 1.1 - The growth index and export turnover of the chemical industry from 2016 to 2021

Source: General Department of Vietnam Customs

There have been many previous studies relevant to investigating the factors affecting the profitability, from domestic studies to overseas studies For instance, Tarihoran and Endri (2021) obtained 19 listed companies on Indonesian Stock Exchange from 2015 to 2020 as samples for the study On the other hand, Alghusin (2015) research was based on the data of almost 25 listed enterprises in Jordanian for the time between 1995 and 2005 In case of domestic studies, there was a research done by Linh and Nga (2022) with the data of 79 companies listed on HOSE and HNX from 2016 to 2020 Trang and Phuong (2018) also used 474 companies listed on HNX and HOSE over the period of 2010 to 2015 However, as far as I found, there has been no thesis related to factors affecting the earnings power of chemical companies listed on the Vietnam Stock Exchange This is the reason why the author chooses the topic “ Factors affecting the profitability of chemical manufacturing enterprises listed on the Vietnam Stock Exchange” so as to study what and how these economic influences affect the profitability of chemical enterprises and propose some suggestions to improve the businesses’ current financial situation.

RESEARCH OBJECTIVES

The general objectives of the research are to figure out and analyze the determinants that affect the profitability of chemical manufacturing companies listed on the Vietnam Stock Exchange After that, the author would base on the results to provide some practical suggestions to business administrators and governments in order to enhance the profitability of those chemical manufacturing corporations listed on the Vietnam Stock Exchange

With the aim of achieving general objectives, the author makes a list of specific objectives that need to be accomplished:

To begin with, determining the factors influencing the profitability of chemical manufacturing enterprises listed on Vietnam Stock Exchange

In the next step, evaluating the impact level of the components that affect the profitability of chemical producing enterprises listed on Vietnam Stock Exchange

Lastly, giving some recommendations to increase the profitability of the companies according to the results after evaluating.

RESEARCH QUESTIONS

Research questions lay the groundwork for the thesis and help the researchers focus on the specific aspects of their subject that they are investigating Therefore, the following questions have to be solved in this study:

First of all, what are the determinants which affect the earnings power of chemical manufacturing companies listed on the Vietnam Stock Exchange?

Secondly, what level of impact do these factors influence the profitability of the chemical enterprises?

Last question, what recommendations should be proposed to boost the profitability?

SUBJECT AND THE SCOPE OF RESEARCH

The research subjects are the profitability and factors that affect the profitability of chemical producing companies listed on the Vietnam Stock Exchange

The geographic scope: The research collects data from financial statements of chemical corporations listed on both the Ho Chi Minh Stock Exchange (HOSE) and Hanoi Stock Exchange (HNX) After the author excluded some companies due to not satisfying requirements for the research’s data, only 23 chemical companies listed on VNX are appropriate to be chosen to collect secondary data for the study

The time scope: The data is taken during the period between 2018 and 2022 so as to collect a sufficient number of samples for analyzing process Besides, this is a period when the economy’s fluctuations were noticeable due to COVID-19 epidemic, which means the impact levels might be clearer to analyze.

METHODOLOGY

The thesis applies the quantitative methods together with the qualitative methods to evaluate the factors influencing the efficiency of the companies more comprehensively To be specific:

The qualitative methods: The author reviews previous studies with similar subjects on credible international and Vietnam’s journals and magazines to find out the possible factors affecting the profitability of the listed chemical companies on the Vietnam Stock Exchange After that, the thesis suggests a research model to calculate the relationship between those factors and the profitability of chemical enterprises

The quantitative methods: These methods are used to measure the variables from the information in the past, analyze the relationship between the dependent variables (the profitability) and the independent variables (the factors) along with the relationship among the independent variables to the others By applying regression model such as Pooled OLS Model, FEM, REM through the software Stata 15.0, the model may be used practically to anticipate the future value of the data and show the fundamental characteristics of the data.

IMPLICATION’S CONTRIBUTION

In scientifically practical implication, the paper will aid in providing additional experimental evidence on factors affecting the profitability of chemical companies for further researches in the future The thesis is meant to provide some key information for the business managers and decision-makers with the impact level between the corporations’ profitability and other macro and micro factors, which helps them make strategic decisions to improve the current situation based on the results after analyzing On the other hand, by understanding the connection between the profitability and what affects it, investors are able to evaluate which companies’ shares are potential and make informed investment decisions whether to buy or not, while policymakers can adjust their policies, abolish some regulations unsuitable for the economy nowadays.

STRUCTURE OF THE THESIS

Apart from the declaration, the acknowledgement and the abstract, the research is divided into five chapters:

The first chapter consists of the reason of choosing the topic as a research subject, the aims the author need to achieve through the thesis, the scope of the data and what methods are used to measure it, the contribution of the thesis in both academic and practical ways and the basic structure of the research

Chapter 2 provides the concepts and theories of the profitability, the factors that affect it and the formula for calculating them Aside from this, chapter 2 also gives a summary of the research outcomes of previous empirical studies on the factors affecting the profitability of enterprises from various but trustworthy sources in and outside the country, then identifies the gaps that the study aims to address

According to the results when summarizing studies in the past few years, the author would suggest a theoretical model, sort out which are dependent variables and which are independent variables and assume the correlation between the independent and the dependent variables Besides, this chapter also explains the process of selecting the samples, how to collect data on financial statements as well as the techniques of analyzing the collected data in detail

Chapter 4: Research results and discussions

Chapter 4 presents the outputs of the data analysis to interpret the identified relationship between the factors and the earnings power of the listed corporations, including descriptive statistics, correlation coefficient analysis, multicollinearity test, serial correlation and heteroskedasticity test FGLS technique is used to address the defects of the model and guarantee the model’s effectiveness as the outcome of the thesis as well

The last chapter provides a comprehensive discussion of the research findings, comparing them with previously published researches, then concluding the elements impacting the financial performance of the chemical manufacturing companies listed on Vietnam Stock Exchange After that, the author makes some practical suggestions for the companies and the governments considering the study's conclusions Lastly, identify the limitations of the result and propose directions for further research

Chapter 1 sets the necessity to choose the topic “ Factors affecting the profitability of chemical manufacturing enterprises listed on the Vietnam Stock Exchange”, along with research questions, motives and practical implications that the research aims for when conducting this research In addition, this chapter identifies the scope of research in terms of geographic and time scope After that, based on the structure and research methods (both qualitative and quantitative methods), the author would go further into detail about the factors affecting the profitability of chemical companies listed on the VNX in next chapters.

THEORETICAL BASIC AND LITERATURE REVIEW

OVERVIEW OF THE CORPORATION’S PROFITABILITY

2.1.1 The definition of the profitability

Ben (1977) claimed that profit is the net flow of income, which means it shows the difference between the amount of total revenue and total expenses The expenses contain manpower, materials, interest on debt and other fees The business owners can use the profit to distribute to stakeholders as reward or invest it in either the enterprise’s projects for more earnings or the assets of the company for further company growth However, when the costs are higher than the earnings, profit will turn into a “loss”

According to Amit (2005), profitability is an ability of a business to generate profits after paying its expenses so as to enhance shareholders’ value Amdemikael (2012) defines profitability as a financial measure used to assess how efficient a business makes use of available resources to gain earnings after deducting its total costs incurred in the specific period of time As Toshniwal (2016) mentioned, the term “profitability” is the combination between the word “profit” and “ability”, with the term “profit” reflects to how much a business manages to earn from all operating activities, while the word “ability” reflects how well a company to earn returns after removing the costs and taxes

Profitability is one of the most vital criteria when investors and business administrators evaluate a company’s performance The higher the profitability ratio is, the efficiently a corporation can utilize its funds to make profit at the level of sales, assets and capital stock The purpose of profitability is to see how effectively an enterprise manages during operating activities so as to suggest proper solutions to improve the firm growth and remain competitive in the market

There are three ways which are often used to calculate the profitability of one firm, including ROA, ROE and ROI

Return on Equity (ROE) is defined as a measure that performs the link between a corporation’s profit and an investor’s return (Ahsan A., 2012) To put it simply, it shows the ability of a company’s performance that can use how much money shareholders invest to generate profits Therefore, the higher ROE is, the more efficient a company is at turning funds into earnings

ROE is measured by different formulas, but the most common formula is dividing annual net income by equity of shareholders The net income refers to a firm’s profitability after subtracting from costs, namely taxes, interests, depreciation, et cetera Meanwhile, shareholders’ equity is the amount after removing a firm’s total liabilities from its total assets The following formula is shown below:

Return on Equity = Net Income

Above is the formula of ROE Which means, the higher the Return of Equity is, the better a company is at making money Nevertheless, a high ROE does not always mean it is positive, and a negative ROE cannot be used to evaluate since it results in a net loss or the negative shareholders’ equity

Beside ROE, Return on Assets (ROA) is another ratio which is commonly used for the same purpose as ROE, specifically for evaluating how efficiently a company uses its assets to generate profits in a given period without taking taxes into account In other words, the higher ROA is, the more productive a firm is at utilizing its total assets to gain earnings Despite having the same purpose, ROE use shareholders’ equity to calculate, whereas ROA uses the total assets of a firm as a dividend in the formula The following formula is shown below:

Return on Assets = Net Income

Based on this indicator, the higher the Return of Assets is, the better a company is at generating for each VND of its property The drawback here is that it cannot be used to compare among different industries, as the assets bases of companies in this industry is not the same to those in another industry

Unlike ROA and ROE, Return on Investment (ROI) is a less favorable choice when evaluating a firm’s performance On the other hand, this percentage is useful since it shows the comparison between the amount an investor has paid for and how much that person has earned back on a business project, according to George and Franklin (1996) Thus, ROI helps investors, individuals or businesses figure out a project is potential enough or not to make an investment

ROI is determined by subtracting cost of investment from the gain and dividing the deduction by the cost of the investment The cost of investment includes purchase price, commissions, maintenance fee and any operating expenses regarding the investment The following formula is shown below:

Return on Investment = Gain from Investment - Cost of Investment

ROI can be positive, negative or it equals 0 In case of being negative, it means that the costs already exceed the returns, leading to a net loss If there are a lot of options, investors can depend partly on this measure to eliminate the negative options and choose the best ones However, there are some noticeable limitations of ROI that need to be considered, which are time is not taken into account by ROI and ROI does not mention any kind of assessment for risk to investors and businesses

And based on previous reference literatures and profitability measures mentioned above, the author makes a decision to use ROA for calculating the profitability of chemical manufacturing enterprises.

RELATED THEORIES

Economies of Scale theory is an economic theory that shows the relationship between the scale of producing goods and cost advantages achieved by companies

To be specific, economics of scale happens when an enterprise increases the size of manufacturing products or services, often measured as the amount of output produced per unit of time, together with lowering the average expenses, it will result in the rise in profitability and competitiveness

The original concept of this theory is from Adam Smith (1776) with the idea to obtain larger production returns by labor division and specialization Letting an employee do a specific task to improve skills or hire specialized labor can help the company produce more goods within less time and money Other sources consisting of buying inputs in bulk at a volume discount; investing on advertising or management quality appropriately; or utilization of technology can also lead to the occurrence of economies of scale

However, bigger does not always mean better At this rate, the balance between demand and supply might become weaker Also, large companies may find less flexible and hard to adapt to changes in the market Scaling up also results in a high risk of diseconomies of scale, which occur when the average producing costs begin to rise as output increases

Taking M and M theory (Modigliani and Miller, 1958) as a theoretical background, trade-off theory was first given by Kraus and Litzenberger (1973) and refined by Myers (1977) as a more complete version because they considered the existence of taxes, bankruptcies and other imperative aspects when explaining a corporation’s capital structure, not just analyzing benefits in the assumption of a perfect market and ignoring the expenses like in M and M theory

According to the recent concept, financial leverage can have a positive relationship with the profitability at first, since the more debt the company takes on for stimulating the growth rate, the greater the value of the company rises thanks for the tax benefit Nonetheless, after going up to a certain level of limit and exceeding the break-even point, in which agency and bankruptcy costs of debt’s tax benefit may be outweighed by its presence, the connection gradually becomes negative In an empirical study in the UK market of Banerjee et al (2000), they also supported the trade-off theory that the growth of financial leverage is found for a positive effect on the profitability to certain limits, but the relationship will be inverse once it exceeds that level of limitation This result implies that a firm may be beneficial if it increases its liabilities reasonably.

FACTORS THAT INFLUENCE ON A FIRM’S PROFITABILITY

After looking into previous researches such as Alghusin (2015), Trang and Phuong (2018), Charles et al (2018), Linh and Nga (2022), etc., the author realizes that this topic pays considerable attention not only in Vietnam but also in other countries, thus a wide range of determinants affecting the profitability have been found out and demonstrated Generally, those factors are divided into two main categories: internal factors and external factors Nevertheless, in the author’s research, the theories and related studies of internal factors influencing the profitability of an enterprise would be focused on, including firm size, financial leverage, liquidity, firm growth, debt ratio and total assets turnover

Firm size is defined by the company’s total assets or total sales it receives every year performed on its annual financial statements and market value of its equity (Frank and Chongyu, 2013) Based on Frank and Chongyu’s study (2013), total sales is the most appropriate measure to calculate firm size To be specific, firm size is measured by the natural logarithm of a firm’s annual total sales

In the investigation of Akinyomi and Adebayo (2013), the profitability was proved to affect positively on firm size by applying regression analysis A large firm size tends to be less risky than smaller ones since the ability to access loans for investments is easier, because the creditors believe that it has adequate assets and ability to pay back the loans it borrows Also, a large corporation often goes together with its high value and has specific position in the market, as well as is meant to have higher survival rate than the smaller one However, some other studies by Abeyrathna and Priyadarshana (2019) on listed manufacturing companies in Sri Lanka or Amato and Wilder (1985) on the US manufacturing firms show no sign of impact between the firm scale and the profitability.

Erza Solomon (1963) stated: “Leverage is the ratio of net returns on shareholders equity and the net rate of return on capitalization” If a business is leveraged, we can say that firm takes loans to purchase assets A financial leverage is measured by dividing the percentage change in EPS by the percentage change in EBIT

Thus, the higher financial leverage is, the more a company relies on borrowed loans to gain earnings, leading to the increase in financial risk There are several studies in line with the results that financial leverage affects negatively the profitability, such as Han et al (2021) and Charles et al (2018), whereas Alpi (2018) concluded that there was no connection between financial leverage and the profitability

Liquidity is a term related to a firm’s capability to pay off its short-term liabilities such as accounts payable that comes due in less than a year (Kasmir, 2008) The more liquid assets your company has, the more flexible and agile your business is, which would help the business overcome some unexpected situations Liquidity is measured by dividing current assets by short-term liabilities

A high volume of liquid assets is also helpful if you find a firm needs to apply for business loans since creditors would base on this volume to judge a company’s solvency Many studies such as Han et al (2021) and Nurlaela et al (2019) support the argument that a high liquidity results in a high profitability of the firm On the contrary, Charles et al (2018) and Wilson (2004) found that liquidity influenced conversely on the profitability due to its loss of rate of return and purchasing power for a long period

A company growth refers to a change of a specific variable in a given period of time In this situation, the growth rate will be measured by dividing the deduction of revenue between two consecutive years by the revenue of the year before the year n, then multiplying it with 100% to get the percentage

Investors and businesses often apply this metric to estimate the near future growth so as to make an appropriate plan for boosting the growth rate Many studies such as Charles et al (2018) and Alghusin (2015) support the argument that a high growth rate of the company results in a high profitability of the firm On the contrary, Biger et al (2008) as well as Trang and Phuong (2018) found that firm growth influenced conversely on the profitability The reason for this is that with a high growth rate, companies are likely to spend more money on resources and operating activities, aside from that, they have to face the fierce competitiveness with other companies in the same sector and struggle to find new customers on the new market, all of these are elements contributing to the erosion of an enterprise’s profitability

Debt plays a vital role in an enterprise, since debt can be used to finance its operating activities and investing activities A firm can charge its bills or invoices and purchase assets by debt without issuing shares and liquefying shareholders’ earnings In the research of Nassar (2016), debt ratio is a measure the amount of leverage used by a company in terms of total debt to total assets A debt ratio is measured by dividing the total debt by total assets

According to Margaritis and Psillaki (2010), there was a positive effect between the debt ratio and the profitability of a company, while Mohammad and Jaafer (2012) opposed the argument that the debt ratio affected in a positive way on the profitability Since the more loans the firm borrows, the more interest payments it needs to paid, thus lowering the credit ratings, which can further increase interest expenses and reduce profitability

According to Kasmir (2015), total assets turnover is a ratio used to measure the turnover of all assets owned by the company and measure how much sales are obtained from the assets In other words, this ratio also offers information about how big the contribution of each total asset in generating sales Total assets turnover is measured by dividing current sales by total assets

Some studies proved that total assets turnover significantly influenced the profitability (Nurlaela et al, 2019; Aang, 2019; Mohammad and Jaafer, 2012)

Otherwise, Hasna (2019) disagreed with those studies above and claimed that no connection was found between the total assets turnover and the profitability.

LITERATURE REVIEW

Because the profitability is always one of the top priorities that every business and investor pays attention to when evaluating the business’s performance, there are a vast number of researches analyzing this topic, not only in foreign nations but in Vietnam as well

Trang and Phuong (2018) studied the determinants affecting the firms’ earnings quality by taking data from 474 companies listed on Ho Chi Minh and Ha Noi Stock Exchange over the 6-year period from 2010 to 2015, leading to 2507 observations The study used Dechow and Dichev’s accruals quality model (2002) to measure the profit quality of firms As for the panel data, the authors used pooled OLS, REM and FEM to calculate The results indicated that maturity, financial leverage and capital intensity had positive effects on the profit quality while growth affected contrarily Meanwhile, firm performance and firm size were not the influencing factors in this case

Huy et al (2020) pointed a study on the determinants influencing the profitability of 35 food-processing firms on the Vietnam Stock Exchange from 2012 to 2018 with 245 observations By applying regression models consisting of the Pooled Ordinary Least Squares Model (pooled OLS), Random Effects Model (REM) and Fixed Effects Models (FEM), the study found that fixed assets to total assets and firm size influenced though not significantly, debt ratio showed a negative impact on the profitability, while total-debt-to-total-equity and long-term- debt had positive influence on profitability The rest variables firm age, GDP growth and inflation rate had no impact on the profitability

Han et al (2021) performed a study on 26 listed companies of steel industry on

Vietnam’s stock market between 2016 and 2020 so as to find out factors affecting the profitability of this industry Using the GLS model as the way to examine data, the authors proved that firm size, growth rate and liquidity had positive impacts on the profitability On the contrary, corporations having high financial leverage tended to have low profitability The article in the end proposed some suggestions to investors and firm administrators based on the results they analyzed

Charles et al (2018) carried out a study to check the level impact of enterprise characteristics on the consumer goods enterprises in Nigeria Only 18 out of 22 selected companies met the conditions to be chosen for the analysis The secondary data was acquired from audited financial statements during the period of six years, from 2011 to 2016 Hausman specification test was used to confirm whether the random effects model suitable for the study or not and panel data techniques (FEM and REM) were utilized to check the influence of these characteristics on profitability The results pointed out that firm size, sales growth, and financial leverage were substantial factors of profitability, in contrast to firm age and liquidity Alghusin (2015) investigated the relationship between the profitability and financial leverage, firm growth and firm size based on the sample of 25 Industrial companies listed on Amman Stock Exchange in Jordan from 1995 to 2005 With the aid of regression model techniques, the results showed the similar effects as those in the study of Charles et al (2018) Hence, the author suggested that company administrators should improve the earnings quality of their companies by reducing the debt ratio and increasing financial assets compared with total assets

Nurlaela et al (2019) did an investigation into the impact of liquidity, assets turnover and capital structure on 28 consumption industry sector corporations listed on the Indonesia Stock Exchange in the period from 2016 to 2018, reflecting 84 observations The measure represented the profitability was ROA After conducting the multiple linear regression analysis by using SPSS, the authors proved that all three factors including capital structure, liquidity and total assets turnover had significantly positive impact on financial performance The studies of Weston and Thomas (1992) as well as Aang (2019) also supported the opinion that the higher total assets turnover was, the better companies could manage assets to get higher profits Nevertheless, Sunjoko and Arilyn (2016) indicated no effect toward total assets turnover in their researches into the listed pharmaceutical companies on the Indonesian Stock Exchange

Beside those researches mentioned above, there are other studies conducted investigations to find and examine various determinants affecting the profitability For example, Matthijs (2018) carried out the test by using OLS regression analysis to determine the factors influencing the profitability of 250 selected manufacturing companies on the New York Stock Exchange between 2012 and 2017 Results showed that Research and Development intensity, firm growth, employee productivity, financial leverage, current ratio had positive effects on profitability, while net assets turnover had the opposite effect on profitability On the other hand, there was no statistically significant link between the profitability and firm age along with firm size Hasna (2019) examined debt ratio, debt to equity ratio and total assets turnover and concluded that none of these variables affecting on the profitability of pharmaceutical sub-sector corporations listed on the Indonesian Stock Exchange In China, there was a study done by Alarussi and Gao (2023) whose purpose was to identify the determinants that affected profitability in 100 Chinese listed companies over the period of three years from 2017 to 2019 The special thing in this study is that they used intangible assets, an independent variable hardly seen in any related researches The results concluded that firm size, working capital and intangible assets influenced positively on profitability, whereas the relationship between liquidity and profitability was strongly negative, which meant the companies suffered low profit due to inefficient use of liquid property The following table will summarize the factors and the outcomes of those studies:

Table 2.1 - Table of previous researches

Positive (+) Negative (-) Non-statistical significance

Trang and Phuong (2018) Han et al (2021) Alpi (2018)

Kartikasari and Marisa (2016) Rajan and Zingales (1995) Nurlaela et al (2019) Charles et al (2018)

Charles et al (2018) Tarihoran and Endri (2021) Linh and Nga (2022) Alghusin (2015) Kartikasari and Marisa (2016) Matthijs (2018)

Han et al (2021) Trang and Phuong (2018)

Charles et al (2018) Trang and Phuong (2018) Lee et al (2006) Alghusin (2015) Biger et al (2008) Liu et al (2017) Han et al (2021)

Lan and Cong (2019) Charles et al (2018) Linh and Nga (2022)

Han et al (2021) Wilson (2004) Amit et al (2005)

Nurlaela et al (2019) Alarussi and Gao (2023) Debt ratio

Dao et al (2022) Huy et al (2020) Hasna (2019)

Wippern (1966) Anandasayanan (2013) Margaritis and Psillaki (2010) Mohammad and Jaafer (2012) Total assets turnover

Weston and Thomas (1992) Matthijs (2018) Hasna (2019)

Nurlaela et al (2019) Sunjoko and Arilyn (2016)

Source: combined by the author

In conclusion, studies of factors affecting the profitability of companies in various sectors collected secondary data from many countries with different economic periods For this reason, the results give different opinions on the factors impacting the capability of firms to gain profits, together with the direction of these factors However, in spite of a wealth of research that has been stated on the table above, there still remains a research gap in terms of understanding regarding the relationship between the determinants affecting the profitability and the profitability of companies in chemical industry listed on the Vietnam Stock Exchange Therefore, the author decides to work out this gap in this study with the aim of fulfilling the research scopes of this topic

Chapter 2 presented the overview of theoretical basis of the profitability, the theories related to the topic (such as Economics of Scale theory, Trade-off theory, and Pecking Order theory) and its three common metrics (ROA, ROE, and ROI) In addition, several theories from Vietnam and overseas related to the topic about the determinants affecting the profitability of firms were interpreted thoroughly and summarized in the table The author also addressed the foundational issues surrounding these six chosen factors, such as their definitions, formulas, along with their impact directions on the profitability in different studies, whether they were negative, positive or having no connection with the ability of gaining profits These played a core role as a foundation to find a research gap and help the author develop the research model in the following chapter.

DATA AND METHODOLOGY

RESEARCH PROCESS

After finding out the topic for the research and giving an outline of previous studies, accompanied by the proposed research model, the author carries out the analysis of regression model via Stata 15.0 software according to the data collected from 23 companies in the chemical field listed on the Vietnam Stock Exchange during the period from 2018-2022 The following is a detailed process of how to do research on selected companies:

Step 1: Give an overview of the theoretical basis and summarize previous studies in various countries related to the research topic After that, identify the gaps among the chosen references and expect the directions of factors towards the profitability for the topic

Step 2: Based on the theoretical basis and experimental evidence that have been stated in Step 1, the author proposes a research model, along with explaining the variables and writing hypotheses for the research

Step 3: Determine the research sample and research methods which are suitable for the research purposes and subjects, then collect secondary data from audited financial statements on reliable websites and calculate the data according to the proposed research model

Step 4: Analyze the descriptive statistics in the model and the correlation coefficient of the regression model between a dependent variable and one or other independent variables and between an independent variable and other independent ones

Step 5: Estimate the regression coefficient by using three widespread models such as Pooled OLS, Fixed Effects Model (FEM) and Random Effects Model (REM) on Stata software

Step 6: Select the appropriate model according to the results of the regression analysis by using the Hausman test to choose the appropriate model between the FEM and REM models

Step 7: Examine for defects in the selected model by testing for autocorrelation with Wooldridge model, multicollinearity with VIF model and heteroskedasticity with Breusch-Pagan model

Step 8: If the regression model violates defects, the author will proceed to solve them by using the FGLS method

Step 9: The author will acquire from the regression results and compare the research results with the proposed hypotheses Lastly, analyze the research results and come to appropriate conclusions based on theoretical basis and practical results, as well as provide some managerial implications to boost the profitability of chemical corporations listed on VNX

Figure 3.2 illustrates the research process with nine steps in order, according to the detailed description above:

Source: combined by the author

METHODOLOGY

The author applied quantitative methods in the research by a statistical software to compute the data The following parts are four major stages to calculate and analyze data via Stata 15.0 software: descriptive statistics, correlation coefficient, regression analysis and defect testing

Identify the topic of the research

Choose research methods and collect data

Analyze descriptive statistics and correlation

Analyze the results and make recommendations

The purpose of doing descriptive statistics analysis is to provide basic features related to the dataset This step consists of calculating the average of the data (Mean), creating a table that show the frequency of each value in the dataset (Frequency Distribution), finding the gap between the maximum or minimum values (Range), measuring how spread out the values are from the Mean, and determining the average distance of data points from the Mean

Correlation analysis is used to evaluate how strongly a dependent variable affects to other independent variables, and how an independent variable affects each other By using a Pearson's correlation, the results will show whether correlation coefficients are statistically significant at a particular level of 1%, 5%, and 10% or not A result is closer to +1 that there is a positive correlation, while a result is closer to -1 means that there is a negative relationship between two variables A value near 0 indicates that there is almost no correlation between the two variables

This is the most important step in this analysis process Regression analysis is a set of statistical methods which is used to estimate the degree of influence between a dependent variable and one or more independent variables Understanding the relationships can help business administrators or investors know how to make improvements effectively, and boosting the profit returns of firms The set consists of Pooled OLS model, FEM and REM On the next step, the author uses Hausman test to select a suitable model between FEM and REM

The purpose of doing a defects test is to identify any defects that can cause the decrease in reliable results and check proposed hypotheses after conducting some diagnostics in Stata software Three kinds of defects that typically happen in most of regression models are heteroskedasticity, multicollinearity and autocorrelation, along with three testing models, including Breusch - Pagan test, VIF test and Wooldridge test respectively In case there does have a defect, for example, a heteroskedasticity, in a regression model, FGLS method would be used to overcome defects, hence improve validity and reliability of regression estimations.

RESEARCH MODEL

The references of the author’s model in this research are mainly based on Lan and Cong (2019) and Nurlaela et al (2019) Regarding the dependent variable, after summarizing the literature reviews, the author selects ROA as the representation the profitability of enterprises On the subject of independent variables, the author intends to use firm size (SIZE), financial leverage (LVR), liquidity (LIQ), firm growth (GR), debt ratio (DR) and total assets turnover (TAT) After that, the author will apply the quantitative method into the regression model by collecting the necessary data and testing them on STATA through many steps to judge the level of impact of these independent variables on the profitability (measured by ROA) of 23 different companies related to chemical industry between 2018 and 2022

To sum up, based on the theoretical foundation presented by the author in Chapter 2 together with the suggestion of multivariable regression model above, the empirical model is indicated as follows:

ROA t = α 0 + α 1 SIZE it + α 2 LVR it + α 3 LIQ it + α 4 GR it + α 5 DR it + α 6 TAT it + U it

Where: α 0 : intercept term α 1 - α 6 : Regression coefficients of independent variables

ROA: A dependent variable, the abbreviation of the return of assets and is calculated by Net Income / Average Total Assets

SIZE: An independent variable, reflecting the corporation’s scale and is calculated by Ln (Total Assets)

LVR: An independent variable, reflecting the corporation’s financial leverage and is calculated by Percentage change in EPS / Percentage change in EBIT

LIQ: An independent variable, reflecting the corporation’s liquidity and is calculated by Current Assets / Short-term Liabilities

GR: An independent variable, reflecting the corporation’s growth and is calculated by (Revenue of year n – Revenue of year (n-1)) / Revenue of year (n-1)

DR: An independent variable, reflecting the corporation’s debt ratio and is calculated by Total Debts / Total Assets

TAT: An independent variable, reflecting the corporation’s total assets turnover and is calculated by Sales / Total Assets

Among three indicators that have been mentioned in Chapter 2, the chosen one to be a dependent variable is ROA t , which is the abbreviation of Return on Assets and is calculated by dividing Net Income by Total Assets, then multiplying with 100% In fact, many authors in previous studies have applied ROA, for example, Kartikasari and Marisa (2016), Muhammad et al (2016), Trang and Phuong (2018), Huy et al (2020), Tiffany and Sufiyati (2023) as a measure of the firm’s profitability The following formula is the one used to measure ROA:

Due to the limitations of the geographic scope and the time scope, the author will analyze independent variables having impacts on the profitability of chemical joint stock enterprises listed on the Vietnam Stock Exchange from 2018 to 2022 Variables being used consist of:

Independent variable SIZE represents for firm size The firm size is a quantifiable measure in terms of scale that a business operates (Brigham and Houston, 2006) The scale of a firm is a representative of a firm’s position in the market and its creditability towards other businesses, banks, and the public Despite having many factors to measure such as total assets, total sales, and market value of equity, the most common formula of firm size is the natural logarithm of total assets Since total assets of each company differ from each other, using total assets to calculate can give clearer value than the other two In regard to the theory above, firm size is measured by the following formula:

Firm size = Ln (Total Assets) (3.2)

Independent variable LVR represents for financial leverage Initially, leverage is a word borrowed from physics and commonly used as an economic term at the present time In general, financial leverage is a percentage of changes in operating profit or EBIT on the levels of earning per share (EPS) Financial leverage is useful for financial managers to choose the optimal capital structure of the corporation It is usually considered as an investment strategy that allows corporations to collect capital at short notice and expect for higher return than the debt the corporations have to pay back Based on the theory above, the factor is measured by dividing the change in percentage of EPS by the change in percentage of EBIT, like the following formula:

Financial Leverage = Percentage change in EPS

Percentage in EPS = Increase in EPS

Percentage in EBIT = Increase in EBIT

Independent variable LIQ represents for liquidity Kasmir (2015) defines liquidity as the difference between current assets and current debt on the firm’s balance sheet It is also considered as the ability of a firm to convert assets into cash The more liquid a company is, the more easily its property turns into cash, which enhances the financial flexibility to have adequate cash for urgent incidents and balance their finances In regard to the theory above, liquidity is measured by the following formula:

Independent variable GR represents for firm growth Firm growth is generally described as a change in an investment, or a firm’s revenue In this study, the author assumes growth rate is the annual change of a company’s earnings as a percentage Business administrators can examine this ratio to determine current performance of the corporation and predict the performance in the near future In regard to the theory above, growth rate is measured by dividing the gap between the revenue in two consecutive years by the revenue of the year before year n, then multiplying with 100%, like the following formula:

Firm Growth = Revenue of year n - Revenue of year (n-1)

A growth rate can be positive, negative or equal 0 A firm growth is positive indicating that the variable is rising over time and vice versa in case of a negative growth rate If the growth rate equals 0, it means the variable is stable over time.

Independent variable DR represents for debt ratio According to Kasmir (2014), debt ratio is defined as a difference between the total debt and total assets, reflecting how much the firm’s debt affects the assets management If the debt ratio is over 100%, it means that the amount of debt already exceeds the assets and vice versa The investors can base on this percentage to judge a company’s risk level In regard to the theory above, debt ratio is measured by the following formula:

A ratio greater than 1 shows that the company has more liabilities than assets, resulting in the risk of default on its loans if interest rates suddenly go up A ratio less than 1 shows that the company has more assets than liabilities, thus has lower chance of insolvency.

Independent variable TAT represents for total assets turnover According to

Investopedia, total assets turnover expresses the interaction between a company’s sales and its assets It can be used as an indicator to determine the efficiency level of a firm to generate profits from the assets as well Investors can depend on total assets turnover ratio to compare corporations in the same sector or industry For instance, investors can compare Long Chau Company’s total assets turnover with Pharmacity company’s total assets turnover since they are both in pharmaceutical sector In regard to the theory above, total assets turnover is measured by the following formula:

There have been different signs and impact level in the relationship between firm size and profitability in previous studies For example, Charles et al (2018), Alghusin (2015) and Biger et al (2008) claimed that firm size affected positively the profitability These studies indicated that the larger the firm scale was, the more capital invested which led to the rise in company’s financial performance in the public’s point of view On the contrary, a negative impact of firm size on profitability was found in Tarihoran and Endri’s research (2021) and Kartikasari and Marisa’s research (2016) In these investigations, it was indicated that the bigger the firms were, in terms of total assets, the less profits were gained because of the amount of expenses the firms had to pay, including both tangible and intangible assets, maintenance fees, purchase fees, shareholders’ dividends, and so on In some other cases, such as Matthijs (2018), Trang and Phuong (2018) as well as Hossam (2024), the studies showed no a statistically significant relationship between scale and profitability In this study, the author expects a positive effect between a company’s size and its profitability

Hypothesis H1: Firm size has a positive effect on the profitability of listed chemical enterprises

Harris and Raviv (1991), Rajan and Zingales (1995) along with Charles et al (2018) proved a negative impact between the financial leverage and the profitability It meant that when the leverage increases, the company will be less profitable due to the rise in marginal cost or capital Other studies such as Trang and Phuong (2018), Kartikasari and Marisa (2016), Nurlaela et al (2019) concluded that financial leverage influenced positively on the profitability It reflected that the company would invest more funds to gain profits, but to a certain extent Financial leverage is only beneficial when the borrowed payments are lower than the earnings, so if the leverage is too high, it will reduce the company’s value On the other hand, Alpi (2018) found that there was no relationship between leverage and the profitability in his research scope In this study, the author expects a negative effect between financial leverage and its profitability

Hypothesis H2: Financial leverage has a negative effect on the profitability of listed chemical enterprises

Different researches show various reviews depending on their economic environments Nurlaela et al (2019), Lan and Cong (2019) together with Han et al (2021) revealed a positive impact between the profitability and the liquidity Since maintaining the liquidity at a reasonable level can help the business operations run smoothly and reduce external expenses, reflecting a high profitability On the contrary, Alarussi and Gao (2023) as well as Charles et al (2018) found that liquidity affected negatively on the profitability in their cases since keeping the liquidity at a high ratio might lose some business opportunities and result in the risk of profits going down Linh and Nga (2022), Amit et al (2005) found that there was no statistics showing the relationship between the profitability and liquidity In this study, the author expects a positive effect between a company’s liquidity and its profitability

Hypothesis H3: Liquidity has a positive effect on the profitability of listed chemical enterprises

RESEARCH RESULTS

DESCRIPTIVE STATISTICS

Table 4.1 shows the descriptive statistics among the variables with a total of

115 observations of 23 chemical firms listed on the VNX for five years from 2018 to 2022 in the model:

Table 4.1 - Descriptive statistics of variables

Variable Obs Mean Std dev Min Max

Source: calculated by the author

Due to a massive outbreak of COVID-19 pandemic that happens during the research period, the economy in Vietnam has been affected heavily in different sectors In particular, as can be seen from the table, the average ROA of chemical firms is 6.463%, of which Duc Giang Chemicals Group JSC (DGC) stands out with the highest ROA (50.8% in 2022) while Da Nang Plastic JSC (DPC) records the lowest ROA (-20%) in the same year Because of a sudden upward trend in demands for face masks and other personal protective equipment, together with the commencement of vaccination programs during COVID-19 pandemic, the profitability of pharmaceutical firms rockets up, while chemical manufacturing companies, particularly plastics producing companies, are in an opposite state The standard deviation of ROA is 9.32% meaning that this variable does not have varying data

The calculations show the average value SIZE is 27.5136, as the highest value belongs to Petrovietnam Fertilizer and Chemicals Corporation (DPM) with 30.5045, and the lowest value SIZE is 24.4364, recorded by DPC Besides, the standard deviation is 1.6437, which is higher than the average value SIZE, indicating that the level of firm size in this model is significantly diverse

The mean value of LVR is 49.075%, with the highest value owned by Habac Nitrogenous Fertilizer and Chemicals JSC (DHB) (124.629% in 2021) and the lowest one is 5.5976% from DPC in 2018 The meaning of having a standard deviation approximately 25.977% is that this variable LVR does not have varying data

The calculations show the value LIQ ranging from 0.2 to 17.3 Specifically, DPC has the highest liquidity value, while the lowest value belongs to DHB As the matter of fact, DHB is a firm that has the lowest levels of liquidity among chosen companies during the whole five-year research period, with the ratio only around 0.2 to 0.3 Besides, the standard deviation is 1.8277 indicating that the level of liquidity in this model is not very diverse

The results show the average value GR is 18.615%, as the highest value is owned by DGC at the beginning of the period with 873.5%, and the lowest value

GR is -56.9%, recorded by Dong Nai Rubber Construction JSC (CDR) at the end of the period On the other hand, the variation of firm growth in this model is greater than the average value (85.241% > 18.615%), which shows that this variable is dramatically diverse in the model

The mean value of DR is 0.2843, with the values vary from 0 to 0.8 In other words, those corporations have the assets funded by equity instead of loans When debt ratio equals 0, it points out that in this year the company does not have any debts and relies entirely on equity financing to pay for its operating activities The meaning of having a standard deviation approximately 0.2368 less than the average value is that this variable DR does not have varying data

The last variable to be analyzed is total assets turnover (TAT) The calculations show the value TAT ranging from 0.24607 to 13.0581 Specifically, Central PetroVietnam Fertilizer and Chemicals JSC (PCE) has the highest total assets turnover value, while the lowest value belongs to BaRia Rubber JSC (BRR) The distribution of TAT in this research model is greater than the average value of the TAT, indicating that the total assets turnover variable in the research model is considerably various.

EMPIRICAL RESULTS

In order to find out the relationship and its degree between a dependent variable and other independent ones, the author will use Pearson’s correlation Table 4.2 gives information about the correlation matrix between variables in the model:

Table 4.2 - Correlation matrix of variables

ROA SIZE LVR LIQ GR DR TAT

Note: *, **, *** correspond to significance level of 10%, 5% and 1%

Source: calculated by the author

The estimations in Table 4.2 show that there are strongly positive connections between SIZE, LIQ, GR, TAT and ROA In contrast, the signs between ROA and LVR with DR are negative indicating that when financial leverage or debt ratio goes down, ROA will decrease However, the degrees of relationship are diverse among the variables The level impact of variables SIZE, LVR, LIQ, GR, DR, and TAT are 0.3194, -0.5221, 0.3309, 0.3513, -0.5262, and 0.1201 respectively Since SIZE, LIQ, TAT and GR have correlation coefficients between 0.3 and 0.5, their relationships with ROA are considered weakly positively correlated As LVR and

DR have correlation coefficients between 0.5 and 0.7, their relationships with ROA are considered moderately negatively correlated Besides, all variables above are statistically significant at the 1% level, except for TAT

About the correlation between independent variables, almost none of these coefficients are above 0.8, except for the one between LVR and DR, with the correlation coefficient = 0.8879 (> 0.8) This is considered a strong correlation coefficient, which means the two variables’ data sets are strongly linked together, for instance, when the value of LVR increases, the value of DR increases in a predictable manner relative to LVR It is also one of the warning signals that there might be multicollinearity phenomenon in the model To make sure that whether it exists or not, a VIF test will be carried out in the next part

Multicollinearity is defined as a phenomenon occurring when several independent variables in a model are correlated Testing whether there is multicollinearity in the model or not is necessary because the bigger the standard error among independent variables’ values, the less trustworthy results come out Therefore, to examine the presence of multicollinearity in the research model, the author intends to use the variance inflation factor test (VIF), with two hypotheses as follows:

Hypothesis 𝐻 0 : The model does not have multicollinearity

Hypothesis 𝐻 1 : The model has multicollinearity

Source: calculated by the author

As can be seen from the Table 4.3, all of independent variables have VIF values below 10 and tolerance value (known as 1/VIF) over 0.1, meaning that the VIF values imply that multicollinearity would not be present Therefore, the author can conclude that hypothesis 𝐻 0 , is not rejected, hence the model has no multicollinearity The estimated results in the research model are proper to be applied for next steps

By running all three kinds of regression models via Stata 15.0, the author is able to know the effect direction and the degree of influence of each independent variable to the dependent variable ROA corresponding to each kind of regression model

Table 4.4 - Model Estimation Pooled OLS

Variables Coefficient Std.Dev P-value

Note: **, *** correspond to significance level of 5% and 1%

Source: calculated by the author

As can be seen from the Table 4.4, the variable SIZE and GR both affect in the same direction to ROA and are statistically significant at the 1% level, while DR affects ROA oppositely and has statistical significance at the 5% level Other variables LVR, LIQ, and TAT show no statistical significance in this model The R2 coefficient is 0.4929, illustrated that the model explains 49.29% of the variability of the data The Pooled OLS model is now written as follows:

Variables Coefficient Std.Dev P-value

Note: **, *** correspond to significance level of 5% and 1%

Source: calculated by the author

In Table 4.5, the variables SIZE and GR both have the same sign of affect towards ROA and are statistically significant at the 1% level, while LVR and DR are statistically significant at the 5% level but affect ROA conversely Other variables LIQ and TAT show no statistical significance in the fixed effects model

The R2 coefficient is 0.4137, showing that 41.37% of the variability in the dependent variable ROA is explained by the independent variables of the model The FEM is now written as follows:

ROA = - 4.09189 + 0.15761*SIZE - 0.29165*LVR + 0.02157*GR - 0.19339*DR

Variables Coefficient Std.Dev P-value

Note: **, *** correspond to significance level of 5% and 1%

Source: calculated by the author

Similar to Pooled OLS model, the variables SIZE and GR illustrated in Table 4.6 both have the same sign of affect towards ROA and are statistically significant at the 1% level, while the only variable with statistical significance at 5% are DR and it affects ROA conversely Other variables LIQ, LVR and TAT show no statistical significance in the random effects model The R2 coefficient is 0.4901, showing that the independent variables of the model explain 49.01% of the variability of the data The REM is now written as follows:

F-test is a technique used to evaluate between Pooled OLS model and FEM, which model is more suitable If the value of Prob > chi2 is greater than 0.05, the chosen model will be Pooled OLS model In contrast, If the value of Prob > chi2 is less than 0.05, FEM will be the chosen one The hypotheses used for the test are as follows:

Hypothesis 𝐻 0 : Pool OLS model is more appropriate

Hypothesis 𝐻 1 : FEM is more appropriate

The results show that the value of (Prob > chi2) is less than the significance level (0.0000 < 0.05), hypothesis 𝐻 0 will be not be accepted Hence, fixed effects model (FEM) is feasible to be used to explain the effect of variables

Hausman test, an abbreviation of Durbin–Wu–Hausman test, is a test named after James Durbin, De-Min Wu and Jerry A Hausman It is used to evaluate between FEM and REM, which model is more acceptable as a regression model Besides, the method is performed to consider the presence of correlation between

𝑈 𝑖𝑡 and other independent variables as well If the value of Prob > chi2 is greater than 0.05, the chosen model will be REM In contrast, If the value of Prob > chi2 is less than 0.05, FEM will be the chosen one The hypotheses used for the test are as follows:

Hypothesis 𝐻 0 : REM is more appropriate

Hypothesis 𝐻 1 : FEM is more appropriate

Since the value of (Prob > chi2) is less than the significance level (0.0075 <

0.05), hypothesis 𝐻 0 will be rejected Hence, fixed effects model (FEM) is the chosen model in this research

Autocorrelation represents the correlation between the current value of a variable at time t and its past values to find out a trend in a specific period A value of is ranged from -1 to 0 reflects negative autocorrelation and vice versa when a value of autocorrelation is ranged between 0 and 1 To examine whether there is autocorrelation among variables in the research model or not, the author intends to use the Wooldridge test with two hypotheses as follows:

Hypothesis 𝐻 0 : The model does not have autocorrelation among variables Hypothesis 𝐻 1 : The model has autocorrelation among variables

Wooldridge test for autocorrelation in panel data Hypothesis H0: no first-order autocorrelation

Source: calculated by the author

Based on the Table 4.9, since Sig value (Prob > F) = 0.0014 (0.14%) < 0.05 (5%), there is evidence to decline hypothesis H0 In other words, hypothesis 𝐻 0 is rejected and hypothesis 𝐻 1 is accepted Consequently, it can be concluded that the model has autocorrelation between variables

DISCUSSIONS

After analyzing all the research results in previous parts, the author will discuss the effects between a dependent variable, measured by Returns on Assets (ROA), and independent variables based on the final model (FGLS model) Next, the author will make a comparison between the research results and the initially proposed hypotheses, along with those studies in the past

The regression coefficient 0.0074292 shows a positive relationship between firm scale and profitability, which means that when financial leverage increases by 1%, the average ROA goes up by 0.0074292 times As a result, chemical companies with a larger scale tend to have fewer difficulties in attracting external investments and accessing loans, leading to the increase in profitability However, this variable illustrates a statistically insignificant result The findings of this study are not in line with Charles et al (2018), Han et al (2021), together with Alarussi and Gao (2023) The reason for this contradiction can depend on intrinsic aspects such as firms’ characteristics, industry, or ownership

The results show a negative and statistically significant relationship between financial leverage and profitability at the significance level of 5%, implying that the results statistically explain the connection between the two variables The regression coefficient of LVR is -0.0798163, which means that when financial leverage increases by 1%, the average ROA decreases by 7.98% Consequently, chemical companies with a lower degree of leverage tend to have higher profitability Other studies having similar findings that total assets turnover influences adversely the profitability are Rajan and Zingales (1995), Alghusin (2015) and Han et al (2021)

The estimations show a negative relationship between liquidity and profitability The regression coefficient of LIQ is -0.0004297, illustrating that when liquidity increases by 1%, the average ROA decreases by 0.0004297 times In consequence, companies in chemical industry with a lower degree of liquidity have higher profitability Despite that, the value is insignificant and not able to statistically confirm that there is any relationship between the liquidity and profitability Amit et al (2005), along with Linh and Nga (2022) also receive the same findings as this research in other sectors

Firm growth has the regression coefficient in case of ROA is 0.0204504 and has a statistically significant estimation at the 5% level As the result, if the growth rate of a firm increases by 1%, the ROA increases by 2.045% In fact, the increase in revenue growth is considered as one of positive signals when investors evaluate a firm to invest With this, the corporate value on the market is respectively higher due to high demand on investments, resulting in companies being more profitable The findings of this study correspond to the research results of Alghusin (2015), Charles et al (2018) and Han et al (2021)

The results show a negative and statistically significant relationship between financial leverage and profitability at the 10% significance level The regression coefficient of DR is -0.0569468, which means that when financial leverage increases by 1%, the average ROA decreases by 0.05695 units Thus, chemical companies with a lower degree of debt ratio tend to have higher profitability Besides, a low debt ratio is one of the vital criteria when creditors appraise a firm’s ability to pay loans The findings of this study appear to contradict some previous studies’ results such as Wippern (1966), Margaritis and Psillaki (2010), as well as Dao et al (2022) On the other hand, investors do not really fond of investing an enterprise with extremely low debt ratios A debt ratio of zero demonstrates that the company does not use any loans or borrowings to finance operating activities at all, which limits the return that shareholders should be received

Hypothesis H6 about Total assets turnover:

The regression coefficient of TAT -0.0798163 means that total assets turnover ratio affects negatively the profitability, in other words, when total assets turnover decreases by 1%, the ROA of a firm increases by 0.0798163 times Consequently, chemical companies with a lower degree of total assets turnover tend to have higher profitability However, the coefficient is insignificant and not able to statistically confirm any relationship between the liquidity and profitability The findings of this study are in common with Sunjoko and Arilyn (2016), together with Hasna (2019)

Table 4.12 below is the summary of the research results after conducting quantitative methods by calculating the data on Stata 15.0 Software:

Table 4.10 – Summary of research results

Variables Expected Results Actual Results

Source: combined by the author

In conclusion, three out of six variables have the sign of impact as expected, while the others are not statistically significant, including financial leverage, firm growth, and debt ratio

In this chapter, the author provides some information about the current situation that happened in chemical industry in Vietnam throughout the research period from 2018 to 2022 At the following part, results of the research model are analyzed in detail, insisting of six calculating stages after collecting data and building model, including descriptive statistics, correlation analysis, regression analysis, selecting suitable model, testing and resolving defects in model Finally, summarize the result analysis for the next chapter.

CONCLUSIONS AND RECOMMENDATIONS

CONCLUSION

One of key objectives that any business would aim to is to maximize the profits as much as possible and improve firm performance The more profits a company gains, the more benefits that shareholders can receive, leading to enhancing the company value in the market Besides, enhancing the company value in the market helps attracting more investments to finance business operations, hence increasing the profitability That is how a cycle is created This is the reason why there has been a large quantity of researches studying the determinants affecting the profitability, especially empirical studies in different countries and sectors The financial crisis occurred in recent years due to COVID-19 pandemic makes business administrators and investors become more and more concerned about this topic

With the topic “ Factors affecting the profitability of chemical manufacturing enterprises listed on the Vietnam Stock Exchange” , the author has focused on researching and analyzing internal factors affecting the profitability of corporations in chemical industry This study has used a quantitative approach for analyzing data based on the data acquired from 23 companies in chemical sector listed on the Vietnam Stock Exchange during the period of five years, between 2018 and 2022, obtaining 115 observations After carrying out regression models, including Pooled OLS, FEM and REM, the study comes to the conclusion that the FEM is the most appropriate model among the three However, the regression model is tested to appear autocorrelation and heteroskedasticity In order to solve these defects to ensure the effectiveness of the model, the author decided to use Feasible Generalized Least Squares (FGLS) In the end, results show only three variables have statistical significance at 5% and 10%, consisting of financial leverage (LVR) and firm growth (GR) at the 5% level, while debt ratio (DR) has statistically significant at the significance level of 10%

Based on the FGLS model, the research results show that financial leverage (LVR) and debt ratio (DR) affect negatively the profitability while firm growth variable (GR) has a positive relationship with ROA, a representative of the profitability Whereas, the other three variables exceed the significance level, including firm size (SIZE), liquidity (LIQ) and total assets turnover (TAT), thus they are not statistically significant in this research As a result, this study will contribute to the literature of relationship between internal factors and profitability by proving its negative relationship between financial leverage with debt ratio and the profitability, along with its positive relationship between the growth rate of a firm and the profitability.

RECOMMENDATIONS

First of all, since the profitability is concluded to be strongly affected by financial leverage (LVR), debt ratio (DR) and firm growth (GR), business managers and investors should pay more attention on the changes of these factors

Companies should focus on investing the Research and Development Department for new products and services Improving financial performance more efficiently so as to increase the company’s value towards investors, other firms and the public is also a way to increase the profitability Specifically, if a company enhances its value, it will receive a high trustworthiness among investors to make a decision of buying its stocks, consumers and clients will have more faith to purchase its products and services, hence get a higher position in the market and increase the profitability thanks to it Aside from that, exploring opportunities to diversify revenue streams and reducing reliance on a single product or market can help mitigate risks and improve long-term growth prospects

The fastest method to decline leverage is to deleverage the company Deleveraging is a term referring to a situation when companies try to find methods in order to cut down on its financial leverage or its debt listed on the balance sheet One of the most direct ways to deleverage is to pay off any existing debts and obligations on its balance sheet as soon as possible by using excess cash from operational activities or selling off non-core assets or bonds at a discount These methods can improve credit risks in banks’ point of view, hence have a higher chance to borrow loans from banks in the future On the other hand, this can be considered as a bad signal for investors because it means a company is not capable of achieving a level of growth required to pay off its debt, resulting in a decline in stocks price for the company

By the way, instead of relying solely on debt financing, businesses should consider raising equity capital through issuing new shares or seeking investment from venture capitalists or private equity firms This helps firms strengthen their financial positions and reduce financial leverage

The important thing a firm needs to do is setting the optimal level of debt ratio considering the company's risk tolerance, the market conditions and financial objectives Based on the level of debt ratio, the company can refinance existing debt by looking for areas in which costs can be reduced without affecting operations then implementing cost-cutting measures Negotiating with creditors with the aim of extending payment periods or reducing interest rates can help decreasing monthly payments and improve the current state of debt ratio

Also, the company can use the money from bond issuance as another available option to pay off existing debts for the purpose of preventing debt ratio from exceeding the break-even point and utilizing the advantages of tax benefit that debt ratio brings about By issuing bonds with reasonable interest rates, the company can decline its interest expenses and debt ratio to an extent.

LIMITATIONS OF THE THESIS AND NEW APPROACHES

Although the initial research objectives have been accomplished, the author still cannot avoid some noticeable limitations in the implementation of the topic, specifically as follows:

The first limit is the sample scope The findings of this study might be acceptable in a particular period, but they have a high chance to change if the time scope is different Apart from that, the author not only collected secondary data from companies listed on HOSE and HNX but also from UPCoM Nevertheless, the data of companies collected from UPCOM might lack the transparency and have higher risks than those listed on HOSE and HNX, making parts of the data less reliable, not to mention the unlisted chemical companies

Secondly, the author only used ROA as a sole measurement for the profitability in this study Relying solely on ROA may provide a limited perspective on the overall financial performance of a company, causing misinterpretation of results Changes in ROA could be influenced easily by factors unrelated to operational efficiency, such as changes in economic policies or rare events, leading to inaccurate conclusions

Thirdly, the thesis only focused on quantitative factors to evaluate factors but did not take other qualitative factors like competitiveness among companies in the same sector, brand reputation, customer satisfaction, and so on

Lastly, some of the actual results in the author’ thesis turn out to be different from the expectations in the beginning This happens because of the appearance of unwanted interference factors or uncontrolled external factors that affect the results

Or else, the chosen samples probably do not reflect the true characteristics of the population, causing the change in expected results

5.3.2 New approaches for future studies

According to the limitations above, the author suggested a few future research approaches as follows:

Further studies in the future can increase the number of observations by extending the study period before 2018 When the number of observations is large, the accuracy of the study is also improved, however, to ensure the explanation of the impact variables are precise, a large number of observations is required for the study

Replacing or adding more variables is other good ways for new researches in the future There are other options such as ROE, ROI, NIM (Net Interest Margin), NPM (Net Profit Margin), CFM (Cash Flow Margin), etc Researchers can use two or more than one dependent variable to do the investigation

Adding more quantitative options like GDP, inflation rate, monetary policy, taxes, stocks change, human resources, etc or combining with qualitative factors like brand reputation, competitiveness among companies, customer satisfaction, product quality, etc to broaden the perspectives of this topic This can make the final results more thorough and accurate when evaluating the independent variables having an impact on the profitability of enterprises in a particular industry

In Chapter 5, the author comes to the conclusion that financial leverage and debt ratio have a pretty high impact on the profitability of a company in a negative way, whereas the increase in firm growth affects positively the profitability On the other hand, the other variables including firm size, liquidity, and total assets turnover still influence the profitability in spite of not being statistically significant on the table According to the results received from the regression model, some recommendations are proposed to improve the ability of gaining profits

The author empathizes the limitations about the sample scope and research results in this study Future research directions can exceed the sample scope, or add more external and internal factors, or change the dependent variables to make the literature related to this topic more comprehensive

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List of chosen chemical corporations listed on the VNX

1 AAA An Phat Bioplastics Joint Stock Company

2 BBS VICEM Packaging But Son Joint Stock Company

3 BFC Binh Dien Fertilizer Joint Stock Company

4 BRC Ben Thanh Rubber Joint Stock Company

5 BRR BaRia Rubber Joint Stock Company

6 CDR Dong Nai Rubber Construction Joint Stock Company

7 CSV South Basic Chemicals Joint Stock Company

8 DAG Dong A Plastic Joint Stock Company

9 DCM Petro Viet Nam Ca Mau Fertilizer Joint Stock Company

10 DGC Duc Giang Chemicals Group Joint Stock Company

11 DHB Habac Nitrogenous Fertilizer and Chemicals Joint Stock

12 DPC Da Nang Plastic Joint Stock Company

13 DPM Petrovietnam Fertilizer and Chemicals Corporation

14 DPR Dong Phu Rubber Joint Stock Company

15 HNP Hnel Plastics Joint Stock Company

16 HSI General Materials Biochemistry Fertilizer Joint Stock

17 MCP My Chau Printing and Packaging Holdings Company

18 NFC Ninh Binh Phosphate Fertilizer Joint Stock Company

19 PBP PetroVietnam Packaging Joint Stock Company

20 PCE Central PetroVietnam Fertilizer And Chemicals Joint Stock

21 PMP Dam Phu My Packaging Joint Stock Company

22 TPC Tan Dai Hung Plastic Joint Stock Company

23 TPP Tan Phu Viet Nam Joint Stock Company

Source: compiled by the author

ROA of 23 chemical firms from 2018 to 2022

Source: compiled by the author

Firm Size of 23 chemical firms from 2018 to 2022

Source: compiled by the author

Financial Leverage of 23 chemical firms from 2018 to 2022

Source: compiled by the author

Liquidity of 23 chemical firms from 2018 to 2022

Source: compiled by the author

Firm Growth of 23 chemical firms from 2018 to 2022

Source: compiled by the author

Debt Ratio of 23 chemical firms from 2018 to 2022

Source: compiled by the author

Total Assets Turnover of 23 chemical firms from 2018 to 2022

Source: compiled by the author

Descriptive Statistics

Correlation Matrix at P-value = 1%

Variable Obs Mean Std dev Min Max

summarize ROA SIZE LEVERAGE LIQ GROWTH DEBT TURNOVER

ROA SIZE LEVERAGE LIQ GROWTH DEBT TURNOVER

pwcorr ROA SIZE LEVERAGE LIQ GROWTH DEBT TURNOVER, star(0.01) sig

Correlation Matrix at P-value = 5%

Correlation Matrix at P-value = 10%

ROA SIZE LEVERAGE LIQ GROWTH DEBT TURNOVER

pwcorr ROA SIZE LEVERAGE LIQ GROWTH DEBT TURNOVER, star(0.05) sig

ROA SIZE LEVERAGE LIQ GROWTH DEBT TURNOVER pwcorr ROA SIZE LEVERAGE LIQ GROWTH DEBT TURNOVER, star(0.1) sig

Regression Results of Pooled OLS Model

Regression Results of Fixed Effects Model

ROA Coefficient Std err t P>|t| [95% conf interval]

Source SS df MS Number of obs = 115

reg ROA SIZE LEVERAGE LIQ GROWTH DEBT TURNOVER

F test that all u_i=0: F(22, 86) = 3.54 Prob > F = 0.0000 rho 95146093 (fraction of variance due to u_i) sigma_e 05537444 sigma_u 24516535

ROA Coefficient Std err t P>|t| [95% conf interval] corr(u_i, Xb) = -0.9667 Prob > F = 0.0000

Group variable: TICKER Number of groups = 23

Fixed-effects (within) regression Number of obs = 115

xtreg ROA SIZE LEVERAGE LIQ GROWTH DEBT TURNOVER, fe

Regression Results of Random Effects Model

Hausman Test

est sto rem rho 10675044 (fraction of variance due to u_i) sigma_e 05537444 sigma_u 0191429

ROA Coefficient Std err z P>|z| [95% conf interval] corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000

Group variable: TICKER Number of groups = 23

Random-effects GLS regression Number of obs = 115

xtreg ROA SIZE LEVERAGE LIQ GROWTH DEBT TURNOVER, re

Test of H0: Difference in coefficients not systematic

B = Inconsistent under Ha, efficient under H0; obtained from xtreg. b = Consistent under H0 and Ha; obtained from xtreg. TURNOVER 0103151 0040942 0062209 0096947

SIZE 1576077 0193044 1383033 0324474 fem rem Difference Std err.

VIF Test

Wooldridge Test

Breusch and Pagan Test

Wooldridge test for autocorrelation in panel data

xtserial ROA SIZE LEVERAGE LIQ GROWTH DEBT TURNOVER

H0: sigma(i)^2 = sigma^2 for all i in fixed effect regression model

Modified Wald test for groupwise heteroskedasticity xttest3

FGLS Estimated Model

_cons -.1044324 1246722 -0.84 0.402 -.3487854 1399207 TURNOVER 0021224 005952 0.36 0.721 -.0095432 0137881 DEBT -.0569468 0319803 -1.78 0.075 -.119627 0057334 GROWTH 0204504 0082979 2.46 0.014 0041869 0367139 LIQ -.0004297 0047975 -0.09 0.929 -.0098326 0089732 LEVERAGE -.0798163 035971 -2.22 0.026 -.1503182 -.0093144 SIZE 0074292 0045502 1.63 0.103 -.0014892 0163475 ROA Coefficient Std err z P>|z| [95% conf interval] Prob > chi2 = 0.0000 Wald chi2(6) = 40.29 Estimated coefficients = 7 Time periods = 5 Estimated autocorrelations = 1 Number of groups = 23 Estimated covariances = 23 Number of obs = 115 Correlation: common AR(1) coefficient for all panels (0.6046)

Cross-sectional time-series FGLS regression

xtgls ROA SIZE LEVERAGE LIQ GROWTH DEBT TURNOVER, corr(ar1) panels(h)