The Impact Of The Covid-19 Pandemic On The Profitability Of Steel Industry Enterprises Listed On The Vietnam Stock Market.pdf

MINISTRY OF EDUCATION AND TRAINING THE STATE BANK OF VIETNAM HO CHI MINH CITY UNIVERSITY OF BANKING GRADUATION THESIS THE IMPACT OF THE COVID-19 PANDEMIC ON THE PROFITABILITY OF STEEL IN

INTRODUCTION

REASONS FOR CHOOSING THE TOPIC

A well-developed iron and steel industry is crucial for a country's economy proactively and solidly The World Steel Association Report (2019) highlights steel's significance as a vital component for various industries, from hand tools to complex machinery and infrastructure Essentially, the steel industry acts as a foundation for other sectors, providing essential raw materials (WSA, 2019) Furthermore, this labor-intensive industry significantly contributes to the national budget (Ministry of Finance of Vietnam, 2016), making it a key economic sector deserving of substantial investment and development

Steel's extensive applications underscore its adaptability and environmental friendliness, as it is 100% recyclable Its low production cost compared to other materials also explains its widespread use Important steel products, such as steel cables, bars, and plates, enable high-pressure boilers and robust machinery In Vietnam, the steel industry has thrived, becoming one of the most advanced sectors in the country Vietnam emerged as the leading steel producer in Southeast Asia, significantly boosting economic development and creating numerous jobs In 2021 and 2022, Vietnam ranked 13th globally in crude steel output, producing 23 million and 20 million tons, respectively (WSA, 2021 & 2022)

However, the emergence of the COVID-19 pandemic not only affects the world economy in general and Vietnam in particular, but the epidemic also endangers human life As of March 17, 2024, more than 774 million infections and more than 7 million deaths from COVID-19 have been recorded worldwide (World Health Organization, 2024) The spread of the pandemic also caused extremely serious damage to the financial system and the global economy (Bakas & Triantafyllou,

2020), paralyzing economies with both financial and economic shocks (Rizvi & al.,

2020) Most manufacturing countries around the world began implementing blockade measures as a last resort to prevent the spread of Covid-19 As a result of the pandemic, an estimated 2.7 billion people or more than four-fifths of workers in the global workforce have been affected by lockdowns and stay-at-home measures (Deloitte Insights, 2020) The steel industry was also seriously affected WSA announced that global steel production decreased by 1.4% in the first 3 months of

2020 due to several main reasons such as reduced automobile production, suspended construction projects, or reduced demand from the energy industry shoot, etc

In Vietnam, the first case of SARS-CoV-2 infection was recorded at the end of January 2020 in Ho Chi Minh City (Ministry of Health, 2020) The government issued blockade and quarantine orders, causing the economy to begin to face difficulties, especially production and business activities of enterprises and import- export activities Most businesses have encountered negative impacts on business results during the outbreak of the COVID-19 pandemic, affected by several State policies, including interest rates, inflation, and other policies According to the Vietnam Steel Association (2020), the COVID-19 pandemic has affected market congestion and goods cannot circulate Growth in Vietnam's steel products and consumption in 2019 only reached 4.4% and 6.4%, much lower than the double-digit growth of 14.9% and 20.9% in 2018 (VSA, 2020)

The COVID-19 pandemic significantly disrupted the development of export markets and trade and transportation between countries In the first half of 2020, domestic steel exporters faced difficulties obtaining export licenses in Indonesia due to the pandemic's impact, resulting in unfavorable conditions for flat steel exports (VCBS, 2020) Additionally, internal steel manufacturers relied heavily on importing raw materials and accessories from China The pandemic caused a slowdown or even a standstill in trade activities between Vietnam and China While existing inventory remained in warehouses, new shipments of raw materials ordered from foreign partners at the end of 2019 arrived at ports, compounding the challenges Amidst falling prices and stagnant goods circulation, many loans were due for repayment, and storage and warehousing costs increased These challenges arose even as most construction projects were halted, leading to a sharp decline in production and business efficiency for steel enterprises, resulting in losses (VSA, 2020)

The COVID-19 pandemic has posed an unprecedented challenge to humanity, endangering the lives of millions worldwide (Maksim Belitski et al., 2021) Numerous studies globally have examined the pandemic's impact on business performance and profitability across various sectors However, in Vietnam, there has been limited research on the steel industry, particularly regarding the effects of economic crises, natural disasters, or epidemics on business profitability Consequently, the author investigated "The impact of the COVID-19 pandemic on the profitability of steel industry enterprises listed on the Vietnamese stock market" to explore how the pandemic has affected these companies' profitability, considering both negative and positive impacts The study aims to offer recommendations for managing risks and enhancing the operational efficiency of steel industry enterprises, thereby contributing to the sustainable development of the economy.

RESEARCH OBJECTIVES

This study aims to examine the profitability of steel industry companies listed on the Vietnamese stock market affected during the COVID-19 pandemic From the research results, the thesis makes recommendations for future financial managers

First, analyze the impact of internal and external factors on the steel industry enterprises listed on the stock market in Vietnam

Second, analyze the influence of the COVID-19 pandemic on the lucrativeness of steel industry companies listed on the stock market in Vietnam

Third, recommend solutions to contribute to improving business efficiency for companies.

RESEARCH QUESTIONS

First, what is the extent of the impact of the COVID-19 pandemic on the profitability of listed steel industry companies in Vietnam?

Second, how do internal and external factors affect the gainfulness of steel industry firms listed on the stock market in Vietnam?

Third, what solutions can assist enhance the corporation operations of listed steel industry companies in Vietnam?

SUBJECT AND SCOPE OF RESEARCH

The research object of the project is the effect of the COVID-19 pandemic on

18 steel enterprises listed on the stock market in Vietnam

❖ Spatial scope: This thesis researches 18 steel industry enterprises listed on the stock market in Vietnam

❖ Time scope: The research data in the article is collected over time from 2012 to 2023.

METHODOLOGY

The thesis uses quantitative research methods on Stata software and analyzes available data in publicly announced financial reports of listed steel industry companies in 2012 - 2023 Data are taken annually, and collected at the end of the fiscal year

The study uses quantitative research methods, collecting data samples from 18 listed steel companies in the period from 2012 to 2023, equivalent to 216 observations Financial data are compiled and collected from reference sources Vietstock and CafeF Inflation rate and GDP data are obtained from the General Statistics Office of Vietnam (GSO)

The data analysis technique used in this study is panel data regression with 3 methods: pooled least squares estimation (POOLED OLS), estimation with a fixed effects model (FEM), and quantitative with a random effects model (REM) Then, the author will compare the regression results of each method through F-Test, Hausman, and multicollinearity tests to select the most suitable model In addition, the author also checks for autocorrelation through the Wooldridge test and calculates heteroscedasticity At the same time, to overcome the above two problems, the author will use the Feasible Generalized Least Squares (FGLS).

STRUCTURE OF THESIS

The research paper includes 5 main contents that need to be closely followed to achieve the research goals, including Introduction, Literature review, Methodology Research, Empirical Results, Conclusion, and Recommendations

This section introduces the main features of the research, including the reason for choosing the topic; research objectives and questions, subjects and scope of research, methodology, and practical contribution

This chapter demonstrates the theoretical basis and practical evidence from previous research In addition, at a higher level, identifies the knowledge gap in the research field of the topic (Research gap) that the thesis will partly contribute to filling (or all of) that knowledge gap From there, the thesis determines the research objectives and research questions

The goal is to specify the research method and data sources that the thesis will use to find the answer set out in the overview of the research problem In this chapter, the research model and related hypotheses are established

This chapter presents the results of data analysis (research results) and descriptive statistics for the significance of the variables in the model Besides, the calculation results and testing results are also illustrated through data tables, charts, and graphs, thereby analyzing and discussing those results in close connection with the analysis section in the overview section research problem

Related conclusions about the research problem are drawn in this section based on the research questions and research objectives In addition, several solutions and policies are proposed for future company administrators and business investors.

THE CONTRIBUTION OF RESEARCH

The research topic focuses on comprehending the impact of the COVID-19 pandemic on the lucrativeness achievement of steel firms listed on the Vietnamese stock exchange, supplying practical practices at enterprises Therefore, it can be seen that the performance of a business can be affected by factors originating from within the business or economic or non-economic events that take place to have an overview and multi-faceted view of the influence of COVID-19

A corporation that effectively manages its profitability will generate funds for sustainable development and attract new investments Based on the research findings, the author will offer recommendations for steel businesses to make timely and appropriate decisions to maximize their value and profits Furthermore, the research enhances the theoretical and practical understanding of COVID-19's impact on steel businesses and serves as a reference for future studies on similar topics

The author has shown the reason and urgency of the research topic, along with the questions raised in parallel with the goals to be achieved in this thesis In addition, with the research object mentioned above and the time range from 2012 to 2023, the author will strictly follow the process stated above to do so in this chapter.

LITERATURE REVIEW

THEORETICAL BASIC OF PROFITABILITY

Profit is the primary goal of all business activities Without profits, a corporation will not be able to survive in the long term Therefore, measuring current and past profitability and predicting future gainfulness is essential Profitability reflects a company's ability to generate profits through economic activities and resource use (Cojocaru, 2000) It performs the function of a fundamental consideration for all business decisions, including operations management and partner relationships Therefore, Lucrativeness becomes an essential criterion for evaluating a company's economic performance (Cojocaru, 2000) Besides, Ha et al

(2021) show that profitability is a financial indicator that reflects the effectiveness of the entire process of investment in performance, product consumption, technical solutions, and economic management at the enterprise

Profitability is one of the most vital goals of financial administration (Malik,

2011) The purpose of financial management is to maximize the owner's wealth, and profitableness is a significant deciding factor in evaluating the performance of a firm

In short, gainfulness is the business's capacity to generate profits and reflects the efficiency of that business's corporation operations The enterprise’s profitability plays a significant role in its production and business activities; is both a goal and a condition for the existence and development of businesses Regardless of the type of economic activity or resources engaged in or consumed, financial performance will ultimately be realized by the profits the enterprise earns

2.1.2 Criteria for evaluating the firm’s profitability

Profitability assessment plays a key role in financial analysis Hanafi & Halim

(2012) define lucrativeness ratios as metrics that evaluate a company's profit generation against various benchmarks like share capital, assets, and revenue These ratios ultimately reflect the effectiveness of a company's decisions and policies, as Brigham and Houston (2015) point out Financial ratio analysis is a common tool for profitability assessment, examining a company's financial health, operational performance, and profit levels (Susanti & Herawati, 2019) As argued by Bustami et al (2021), rates of return like ROA, and ROE serve as indicators of management's overall effectiveness in generating profits

There are many different ways to assess the enterprise’s profitability such as return on assets (ROA), Return on equity (ROE), Return on invested capital (ROIC), or Return on sale (ROS) Among them, ROA and ROE are two common measures, used by many researchers because of simple calculation

ROA measures a business's performance in using assets to generate after-tax profits, regardless of whether these assets are formed by capital or equity (Le et al.,

2016) The larger the ROA, the more effective the use of the business's assets In other words, with the same amount of assets, companies with greater ROA can generate greater profits and vice versa (Julita, 2008) ROA indicator shows the effectiveness of the process’s organizing, and managing production, and enterprise activities

The evaluation of ROA is very general, including some studies that use ROA to appraise a company's lucrativeness such as Stanwick (2000), Wilson (2005), Malik

The ROE index assists corporation managers to be aware of how much net profit the company will earn from the amount of equity the company spends on business (Nguyen, 2023) In addition, ROE demonstrates how much profit a dong of equity will generate after corporate income tax The higher the ROE index, the more effective the business's capital use process is (Morsy & Rwegasira, 2010)

As for the ROE criterion, many previous studies by researchers such as Chander & Aggarwal (2008), and Neneng Khoirial (2020) have also used this criterion to assess business profitability This indicator has a substantial meaning in determining whether the establishment goal of the enterprise's leadership is to maximize profits or maximize scale.

Related Theories

2.2.1 Theory of shocks affecting the economy

An economic shock denotes any alteration in fundamental macroeconomic factors or relationships that markedly influences macroeconomic results and indicators of economic effectiveness, such as unemployment, consumption, and inflation These shocks are frequently unforeseeable and typically arise from events considered beyond the realm of regular economic transactions They have far- reaching and enduring impacts on the economy, and according to the real business cycle theory (RBC), are thought to underlie recessions and economic cycles

Many renowned economists have examined and analyzed economic shocks in their work For example, Friedman, M (1956) explored the effects of monetary shocks and developed a monetary theory to control inflation and stabilize the economy Robert Lucas, Jr (1981) is well-known for the Rational Expectations Hypothesis and has studied supply shocks and their impact on the business cycle and Friedrich Hayek (1931) investigated the effects of economic shocks on the production structure and prices within the economy

The COVID-19 pandemic has been perceived as a substantial upheaval for the global economy, impacting not only Vietnam but the entire world It led to the collapse of supply chains, loss of lives, widespread unemployment among workers, and significantly disrupted all socio-economic activities Numerous studies and reports from leading economic organizations have recognized SARS-CoV-2 as a significant financial shock to the global economy For instance, the International Monetary Fund (2020) described COVID-19 as a crisis like no other due to the unprecedented scale and speed of the economic collapse caused by the pandemic Similarly, Reinhart, C & Rogoff, K (2020) identified the COVID-19 pandemic as a major economic shock affecting developing economies and global financial markets

This theory encompasses two primary approaches: the Structure-Conduct- Performance (SCP) and the Relative Market Power theory (RMP)

The Structure-Behavior-Performance (SCP) paradigm, originally proposed by Chamberlin and Robinson (1933), asserts that market structure determines the behavior of firms in that market, and that determines the outcomes of market participants If an enterprise dominates the market, its behavior is independent of other businesses in the market economy In other words, if a company holds too high a market share, it will be able to control the economic system, thereby forming monopoly prices and creating superior profits compared to other organizations or other industries Bain, J S (1951) further developed this theory, suggesting that highly concentrated industries can promote behaviors that lead to poor economic outcomes, such as reduced output and price manipulation

On the other hand, the relative market power (RMP) theory put forth by Smirlock (1985), posits that businesses with significant market shares and unique products or services can exert market power to achieve non-competitive profits (Berger, 1995) This theory holds that greater market concentration in a particular sector will result in higher profits due to the advantages provided by market power Therefore, there is a positive correlation between profitability and market power (Maudos & de Guevara, 2007) It is clear that large association with strong brands and high-quality products can enhance their services and achieve higher financial gain, supporting the hypothesis that market power increases through a scale expansion will promote business efficiency

The Efficiency Structure Hypothesis (ES) was initiated by Demsetz (1973) and Peltzman (1977) Different from the market power theory, the ES hypothesis believes that businesses enhance gainfulness as an indirect result of improving management efficiency, not from market power benefits This theory also posits that managerial productivity not only increases profits but also leads to greater market share and improved market concentration (Athanasoglou et al., 2008) ES theory holds that efficiency creates market structure This hypothesis is often proposed in two different approaches and depends on the type of effectiveness they consider (Berger, 1995) For the X-efficiency approach, businesses that operate more efficiently are more profitable, because they can minimize costs and maximize profits through mergers and acquisitions (M&A) Because of the ability to diminish production costs at any given output, corporations can reduce product and service prices after implementing M&A to attract more customers, which achieves a larger market share (Muharram & Matthews, 2009) For the Scale-Efficiency approach, the relationship described above is explained based on scale Also based on Olwenly & Shipho (2011), the scale efficiency approach asserts that larger businesses have lower production and costs than small businesses, and thus have higher profits rely on economies of enterprise scale

In summary, it can be seen that the market power theory supposes that the profitability of a firm is determined by market factors, while the efficient structure hypothesis believes that lucrativeness is influenced by factors within the business, such as size, internal efficiency, and management decisions From the above two theories, many researchers have introduced several variables into the model to measure enterprise profitability influenced by internal and external factors.

LITERATURE REVIEW

2.3.1 Research Factors Affecting Business Profitability

A study on Impact of Leverage on Profitability of Selected Steel Companies of India (Mochi, 2023)

The study explores the impact of financial leverage, measured by the Debt to

Equity (D/E) ratio, on the profitability of selected Indian steel companies—Tata Steel Ltd, JSW Steel Ltd, and SAIL—over five years (2018-2022) Using Return on Assets (ROA) and Return on Equity (ROE) as profitability metrics, the research finds that a high D/E ratio increases financial risk, leading to lower ROA, but may also maintain a high ROE depending on the company's debt management strategy The study concludes that balancing debt and equity is crucial for profitability

Working Capital Management on the Firm’s Profitability of Steel Sector Firms in India (Garg, 2023)

The main aim of this study is to examines the effect of Working Capital Management (WCM) on the profitability of Indian steel companies listed in the BSE Dollex 200 from 2011 to 2020 Utilizing panel data analysis, including pooled OLS, FEM, and REM, the research finds that Inventory Conversion Period, Cash Conversion Cycle, and Accounts Payable Period significantly negatively impact profitability The study highlights the importance of effective WCM, particularly in the capital-intensive steel sector, for maintaining profitability amidst challenges like fluctuating raw material prices and high fixed costs

The Impact of Financial Leverage, growth, and Size on the Profitability of Jordanian Industrial Listed Companies (Alghusin, 2015)

The author uses the ROA index to measure operating efficiency under the impact of financial leverage, growth, and scale Research data were taken between

1995 and 2005, with the study sample collected from 25 industrial enterprises listed on the Amman Stock Exchange (ASE) Using panel data regression analysis techniques, research results illustrate that financial leverage is negatively correlated with lucrativeness while revenue growth shows the opposite In addition, a corporation’s size has an effective impact on ROA but is not statistically significant

ROA and ROE Forecasting in Iron and Steel Industry Using Machine Learning Techniques for Sustainable Profitability (Kayakus., 2023)

The researchers utilized artificial neural networks (ANNs), support vector regression (SVR), and multiple linear regression (MLR) to estimate the return on assets (ROA) and return on equity (ROE) for 13 companies in the iron and steel sector With models incorporating five independent variables, the study found that ANN and SVR methods achieved higher prediction accuracy for both ROA (86.4% and 79.9%) and ROE (85.8% and 80.9%) compared to MLR These findings highlight the effectiveness of machine learning techniques in providing reliable forecasts, supporting strategic decision-making for sustainable profitability in the steel industry

Determinants of Firm Profitability in Nigeria: Evidence from Dynamic Panel Models (Odusanya et al., 2018)

By applying the GMM system to determine the determinants of profitability of 114 companies listed on the Nigerian Stock Exchange (NSE) during the period

1998 - 2012, the study shows that Short-term leverage and inflation rates have a significant negative influence on a business's lucrativeness

The determinants of profitability in companies listed on the Bucharest stock exchange (Vătavu, 2014)

The author took data from 126 Romanian enterprises listed on the Bucharest Stock Exchange over 10 years (from 2003 to 2012) to conduct research with the dependent variable being ROA Research results have shown that scale has a beneficial consequence on profitability, while debt ratios have the opposite impact Additionally, during periods of unstable economic conditions, high inflation has a strong detrimental effect on business operations

Factors affecting the profitability of textile and garment enterprises listed on Vietnam’s stock market (Hien & Ha, 2021)

The author uses descriptive statistics methods, Pooled OLS, REM, and FEM to estimate the model with two additional variables: ROA and ROE of 20 textile and garment enterprises listed on the stock market in the period 2009 – 2018 Research consequences demonstrate that total asset turnover, liquidity, revenue growth, operating time, and business size have a positive impact on profitability, whereas financial leverage and market concentration have opposite effects In addition, GDP and inflation rate are impacts of macro factors that are not statistically significant on business profitability

Factors affecting the profitability of Vietnamese real estate businesses: Applying random and fixed effects models (Hang & Linh, 2020)

Through the application of the fixed effect model and random effect model, the authors researched based on data from 27 real estate businesses listed on HOSE during the period 2010 – 2019 The results indicate that asset structure reduces a business's ability, while financial leverage, size, and some other factors have a favorable impact Besides, the influence of macro factors such as GDP and the inflation rate is not statistically significant

Factors affecting the profitability of construction industry enterprises listed on the Vietnamese stock market (Ha et al., 2021)

The author uses FEM and REM regression models to identify factors affecting the profitability of 94 construction enterprises listed on HOSE and HNX in the period

2010 - 2019 The results show the ability Profitability of construction enterprises has a positive relationship with receivables turnover and firm size, and also has an adverse contact with financial leverage

Factors affecting the profitability of listed steel industry enterprises listed on the Vietnamese stock market (Giang, N T., 2022)

The study used panel regression methods such as Pooled OLS, REM, and FEM, then conducted FGLS regression after performing model tests and defects to show that company size has an adverse effect Direction to profitability (ROA and ROE) On the contrary, asset growth rate, and corporate income tax payment rate have a positive impact on the profitability of steel industry enterprises listed on HOSE

2.3.2 Research the impact of the COVID-19 pandemic on business profitability 2.3.2.1 Worldwide Researches

Impact of COVID-19 Pandemic on Liquidity and Profitability of Firms in Nigeria (Omaliko et al., 2021)

The author uses several key variables, namely Liquidity Ratio (LR), ROE, Pre- COVID-19 Pandemic Period (2017-2018), and During COVID-19 Pandemic Period (2019-2020) The studied design used Ex Post Facto design and data for the study were obtained from the National Stock Exchange of India (NSE) Factbook Research results show that the COVID-19 pandemic has a serious impact on the Liquidity and Profitability of companies in Nigeria at the 5% significance level

The Impact of the COVID-19 Pandemic on the Profitability of Companies Incorporated in IDX30 (Studies Before and During the Pandemic) (Darma et al.,2022)

This research compares the differences in profit gained by businesses listed in IDX30 before and during the COVID-19 Pandemic The profit level is measured by

3 variables: return on Asset (ROA), Net Profit Margin (NPM), and Return on Equity (ROE), and studied during the two–year research period From the research, 18 enterprises were obtained from the Indonesia Stock Exchange website, which met the sample criteria providing 108 data Through using Wilcoxon sign rank non- parametric tests, research results show a significant difference in ROA before and during the Pandemic

Impact of COVID-19 Pandemic on micro, small, and medium-sized Enterprises operating in Pakistan (Shafi et al., 2020)

The data were collected from 184 micro, small, and medium-sized enterprises in Pakistan by administering an online questionnaire, which was analyzed through descriptive statistics The research revealed that a majority of these businesses were significantly affected by the pandemic They encountered financial difficulties, supply chain disruptions, and a decline in revenue and profits Notably, over two- thirds of the surveyed businesses indicated they wouldn't survive beyond two months if lockdown restrictions persisted

The Impact of Corporate Governance on Firm Performance During The COVID-19 Pandemic: Evidence from Malaysia (Khatib & Nour, 2021)

Khatib and Nour researched to assess the impact of the COVID-19 pandemic on business performance and governance in Malaysian businesses Research results show that the profitability rates of businesses during the pandemic period (2020) decreased compared to the pre-epidemic period (2019)

The Impact of the COVID-19 Pandemic on the Financial Performance of Firms on the Indonesia Stock Exchange (Devi et al., 2020)

Using a research sample of 214 companies in 9 industries or 49 sub-sectors, results collected from the Wilcoxon test show that ROA decreased significantly during the pandemic At the same time, the study also shows that the consumer goods industry has increased liquidity and short-term profitability but decreased leverage

In contrast, industries with reduced profit margins and liquidity during the epidemic period include real estate, construction, finance, investment, trade, and services

The COVID-19 pandemic also has a great impact on production and business activities and the lucrativeness of corporations in Vietnam, disrupting supply chains, and causing financial and human resource difficulties

The Effect of COVID-19 Pandemic on Financial Performance of Firms: Empirical Evidence from Vietnamese Logistics Enterprises (Hong, N T X., 2022)

The Wilcoxon test was used on 114 businesses, with results showing that the ROA of these businesses decreased during the outbreak period compared to the pre- epidemic period

Analyzing the performance of textile and garment enterprises listed on the Vietnamese stock market under the impact of the COVID-19 pandemic (Lan, N T T., 2023)

Similarly, this study uses a data sample of 15 enterprises, with a panel data regression method, the research results show that COVID-19 has a drawback impact on textile and garment enterprises, causing the ROA of these businesses all decrease

Analyzing the impact of the COVID-19 pandemic on stock prices and business activities of companies in the oil and gas industry in Vietnam (Thu, N T T., 2022)

From 21 oil and gas companies listed on the Vietnamese stock exchange, the author used a regression method with the double difference model DID (Difference – in – Difference Method) to obtain the results that the COVID-19 pandemic harms both stock prices and stock trading volume Besides, the profits of businesses are the result of the psychological behavior of investors and the ineffective performance of businesses However, when social distancing was implemented, stock prices decreased but the trading volume of stocks in this industry group increased during this period

The impact of the COVID-19 pandemic on the business performance of trading and service companies listed on the Vietnamese stock market (Truong et al., 2023)

METHODOLOGY RESEARCH

RESEARCH DATA

The research article gathers data from the combined financial statements and audited annual reports of 18 steel industry enterprises listed on the Vietnam stock market spanning the years 2012 to 2023, equivalent to 216 observations

Financial data are compiled and collected from reference sources Vietstock and CafeF Inflation rate and GDP data are obtained from the General Statistics Office of Vietnam (GSO)

Criteria for selecting 18 steel companies include:

(1) The fiscal year is calculated from January 1 to December 31;

(2) Have complete and continuous financial reports from 2012 to 2023;

(3) The financial statements are audited and have an opinion of reasonable and honest acceptance according to the principle of materiality.

RESEARCH MODEL

Based on previous empirical studies on factors affecting the profitability of businesses such as Chander and Aggarwal (2008); Malik (2011); Vătavu (2014); Alghusin (2015); and Hien (2021) Besides, research articles on the impact of the COVID-19 pandemic on companies' profitability such as Darma et al., (2022), Han and Mai (2021), Nguyen (2022), and Truong et al (2023) Inheriting the above empirical studies, the research is conducted according to the descriptive statistical research approach and also utilizes s a panel data regression model with the following model:

𝜷 𝟏 − 𝜷 𝟕 : Regression coefficients of independent variables

𝑹 𝒊𝒕 : Enterprise profitability is measured by ROA

COVID: value equal to 1 for three years 2020, 2021, and 2022; the value is 0 for the remaining years from 2012 to 2023

LEV: Financial leverage of the enterprise

Table 3.2: Summary table of variables included in the research model

Symbol Name Measurement variable Expected sign sources

Net Income / Average Total Assets

Malik (2011), Agiomirgianakis et al (2006), Vătavu, S (2014)

(revenue of year n – Revenue of year n – 1)/

SIZE Firms size = Ln (Total Assets) +

LIQ Liquidity = Current assets/ Short – term liabilities – Agiomirgianakis et al (2006),

Ratio = Total Debt/ Total Asset – Malik (2011);

Inflation rates are collected from the GSO –

GDP is collected from the

(Source: gathered by the author from previous studies)

VARIABLES DETERMINATION AND RESEARCH HYPOTHESIS 24 1 Firm’s Profitability

ROA is an important metric to measure and compare the performance of businesses, used in many research articles Besides, the ROA ratio is not affected by high equity (Rivard and Thomas, 1977) Therefore, the author will use the ROA ratio to represent the profitability of steel industry enterprises:

ROA is an important metric to measure and compare business performance, used in many research articles In addition, the ROA ratio is not affected by high equity, suitable for research when comparing and evaluating businesses in the same field and industry (Rivard and Thomas, 1977) Therefore, based on previous studies by Malik (2011), Agiomirgianakis et al (2006), Vătavu, S (2014), and Lan, N T T

(2023), the author will use the ROA ratio to show the gainfulness of steel industry enterprises:

The study by Agiomirgianakis et al (2006), Vătavu, S (2014), Hien, P T &

Ha, N N (2021) concluded that business size has a positive impact on business profitability According to Alarussi (2018), large-scale companies will have advantages in capital, factories, warehouses, and more effective production and business opportunities in today's concentrated market The larger the enterprise scale, the stronger the financial potential and the lower the risk of bankruptcy (Ha et al.,

2021) In general, a company with a large scale of assets and a long period of operation will create a reputation and brand so that it can easily access capital to invest in new projects, develop technology, increase growth opportunities, and enhance the company's competitiveness However, as a business expands in scale, management becomes more difficult and more costly Based on the research of Ahmad (2012), Vătavu, S (2014), and Hien & Ha (2021), the author uses the logarithm of total assets as a measure of enterprise size:

Hypothesis H 1 : Firm size has a positive effect on the profitability of businesses

According to research by Yazdanfar (2013), Alghusin (2015), Giang, N T

(2020), Hang, N T & Linh, N T (2020), revenue growth has a positive impact on business profitability Businesses with high revenue growth rates show that these companies are in good health and highly profitable (Yazdanfa, 2013) If this rate decreases compared to previous cycles, the sales team needs a different approach to driving revenue (Tram, 2023) Based on Agiomirgianakis (2006), Yazdanfar (2013), Alghusin (2015), Revenue growth is determined by:

Hypothesis H 2 : Revenue growth has a beneficial impact on the enterprise’s lucrativeness

The current liquidity ratio is the ratio between short-term assets and short-term liabilities (Mamduh and Halim, 2014), used as a measure reflecting the solvency or more precisely the financial potential of an enterprise This ratio shows how many dongs of short-term assets a company can use for each dollar of short-term debt payable However, conclusions about the relationship between this variable and performance are not unanimous Studies by Chander & Aggarwal (2008), Safdar et al., (2016), and Le & Phan (2017) concluded that there is a positive relationship between liquidity and profitability, while other studies Research by Agiomirgianakis et al (2006), Githaiga & Kabiru (2015) showed that the current liquidity ratio has a statistically negative impact on the profitability of the business “If a company has a low liquidity index, it demonstrates that the firm has financial problems and faces many risks leading to insolvency in the future” (Tram, 2020)

According to Lan, N T T (2023), a business's liquidity ratio at an appropriate level will boost its profits However, when the liquidity ratio is excessively high, it indicates that the business is holding an excess amount of assets, which can result in higher maintenance costs and increased opportunity costs Therefore, liquidity is expected to have a negative influence on the profitability of steel industry enterprises Based on Agiomirgianakis et al (2006), Lan, N T T (2023), the liquidity formula is calculated by:

Hypothesis H 3 : Liquidity has an adverse influence on the enterprise’s profitableness

Some research demonstrates the relationship between financial leverage and business profitability in two different directions Specifically, empirical studies conducted by researchers such as Margaritis & Psillaki (2010) or Abdullah & Tursoy

(2019) in developed countries like the US, UK, and Germany indicate that higher leverage ratios can enhance operational efficiency and subsequently increase business profitability Conversely, studies in emerging markets, particularly those in Vietnam, suggest a negative correlation between financial leverage and corporate profitability Research by Malik (2011), Vătavu (2014), Alghusin (2015), and Odusanya et al

(2018) have similarly observed that debt leverage negatively affects business profitability

Lan, N T T (2023) points out that the higher the financial leverage ratio, the worse the business will perform because the business will be under greater pressure to pay interest and loan principal Firms that rely heavily on debt are inherently exposed to greater risks, whereas those preferring equity capital tend to adopt a more cautious approach, leveraging internal reserves Therefore, relying on Malik (2011); and Vătavu, S (2014), Financial leverage measures the level of loan use compared to the company's total capital resources with the formula determined by:

Hypothesis H 4 : Financial leverage has a detrimental consequence on the corporation’s profitability

Research by Vătavu (2014), and Odusanya et al (2018) pointed out that the profitability of businesses is adversely affected by inflation Nevertheless, Hien, P

T & Ha, N N (2021), Hang, N T & Linh, N T (2020) show that inflation has no relationship with profitability However, the author maintains the expectation that inflation will adversely affect the profitability of steel industry enterprises This means that an increase in inflation leads to fluctuations in the prices of input materials and finished products, affecting the stability of the production process Besides, consumers must be more careful in spending if inflation is high This leads to a reduction in demand for key steel-consuming sectors such as real estate, construction, and infrastructure At the same time, rising interest rates due to the central bank's tightening monetary policies also affected investment projects, contributing to reducing steel demand Therefore, inflation is expected to have a detrimental effect on the profitability of steel industry enterprises

Hypothesis H 5 : Inflation has a drawback effect on the establishment’s gainfulness

The COVID-19 pandemic has had a strong influence on the profitability of many businesses around the world Based on previous studies by Devi et al (2020), Omaliko et al (2021), Hong, N T X (2022), and Lan, N T T (2023), the author discovered that the performance of businesses is detrimentally impacted by the COVID-19 pandemic, causing the investment scale or sales revenue of businesses to decrease

In this thesis, the Coronavirus disease pandemic is represented by a binary dummy variable, taking the value of 1 in the three years of the pandemic, 2020, 2021, and 2022, and taking the value of 0 in the remaining years during the period 2012 –

2023, similar to the determination of the recent study by Vo et al (2022); and Lan,

N T T (2023) The World Health Organization (2020) declared COVID-19 a serious global pandemic that persisted until 2022 By early 2023, WHO officially stated that COVID-19 was no longer a global health emergency Consequently, the author identified the years 2020, 2021, and 2022 as being significantly impacted by SARS-CoV-2 Therefore, the COVID-19 dummy variable is calculated as follows:

COVID = { 0 in the three years 2020, 2021, 2022

1 in the remaining years during the period (2012 − 2023)

Hypothesis H 6 : The COVID-19 Pandemic has an unfavorable impact on the business’s profitability

GDP is an index intended to indicate changes in domestic economic activity GDP reflects general macroeconomic conditions, so adding this variable to the model allows controlling for the business cycle During periods of good economic growth, GDP assists businesses attract more investors At the same time, business output also increases because demand for business goods and services is likely to increase as people's income increases, increasing spending and strengthening the market It is expected that sales revenue will increase, thereby creating higher profits for the business and improving business performance (Lee, 2014) In contrast, unfavorable economic conditions reflect an economic slowdown, as was the case with the recession caused by the COVID-19 pandemic, which reduced business performance (Lan, 2023) Pervan et al (2019); Hang, N T & Linh, N T T (2020); and Lan, N

T T (2023) concluded that GDP and enterprise lucrative have a positive relationship Therefore, the hypothesis is:

Hypothesis H 7 : GDP has an advantageous influence on the enterprise’s profitability.

METHODOLOGY

The research uses panel data regression analysis techniques After collecting and processing all relevant data, the author performs empirical analysis using a regression model with 1 dependent variable and 6 independent variables Pooled OLS, FEM, and REM were conducted to select the model that best fits the research data F-Test is performed to choose between the Pooled OLS model and the FEM model The Hausman test will be used to select between the FEM or REM model Besides, determining between the Pooled OLS model and the REM model will use the Breusch-Pagan Lagrangian test This research method is similar to Vătavu, S (2014); Odusanya et al (2018); and Lan, N T T (2023)

After choosing the appropriate model, the author will test for autocorrelation and heteroskedasticity If these phenomena appear, the Feasible Generalized Least Squares (FGLS) method is applied to correct model defects and draw conclusions, ensuring the best results (Judge, Hill et al, 1988) The research process is carried out through 6 main steps:

Step 1 The author will review the underlying theory related to the impact of the COVID-19 epidemic as well as the impact of factors on the profitability of businesses, and at the same time conduct a review of the Previous relevant research in the world and Vietnam After that, the author will present the research model, research variables, and data collection

Step 2 Through STATA 15.1 software, the study will conduct descriptive statistics to synthesize the data characteristics of the variables in the model including maximum value, minimum value, mean, and standard deviation Based on statistical criteria, the author can comprehend the phenomena and make the right decisions about the research data series

Step 3 Analyze the correlation of the independent variables of the model

Besides, the author uses the Variance Inflation Factor (VIF) to check the level of multicollinearity

Step 4 Perform regression using three methods: Pooled OLS, FEM, and REM

Then, the author will select the appropriate model through the F-Test, Breusch-Pagan Lagrangian, and Hausman tests

Step 5 Check and fix defects in the model such as autocorrelation, and heteroscedasticity through method FGLS

Step 6 Discuss the results and present the main conclusions, evaluate the level of impact of each factor on the profitability of steel industry enterprises, and provide suggestions on related policy implications

In Chapter 3, the author presented the research method, including the research process of the thesis In addition, chapter 3 also proposes a research model and sets out research hypotheses about factors affecting the profitability of steel industry enterprises listed on the Vietnam stock market, which based on the theory has been presented in Chapter 2.

RESULTS

DESCRIPTIVE STATISTICS

Using STATA 15.1 software, the author has compiled and described research data in terms of the number of observations, mean value, standard deviation, maximum and minimum value of the dependent variable, and independent variables

Variable Observations Mean Standard deviation Min Max

(Source: Research data results collected by the author from STATA)

Based on the results from Table 4.1, there are a total of 216 observations from

18 steel industry companies listed on the Vietnam stock market from 2012 to 2023, including:

Dependent variable: The ratio of return on assets (ROA), has 216 observations with a positive mean value of 0.0309922 and standard deviation of

0.0696642 Besides, the largest value of ROA is 0.2170731, belonging to the ROA of Thanh Thai Group Joint Stock Company (KKC) in 2016 and the smallest value is -0.4685243, belonging to the ROA of Thanh Thai Group Joint Stock Company

The average value of company size (SIZE) is 14.19172 and the standard deviation is 1.933336 In addition, Binh Tay Steel Wire Netting Joint Stock Company

(VDT) has the lowest value at 10.32866 in 2022 while the highest value of Hoa Phat

Group JSC (HPG) in 2023 is 19.0508

The average value of growth revenue (GROWTH) is 0.0743146 and the standard deviation is 0.3397115 Thong Nhat Flat Steel Joint Stock Company (TNS) had the lowest value at -0.647802 in 2012 and the highest value equal 2.850185 in

The average value of financial leverage (LEV) is 0.5250509 and the standard deviation is 0.1569157 It can be seen that the highest value is 0.8398896 for Thong Nhat Flat Steel Joint Stock Company (TNS) in 2020, whereas the lowest value is 0.0726862 in 2019

The average value of Liquidity (LIQ) is 1.433712 and the standard deviation is 1.112815 Thong Nhat Flat Steel Joint Stock Company (TNS) has the lowest value at 0.1765373 in 2013 while the highest value of VNSTEEL – Thu Duc Steel JSC (TDS) in 2022 is 13.16416

The average value of Inflation (INF) is 0.0371 and the standard deviation is

0.0213598 VNSTEEL – Nha Be Steel Joint Stock Company (TNB) had the lowest value at 0.0063 in 2015 while the highest value in 2012 was 0.0921

The average value of the Gross Domestic Product (GDP) is 0.0575083 and the standard deviation is 0.0157822 Besides, the lowest value of Steel Structure Manufacture Joint Stock Company (SSM) is 0.0258 in the year 2021 and the highest value of Thong Nhat Flat Steel Joint Stock Company (TNS) in 2022 is 0.0802

The average value of the COVID-19 Pandemic (COVID) is 0.25 and the standard deviation is 0.4340185 Besides, the lowest value is 0 while the highest value is 1 The dummy variable represents the impact of the COVID-19 pandemic on the dependent variable ROA with 162 observations indicating that a pandemic has not yet occurred (= 0), accounting for 75% and the number of observations affected by the pandemic (= 1) has 54 observations, accounting for less than 25%

(Source: Research data results collected by the author from STATA)

CORRELATION ANALYSIS

ROASIZEGROWTHLEVLIQINFCOVIDGDP ROA1.0000 SIZE0.10371.0000 GROWTH0.2415***0.1222*1.0000 LEV-0.2773*** 0.11100.1607**1.0000 LIQ0.1430**-0.3547*** -0.1415**-0.5629*** 1.0000 INF-0.1262*-0.0449-0.06940.0137-0.08711.0000 COVID-0.01190.0470-0.0477-0.02750.1469**-0.2628*** 1.0000 GDP-0.1938*** -0.01920.0035-0.02170.0399-0.0548-0.4574*** 1.0000 Note: *,**,***: correspond to a significance level of 10%, 5%, and 1% (Source: Research data results collected by the author from STATA)

Table 4.3 Correlation matrix between variables in the model

From the results of Table 4.3., the correlation between variables is at an acceptable level when the absolute value of the correlation coefficient between variables is less than 0.8 In particular, the absolute value coefficient of the largest correlation relationship in Table 4.3 is between the independent variable LIQ and the independent variable LEV at 0.5629.

MULTI-COLLINEARITY TESTING

This thesis will check whether there is multicollinearity between independent variables or not by testing multicollinearity in the research model based on the variance inflation factor (VIF) through the VIF command in STATA 15.1 software

If the study results in a VIF index < 10, then the research model does not have multicollinearity and vice versa (Hair et al., 1995)

Table 4.4 Result of multicollinearity testing

(Source: Research data results collected by the author from STATA)

The results of VIF returned from STATA 15.1 software are in Table 4.4 indication that the VIF index of the independent variables is below 10 The largest VIF is 1.77 (LIQ) and the smallest is 1.05 (GROWTH), the mean value is 1.36, and all are less than 10 Based on the above reason, the thesis believes that there is no multicollinearity phenomenon within the model.

PANEL DATA REGRESSION RESULTS

In this thesis, the author conducts regression using STATA 15.1 software with three main estimation methods Pooled OLS, FEM, and REM to identify and evaluate the influence of seven independent variables: SIZE, GROWTH, LEV, LIQ, INF, GDP, and COVID to the dependent variable ROA based on the estimated coefficients and statistical significance level of each coefficient The regression results are presented in Table 4.5.:

Table 4.5 Regression results based on Pooled OLS, FEM, and REM

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

(Source: Research data results collected by the author from STATA)

The results of the regression model according to the three estimation methods Pooled OLS, FEM, and REM are summarized in the table above The results show that in all three models, there are four factors including GROWTH, LEV, COVID, and GDP that affect the dependent variable with high statistical significance at the 1% significance level However, the LIQ variable is not statistically significant in all

Besides, the SIZE variable in the OLS model has a statistical significance level of 5% However, in the FEM and REM models, the SIZE variable has no impact on the dependent variable ROA

In addition, the INF variable in the OLS and FEM models affects the dependent variable ROA with statistical significance at the 5% level However, in the REM model, the INF variable affects the dependent variable with high statistical significance, at the 1% level

Although some regression results are similar, to ensure stability, unbiasedness, and effectiveness, the author must select the model and test the model after selection.

SELECTING SUITABLE REGRESSION MODEL

4.5.1 Verification between OLS Pooled Model and FEM models

To determine the appropriate model among the two Pooled OLS and FEM models, the author relies on the F-test, with the hypothesis:

H0: The Pooled OLS model is more appropriate than the FEM model

H1: The FEM model is more appropriate than the Pooled OLS model

Table 4.6 Results of F – test between models Pooled OLS and FEM

(Source: Research data results collected by the author from STATA)

From the test results, we have Prob > F = 0.0027 < 0.05, describing the rejection of hypothesis H0 Therefore, the FEM model is more suitable for estimating research variables than the Pooled OLS model

4.5.2 Verification between REM and FEM

To determine the appropriate model among the two FEM and REM, the author relies on the Hausman test, with the hypothesis:

H0: REM is more appropriate than the FEM

H1: FEM is more appropriate than the REM

Table 4.7 Results of Hausman test between models FEM and REM

(Source: Research data results collected by the author from STATA)

From the test results, we have Prob > chi2 = 0.1796 > 0.05, indicating acceptance of hypothesis H0 Therefore, the REM is more acceptable for estimating research variables than the FEM model

Conclusion: From the results of the two tests above, it is clear that REM is the most appropriate model for estimation Therefore, the author will continue to conduct tests for possible defects in the model based on the regression results of the REM model.

TESTING DEFECTS IN THE REM MODEL

In this essay, the author tests whether the model has autocorrelation or not by performing the Wooldridge test and the research hypothesis is:

H0: The research model does not have an autocorrelation phenomenon

H1: The research model has an autocorrelation phenomenon

If the test results are obtained with Prob > F > 5%, the research model accepts hypothesis H0, meaning the research model does not have an autocorrelation phenomenon

Table 4.8 Results of Autocorrelation test

(Source: Research data results collected by the author from STATA)

The Wooldridge test results describe that Prob > F = 0.1855 > 0.05 shows acceptance of hypothesis H0 Therefore, it is concluded that autocorrelation does not occur in the model

The author continues to test the presence of heteroscedasticity in the model through the Breusch and Pangan Lagrangian multiplier test and the research hypothesis is:

H0: The research model does not have heteroscedasticity

H1: The research model has heteroscedasticity

If the test results are obtained with Prob > F > 5%, the research model accepts hypothesis H0, meaning that the research model does not have heteroskedasticity

Table 4.9 Results of Heteroscedasticity test

Breusch and Pangan LM Prob > F = 0.0497 < 5%

(Source: Research data results collected by the author from STATA)

The results of the Breusch and Pangan LM test show that Prob < F = 0.0497 < 0.05 represents a rejection of hypothesis H0 Therefore, it is concluded that heteroskedasticity occurs in the model

From the above two tests including F-test: Choosing between OLS and FEM, and the Hausman test: Choosing between FEM and REM, the author concludes that the REM model is the most appreciated Then, the essay tests the phenomenon of autocorrelation and heteroscedasticity that may appear in the model through the Wooldridge test and the Breusch and Pangan LM test and shows that the model does not occur autocorrelation but heteroskedasticity occurs From this result, the author uses the FGLS method to overcome the above defect.

FIXING REGRESSION MODEL DEFECT

To overcome the phenomenon of heteroscedasticity in the selected model, the author continues to apply the feasible generalized least squares (FGLS) regression model method The results of FGLS are as follows:

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

(Source: Research data results collected by the author from STATA)

With 216 observations over 12 years, using the FGLS method, the author evaluates the model as statistically significant because the coefficient Prob < chi2 0.0000 < 0.05 Therefore, the author believes that this is an appropriate estimation result for the essay

From the FGLS result, the regression model is described as:

Table 4.10 describes that the model is statistically significant with P-value 0.0000 with the variables SIZE and LIQ not statistically significant in the model In addition, only the GROWTH variable has a positive impact on the dependent variable ROA, while variables such as INF and GDP have adverse effects on ROA with the same 1% statistical significance level Furthermore, the COVID variable also has a detrimental influence on ROA with a significance level of 10%.

RESEARCH DISCUSSION

Table 4.11 Summary of research results

(Source: Research data results collected by the author from STATA)

The results of the research model show that the SIZE variable has the same sign as the dependent variable ROA However, the results are not statistically significant in the research model although consistent with hypothesis H1 when the hypothesis expects business size to have a positive impact on the rate of return on total assets Thus, in the thesis, the author does not have enough basis to conclude the impact of the independent variable SIZE on the dependent variable ROA

Conclusion: There is no basis to conclude that business size has an impact on the profitability of steel industry enterprises listed on the Vietnam stock market

The results of the research model show that the GROWTH variable is positively correlated with the profitability of businesses with statistical significance at the 1% level This result supports hypothesis H2 and is similar to the research results of Agiamirgianakis (2006); Chander & Aggrawal (2008); Alghusin (2015); and Hien (2021) This shows that the higher the revenue growth, the greater the profitability The growth rate of revenue will partly reflect the capacity of the business's sales activities The more this speed increases, the more goods it sells Therefore, the business results of the business will also be reported as a positive sign

Conclusion: The revenue growth rate has a beneficial effect on the lucrativeness of steel industry enterprises listed on the Vietnam stock market

The results of the research model show that the LIQ variable has the same negative direction as the dependent variable ROA However, the result is not statistically significant in the research model although it is consistent with hypothesis

H3 when the hypothesis that corporate liquidity expectations have a detrimental impact on the rate of return on total assets Thus, in the thesis, the author does not have enough basis to conclude the impact of the independent variable LIQ on the dependent variable ROA

Conclusion: There is no basis to conclude that Liquidity affects the profitability of steel industry enterprises listed on the Vietnamese stock market

Research results show that financial leverage and business performance have a negative correlation, with a statistical significance level of 1% This means that when financial leverage increases by 1 unit, profitability decreases by 0.1305 times This result supports hypothesis H4 and is similar to the results of Malik (2011); Vătavu, S (2014); Alghusin (2015); Alarussi et al (2018), Hien & Ha (2021); Lan,

N T T (2023) It is clear that if a business has a high debt-to-total asset ratio, its operating efficiency will be poorer, leading to low profitability This is because the higher the debt ratio, the greater the pressure to pay interest and principal, making it more difficult for businesses to operate

Conclusion: Financial leverage has an adverse consequence on the gainfulness of steel industry enterprises listed on the Vietnamese stock market

Model results show that the macro variable INF has an unfavorable impact on business profitability at the 1% significance level Accepts hypothesis H5 and is consistent with the studies of Vătavu, S (2014); and Odusanya et al (2018) This is completely consistent with reality when prices of input materials and costs increase in the context of rising inflation This can increase the total cost of developing and operating real estate projects, leading to a decrease in the rate of return on total assets of businesses

Conclusion: The inflation rate has a negative impact on the profitability of steel industry enterprises listed on the Vietnamese stock market

The results show that there is a negative correlation between the independent variable COVID and ROA at the 10% significance level This result supports hypothesis H6 and is consistent with the studies of Hien & Ha (2021); and Lan, N T

T (2023) Most of the steel industry's raw materials are imported from the Chinese market When the epidemic broke out, the supply chain of input materials was interrupted, making it difficult for businesses to meet orders

Conclusion: The COVID-19 pandemic has a drawback effect on the profitableness of steel industry corporations listed on the Vietnamese stock market

Based on the research results, GDP has an adverse correlation with the lucrativeness of steel industry enterprises with a statistical significance level of 1% However, the results are not consistent with hypothesis H7 and previous studies such as Lee (2014); Hang & Linh (2020); and Lan, N T T (2023) Thus, in the thesis, the author does not have enough basis to conclude the impact of the independent variable GDP on the dependent variable ROA

Conclusion: There is no basis to conclude that GDP affects the profitability of steel industry enterprises listed on the Vietnamese stock market

Chapter 4 performs descriptive statistics on a dependent variable ROA and seven independent variables according to evaluation criteria such as standard deviation, mean value, maximum value, minimum value, number of observations, and characteristics of each variable From the research results, the model does not have a multicollinearity phenomenon However, after choosing the REM model as the appropriate model for the study, the author realized that there was heteroskedasticity Therefore, FGLS is applied to overcome that defect

The study found factors that negatively impact ROA profitability, including Financial leverage (LEV), Gross Domestic Product (GDP), and inflation rate (INF) with statistical significance at 1%, while the only positive factor is Revenue Growth (GROWTH) with the same level of significance In addition, the COVID-19 pandemic also negatively impacted the dependent variable ROA with a statistical significance level of 10% Besides, Enterprise Size (SIZE) and Liquidity Ratio (LIQ) are not statistically significant in the research model.

CONCLUSION AND RECOMMENDATION

CONCLUSION

Based on the theoretical basis and empirical evidence in Chapter 2, build a model and research method in Chapter 3 to conduct research in Chapter 4 on the impact of the COVID-19 pandemic on the profitability of steel industry enterprises listed on the Vietnam stock market After the process of comparing and selecting the model, the author found that REM is a suitable model for the model proposed in Chapter 3 However, the model has heteroskedasticity, so that FGLS is used to fix it Thus, finding the most robust and effective estimate

The detailed results presented in Chapter 4 reveal that the profitability of 18 steel industry enterprises is influenced by both internal and external factors, particularly the impact of the COVID-19 pandemic These factors will affect the performance of the businesses in various ways, depending on the specific period and the nature of economic or non-economic events that occur

Furthermore, the study also answers the initial research question, specifically:

In the first question, it is to assess the impact of the COVID-19 pandemic on business profitability From the results of Chapter 4, it can be seen that the COVID-

19 pandemic affected adversely profitability at the 10% significance level

In the second question, the thesis analyzed other factors affecting the return on assets (ROA), the results showed that the variables LEV, GDP, and INF had an unfavorable impact on the dependent variable ROA while The GROWTH variable had a beneficial influence on the profitability of the firm, all at the 1% significance level In addition, the variables SIZE and LIQ are not statistically significant in the article

In the last question, the study will display and propose some solutions to support administrators in developing reasonable policies to increase the operational efficiency of steel industry enterprises through section 5.2.

RECOMMENDATION

To improve the effectiveness of steel industry enterprises from internal factors available within the enterprise to external factors such as inflation, GDP, or unforeseen shocks ( COVID-19), corporations need to:

Firstly, businesses should proactively invest in technology, machinery, and production process enhancements to boost production efficiency and product quality, thereby fostering business growth Additionally, steel companies can expand by offering comprehensive construction solutions, providing customers with both steel products and construction services Furthermore, they can tap into niche markets by developing specialized products such as construction steel, stainless steel, and color- coated steel to cater to the high-end demands of customers

At the same time, take advantage of new opportunities and trends such as greening production, investing in environmentally friendly technologies, and sustainable production processes to meet increasingly strict environmental requirements and increase profits, which attract customers who care about the environment Replacing blast furnaces with electric furnaces (Electric Arc Furnace - EAF) to reduce CO2 emissions and fine dust as well as use solar energy and wind energy to replace fossil fuels Furthermore, leveraging information technology for digital transformation and applying Artificial Intelligence (AI) in smart factories can optimize production and reduce CO2 emissions

The EU Carbon Border Adjustment Mechanism (CBAM) will impose a carbon tax on goods imported into EU markets based on the greenhouse gas emissions produced during manufacturing in the host country Therefore, steel businesses need to be aware of CBAM and understand the opportunities and challenges it presents

To export to Europe, businesses must inventory greenhouse gases as required by the Ministry of Industry and Trade of Vietnam and CBAM Additionally, adopting digital transformation solutions in operations management, such as Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) systems, can improve efficiency and reduce costs

Second, steel enterprises need to be careful in using financial leverage to avoid loss of liquidity and reduce pressure from balancing interests between creditors, and owners and pressures from profits to offset the required rate of return from creditors

In addition, the financial pressure from paying principal and interest is also huge The main reason for the high leverage ratio in the steel industry is high capital costs and large inventory requirements Steel production requires large investments in infrastructure, machinery, and technology, and the industry requires large inventories to ensure stable supply, meet market demand, and protect against fluctuations in raw material prices Consequently, companies frequently rely on loans from external financial sources

Therefore, steel businesses must control and maintain reasonable inventory levels to minimize storage costs, improve cash flow, and avoid risks associated with declining steel prices or operational difficulties, especially in a volatile macroeconomic environment In addition, ensuring transparency in financial reporting is essential to build trust with investors and financial partners Companies should adjust their financial strategies, monitor, and regularly evaluate the leverage ratio to keep it within safe limits They must also manage borrowing-related risks and implement prudent borrowing policies to ensure solvency, maintain normal operations, and mitigate the risks of interest rate and exchange rate fluctuations

Third, companies also should have appropriate strategies to cope with inflation, minimize detrimental impacts, and be ready to adapt to changes in the economic environment and raw material prices, to make full use of them Take advantage of opportunities to maintain growth and profits In addition to closely monitoring price fluctuations of input materials such as iron ore, coal, steel scrap, etc to be able to forecast price trends and come up with appropriate purchasing strategies, the steel organizations should also collaborate closely with raw material suppliers to maintain stable supplies and negotiate competitive prices

Fourth, in response to economic shocks such as the COVID-19 pandemic, steel businesses should enhance their risk management capabilities and diversify their markets The pandemic underscored the importance of having a capable management team and a strategic vision to navigate crises effectively Therefore, companies must invest in training and developing their management teams to enhance their capacity and readiness to respond swiftly to unexpected events Additionally, firms should establish robust risk management systems, utilizing advanced tools and methods to develop comprehensive emergency response plans and risk mitigation measures Moreover, companies should focus on expanding exports to promising new markets such as the US, and Middle East, and develop specialized niche markets

Furthermore, businesses should embrace digital transformation as an essential trend to enhance competitiveness, optimize resource utilization, and boost productivity It's evident that amid social distancing measures and limited physical contact due to the COVID-19 impact, enhancing digitalization and automation is crucial for steel enterprises to sustain operations Specifically, companies should implement automation systems, Internet of Things (IoT) technologies, Mental Forming Technology (MFT), Artificial Intelligence (AI), ERP systems, and other digital tools to streamline production processes Improving digital infrastructure supports remote work capabilities and facilitates online transactions

While the COVID-19 pandemic poses significant challenges for steel enterprises, it also presents opportunities for innovation and competitiveness improvement Companies should actively apply the lessons learned from the pandemic to adapt and foster sustainable development

On the part of the state, it is necessary to have policies and measures to support and remove difficulties for the production and business of enterprises in general, and the steel industry in particular VSA needs to be a pioneer in helping domestic steel producers, and at the same time play a role in stabilizing the steel market to ensure the interests of stakeholders The Government needs to have priority and support policies in the process of "Green manufacturing" of steel enterprises towards the goal of green and sustainable development At the same time, it helps steel manufacturing enterprises to supply green steel products at prices suitable to the market level.

LIMITATIONS AND FUTURE RESEARCH DIRECTIONS

Although the thesis has shown the impact of independent variables on the profitability of enterprises However, due to limited time, experience, and knowledge, the research paper still has some limitations, including:

Firstly, in terms of the time and scope of the study, the thesis was only conducted within the scope of 18 steel companies listed on the Vietnamese stock market in the period from 2012 to 2022, so it may not reflect the entire situation of factors affecting the efficiency of steel enterprises

Secondly, the thesis only focuses on analyzing the impact on the dependent variable of ROA but not on other criteria such as ROE, ROI, NIM, etc

Third, only seven independent variables are studied in the article without taking into account other related variables such as the number of years of operation of the enterprise, and total asset turnover

Fourth, this study has not fully tested the hypotheses of the table-data linear regression model (including endogenous phenomena) to consider the impact of profitability

Based on the remaining limitations, the author proposes a direction for further studies Specifically, increasing the number of variables observed in the article, including macro and micro factors to the profitability of steel enterprises such as the level of market concentration, quality of governance, etc to more comprehensively evaluate independent variables

In addition, new studies need to be carried out on many research models with many different dependent variables in addition to the ROA coefficient In addition, the following research papers need to extend the research time and increase the number of steel companies in Vietnam more fully

In Chapter 5, the author has summarized the factors affecting the profitability of steel enterprises based on the research results in Chapter 4 Based on that, the thesis proposes several recommendations and solutions to support managers in improving the profitability of the business In addition, the thesis also presented the limitations of the research paper and gave directions for further research to be able to improve the research topic in the future

Ban biên tập trang thông tin điện tử Cục Y tế dự phòng (2023), Tình hình dịch COVID-19 trên thế giới và khuyến nghị của Tổ chức Y tế thế giới (WHO) https://vncdc.gov.vn/tinh-hinh-dich-covid-19-tren-the-gioi-va-khuyen-nghi-cua-to- chuc-y-te-the-gioi-who-nd17305.html?fbclid=IwAR3y- wZZVPD7ixo57W3NjEpLVLCAfdeZhcZ0OTEiqKdE8SKFTWC1V- eCmnI_aem_AUBinY0Jb- pmIfl2mLpUkGAFnwhAM75asi5XcbXCS8dYeqMqHTzjK53iLjem8-

Hà Minh Tâm, Vũ Thị Minh Hà, Trần Minh Tâm và Lê Thị Khuyên (2021), Nhân tố tác động đến khả năng sinh lời của doanh nghiệp ngành xây dựng niêm yết trên thị trường chứng khoán Việt Nam https://tapchitaichinh.vn/nhan-to-tac-dong-den-kha-nang-sinh-loi-cua-doanh- nghiep-nganh-xay-dung-niem-yet-tren-thi-truong-chung-khoan-viet-nam.html

Nguyễn Thị Anh Thư (2022) Phân tích tác động của đại dịch COVID-19 đến giá cổ phiếu và hoạt động kinh doanh của các công ty trong ngành dầu khí tại Việt Nam,

Trường Đại học Kinh Tế Tp Hồ Chí Minh

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Appendix 1: List of 18 steel enterprises listed on the Vietnam stock market

1 DTL Dai Thien Loc corporation

2 HMC VNSTEEL - HOCHIMINH City Metal Corporation

3 HPG Hoa Phat Group JSC

5 KKC Thanh Thai Group Joint Stock Company

6 KMT Central Viet Nam Mental Corporation

7 NKG Nam Kim Steel Joint Stock Company

8 SMC SMC Trading Investment Joint Stock Company

9 SSM Steel Structure Manufacture Joint Stock Company

10 TDS VNSTEEL – Thu Duc Steel JSC

11 THIS Thai Nguyen Iron And Steel JSC

12 TLH Tien Len Steel Group Joint Stock Company

13 TNB VNSTEEL – Nha Be Steel Joint Stock Company

14 TNS Thong Nhat Flat Steel Joint Stock Company

15 TVN Viet Nam Steel Corporation

17 VDT Binh Tay Steel Wire Netting Joint Stock Company

18 VGS Vietnam Germany Steel Pipe JSC

Appendix 5: Summarizing regression models of ROA

Appendix 10: Summarizing regression models of ROA

Appendix 11: Data table of factors calculated from financial statements of 18 steel companies listed on the stock market in the period 2012-2023

STOCK YEAR ROA SIZE GROWT

LEV LIQ INF COVID GDP

DTL 2012 0.00689456 14.46385701 -0.13835382 0.51908359 1.07303238 0.09210000 0 0.0525 DTL 2013 0.00824105 14.63917578 0.28098965 0.55750631 1.05399987 0.06600000 0 0.0542 DTL 2014 0.00277522 14.67470466 0.02776225 0.60226693 1.01709013 0.04090000 0 0.0598 DTL 2015 -0.02625821 14.69600166 -0.11829278 0.66417836 0.96807411 0.00630000 0 0.0668 DTL 2016 0.06419208 14.72671478 0.55706750 0.58970955 1.15436592 0.02660000 0 0.0621 DTL 2017 0.08290300 14.70272010 0.09734636 0.50324625 1.33234347 0.03530000 0 0.0681 DTL 2018 -0.00610323 14.85444609 0.09226390 0.58256278 1.19816356 0.03540000 0 0.0708 DTL 2019 -0.05028637 14.84276325 -0.27328044 0.62903270 1.14694319 0.02790000 0 0.0702 DTL 2020 0.00875036 14.70300791 -0.21131912 0.57521625 1.21863623 0.03230000 1 0.0291 DTL 2021 0.02973972 14.45069753 -0.30339406 0.43508365 1.44573813 0.01840000 1 0.0258 DTL 2022 -0.06444708 14.67781384 0.16784924 0.59505237 1.26022759 0.03150000 1 0.0802 DTL 2023 -0.07532550 14.54441284 0.22268713 0.60173224 1.23967327 0.03250000 0 0.0505 HMC 2012 0.02618871 13.85310495 -0.31932717 0.62591808 1.15958067 0.09210000 0 0.0525 HMC 2013 0.02017312 13.84923253 -0.28147156 0.63484086 1.16163987 0.06600000 0 0.0542 HMC 2014 0.02055652 13.90389041 -0.05733636 0.67326710 1.09800008 0.04090000 0 0.0598

HMC 2015 -0.03892076 13.66379541 -0.30047931 0.65544270 1.07414352 0.00630000 0 0.0668 HMC 2016 0.08082584 13.56088924 0.15611665 0.53306459 1.47839375 0.02660000 0 0.0621 HMC 2017 0.07615181 13.87376815 0.17251718 0.60958898 1.41295846 0.03530000 0 0.0681 HMC 2018 0.11063896 13.69608154 0.38214541 0.50720454 1.66192927 0.03540000 0 0.0708 HMC 2019 0.01195521 13.76804467 0.17434024 0.60926154 1.42101277 0.02790000 0 0.0702 HMC 2020 0.04247090 13.70232842 -0.19204056 0.55042957 1.55552594 0.03230000 1 0.0291 HMC 2021 0.11263035 14.04788814 0.05498033 0.59802663 1.50598302 0.01840000 1 0.0258 HMC 2022 0.00273132 13.95079903 -0.10940423 0.64552826 1.38502173 0.03150000 1 0.0802 HMC 2023 0.01652415 14.06228001 -0.08558326 0.66469149 1.37172986 0.03250000 0 0.0505 HPG 2012 0.05419215 16.76077882 -0.05741939 0.38715296 1.38831506 0.09210000 0 0.0525 HPG 2013 0.08712091 16.95432005 0.12524268 0.48285398 1.11307980 0.06600000 0 0.0542 HPG 2014 0.14714110 16.91059501 0.34810158 0.40820035 1.30266669 0.04090000 0 0.0598 HPG 2015 0.13739027 17.05445443 0.07551642 0.39177181 1.19237227 0.00630000 0 0.0668 HPG 2016 0.19882301 17.31885987 0.21237360 0.36070610 1.51712613 0.02660000 0 0.0621 HPG 2017 0.15115856 17.78622097 0.38693630 0.34928253 1.78555910 0.03530000 0 0.0681 HPG 2018 0.10994912 18.17507438 0.20958430 0.28937968 1.11806673 0.03540000 0 0.0708 HPG 2019 0.07446005 18.43828517 0.14008294 0.26513314 1.12795411 0.02790000 0 0.0702 HPG 2020 0.10269954 18.69460436 0.41566229 0.39521443 1.09181378 0.03230000 1 0.0291 HPG 2021 0.19368070 18.99862144 0.66092184 0.41214537 1.28172797 0.01840000 1 0.0258 HPG 2022 0.04957527 18.95328071 -0.05525473 0.36625005 1.29060197 0.03150000 1 0.0802 HPG 2023 0.03621416 19.05079540 -0.15880321 0.38083133 1.15665500 0.03250000 0 0.0505 HSG 2012 0.06915409 15.48753615 0.23536273 0.50593779 0.96769345 0.09210000 0 0.0525 HSG 2013 0.08132541 15.78152735 0.16573655 0.60747187 0.97145783 0.06600000 0 0.0542 HSG 2014 0.04020738 16.13845107 0.27470151 0.67286716 0.93193166 0.04090000 0 0.0598 HSG 2015 0.06915599 16.06053158 0.16387270 0.58840929 0.93055971 0.00630000 0 0.0668 HSG 2016 0.12219267 16.32592136 0.02561164 0.54893539 1.04441199 0.02660000 0 0.0621 HSG 2017 0.06211497 16.88069688 0.46135361 0.62634660 0.95051104 0.03530000 0 0.0681 HSG 2018 0.01925050 16.87209435 0.31711996 0.59457129 0.85457512 0.03540000 0 0.0708 HSG 2019 0.02097897 16.66189780 -0.18601592 0.50898096 0.83897566 0.02790000 0 0.0702 HSG 2020 0.06493509 16.69225702 -0.01797856 0.50637995 1.00342835 0.03230000 1 0.0291 HSG 2021 0.16205159 17.09709936 0.76989446 0.53994405 1.29799884 0.01840000 1 0.0258 HSG 2022 0.01476146 16.65021755 0.02019668 0.35295401 1.63665950 0.03150000 1 0.0802 HSG 2023 0.00173115 16.66998487 -0.36330203 0.37827689 1.71639853 0.03250000 0 0.0505 KKC 2012 0.04085285 12.26586398 0.22225658 0.63582843 1.49896667 0.09210000 0 0.0525 KKC 2013 0.06815980 12.23762945 0.08282995 0.59343449 1.60960716 0.06600000 0 0.0542 KKC 2014 0.04538219 12.48070367 0.22476537 0.67942586 1.40826814 0.04090000 0 0.0598 KKC 2015 -0.17991848 11.74290162 -0.08778998 0.57015502 1.58305694 0.00630000 0 0.0668 KKC 2016 0.21707313 12.09137422 -0.00796998 0.47956283 1.93665735 0.02660000 0 0.0621 KKC 2017 0.13542381 11.66698471 -0.37264097 0.28671479 3.06756959 0.03530000 0 0.0681 KKC 2018 -0.02348309 12.04452383 0.13058941 0.59891870 1.51793161 0.03540000 0 0.0708 KKC 2019 -0.03770383 11.80375009 0.56947651 0.52743490 1.71405891 0.02790000 0 0.0702 KKC 2020 0.09861800 11.75413681 -0.16446112 0.40478005 2.23980978 0.03230000 1 0.0291 KKC 2021 0.02094100 12.52160940 -0.23055949 0.73185898 1.31314813 0.01840000 1 0.0258 KKC 2022 -0.46852427 11.14102085 -0.35265971 0.40206260 2.25527616 0.03150000 1 0.0802 KKC 2023 0.00472346 11.15430577 -0.54405800 0.40521585 1.74401272 0.03250000 0 0.0505 KMT 2012 0.01277235 12.45729040 0.08705850 0.53636852 1.29467769 0.09210000 0 0.0525 KMT 2013 0.00847882 12.36204595 0.05217875 0.49631886 1.35832924 0.06600000 0 0.0542 KMT 2014 0.00639674 12.72146184 -0.09514800 0.65279910 1.21125369 0.04090000 0 0.0598 KMT 2015 0.00620647 13.17164485 0.48280154 0.77657837 1.07040627 0.00630000 0 0.0668 KMT 2016 0.00976680 13.18729307 -0.11244338 0.77634188 1.06159421 0.02660000 0 0.0621 KMT 2017 0.01517719 13.58079025 0.32851040 0.83988963 1.04870983 0.03530000 0 0.0681