The impact of vietnam’s steel financial indicators on investment returns The impact of vietnam’s steel financial indicators on investment returns
INTRODUCTION
Statement of the Problem
The steel industry is taken into consideration the spine of the national economic system, playing a strategic role inside the manner of industrialization and modernization of the country In the file providing to construct "Vietnam Steel Development Strategy to 2030, Vision to 2050", the Ministry of Industry and Trade emphasized: "The steel industry is a basic industry, presenting enter substances for important economic sectors of the country such as mechanical manufacturing, supporting industries On the other hand, strongly developing the steel production industry also creates a solid foundation and develops the market for processing and manufacturing industries, construction, machinery contributing to creating a stable supply source and improving the productivity and efficiency of industrial sectors." Currently, many researchers have assessed the importance of the steel industry Steel is an important aspect for the improvement of any current economy within the international these days (Endri, Ridho et al 2021) Steel consumption is taken into consideration a critical indicator to degree socio-financial development and dwelling requirements of human beings within the country (Balakrishnan, C.,
2016) Recognizing the key role of the steel enterprise, the enterprise has evolved strongly (Balakrishnan, C., 2016) Currently, the steel enterprise continues to be on the rise, the steel enterprise finished an average increase rate of about 10% yearly, even as the common annual GDP increase price in Vietnam turned into 6.2% from
By the give up of 2023, there had been more than 20 steel firms indexed at the Vietnamese stock market on all 3 exchanges: Ho Chi Minh City Stock Exchange (HOSE), Hanoi Stock Exchange (HNX) and UPCOM According to Vietnam Steel Association, Vietnam's metal enterprise holds a weighted position within the
VNindex by means of 2023 The general market capitalization of the VNindex reaches 6,660,000 billion VND, envisioned to be equivalent to 65.2% of Vietnam's GDP by using 2023 Notably, the steel industry itself contributed a substantial 1,495,000 billion VND to this total market cap This translates to a 22.3% weight for the steel industry within the VNindex Furthermore, in 2023, the steel industry contributed 12.42% to the growth of the VNindex These numbers underscore the significant role the steel industry plays in the Vietnamese stock market
With open potential and opportunities, many investors are currently interested in and trading in steel industry stocks listed on Vietnamese stock exchanges Of course, for each trader, the most important thing is investment returns Therefore, researching the impact of steel industry financial indicators on investment returns is extremely important and necessary To help investors to come to decision with significant investment choices, the usage of financial ratio evaluation is essential Evaluating investment possibilities is of extreme significance due to the need to maximise investment returns (Kabir, Aripin et al 2017) This helps investors make effective investment decisions and helps businesses in shareholder relations to raise capital Therefore, this research will be useful for investors interested in steel stocks, and managers of steel companies
My decision to delve into this topic stems from a confluence of personal aspirations and professional experiences As an intern at SSI Securities Corporation, the author was fortunate to receive my initial training and guidance in steel analysis under the mentorship of my manager While the steel industry presents a challenging analytical landscape due to its inherent volatility and cyclical nature, it also stands as a cornerstone of Vietnam's economy, full of untapped opportunities for further development Beyond the intellectual allure of steel industry analysis, I harbour a deep-seated desire to establish myself as a dedicated researcher in this field The prospect of contributing to a more comprehensive understanding of “The impact of Vietnam’s steel financial indicators on investment returns” holds immense personal gratification The author is confident that this research endeavor will not only enrich my academic pursuits but also enable me to make a meaningful contribution to the world of finance and investment
Research Objectives
Evaluate the impact of financial indicators in Vietnam’s steel industry on investment returns in publicly traded steel companies listed on the Stock Exchange of Vietnam
First, identify the important financial indicators in the Vietnamese steel industry that affect the return on investment
Second, use each financial indicator to analyze the effect on the return on investment
Third, based on the findings of the thesis, investors and managerial implications also proposed.
Scope
Scope: The study uses data collected from the annual reports and financial statements of 14 steel companies listed in the Vietnamese stock market In reality, there are over 20 steel companies listed on the Vietnamese stock market However, according to the author's survey, only 14 steel companies met the criteria and had sufficient information available as of 2023 to serve the research purposes
Temporal: The study provides analytical data from 2014 to 2023 The reason the author chose this period is because the Vietnamese stock market is still quite new, the chosen timeframe ensures the availability of reliable and consistent financial data for 14 steel companies under investigation This period also encompasses a decade of significant developments in Vietnam's steel industry, capturing both periods of growth and fluctuations.
Research Methodology
This thesis uses a mixed methods approach that combines qualitative and quantitative research methods The qualitative method includes statistical analysis, integration, analysis and previous analysis of the existing literature to establish the theoretical foundation of the study is established quantitative method uses linear regression model role to identify financial indicators and the extent to which each indicator influences the investment returns of Vietnamese steel companies
In this thesis, the author will use these following research methods:
- Literature Review Method: Textbooks, articles and previous research will be used as references for the study
- Data Collection, Processing, and Analysis Method: The study will use the help of both EXCEL and STATA 17.0 software systems, to analyze the impact of Vietnam's steel industry financial indicators on investment returns
- In addition, the thesis will use the OLS regression model, fixed effects model (FEM), random effects model (REM), F-test, Breusch and Pagan Lagrangian, Hausman test, and model impairment tests to examize the impact of
Vietnamese steel industry financial indicators on returns of investment.
Research Structure
Besides the abstract, appendix, and references, the thesis is divided into five chapters:
This chapter presents the major aspects of the research topic, e.g:
- The rationale behind the author's choice of topic
- The establishment of research objectives and scope
- The research methodologies used in the topic
- The contribution of the research topic
This chapter provides a detailed overview of the basic principles of investment returns, financial indicators and their impact on investment return, including the steel industry Additionally, the author reviews prior empirical studies to identify quantitative factors that serve as the basis for proposing the research model
Chapter 3: Research Methodology and Research Model
In Chapter 3, the author proposes a research framework based on the established principles, presents the research model adapted from previous research, and identifies the research data, research methodology, and data processing sequence used in the study
Chapter 4: Research Results and Discussion
This chapter tests hypotheses with the proposed research model and data collected from 14 steel companies in Vietnam and estimates the regression coefficients of variables using the statistical software STATA 17.0 The aim is to find out drawbacks of the model, test hypotheses and draw conclusions
Chapter 5: Conclusion and Investors and Managerial Implications
This chapter summarizes the empirical findings and presents several investors and managerial implications and policy recommendations aimed at increasing investment returns in Vietnamese steel companies Additionally, the author acknowledges the limitations of the thesis and suggests directions for future research
LITERATURE REVIEW
Financial indicators
According to (DePamphilis 2014), financial indicators evaluation determines the financial strengths and weaknesses of a organization by using calculating working ratios from that organisation's financial statements Studies of financial institutions display that analysts use financial ratios These ratios are calculated from numbers in a company's financial statements They help analysts understand the company's performance better The figures are taken from a company's financial statements – balance sheet, income statement, and cash flow statement – are employed to carry out quantitative analysis and assess a company's ability to pay debts (liquidity), risk level (leverage), growth rate, profitability, and overall value
Foster (1978) shows that financial ratios establish the connection among items on the balance sheet and profit and loss account to identify the firm's strengths and weaknesses
In their research, Babalola & Abiola (2013) use financial ratios to compare distinct information from financial statements to analyze overall performance of a company While calculating a ratio is a simple mathematical operation, interpreting its meaning is more complex Financial ratios can be interpreted as hints, indicators, or red flags highlighting notable associations between variables used to measure a company's performance
D Kharatyan emphasizes that a number of the maximum crucial questions that financial ratios help solution consist of: Were all resources utilized effectively? Did
7 the company's profitability meet or surpass expectations? Were financing decisions made prudently? Kharatyan further emphasizes that financial indicators analysis can be used to examine a company's current state in comparison to its past performance, effectively monitoring economic performance over time Comparing current performance to past performance is enormously beneficial as it enables market participants to identify issues that require resolution D Kharatyan further emphasized that the actual energy of financial ratios lies of their incorporated use This combined evaluation helps to keep an eye on changes in a company's financial health and overall performance through the years Additionally, evaluating those developments to similar corporations or enterprise averages strengthens the insights gained Financial indicators must be compared to industry norms to determine if a specific company is performing well within the industry or lagging behind its peers
Therefore, this research utilizes financial indicators among steel companies within the same steel industry to analyze their impact on investment returns
There's no perfect approach to choosing the proper financial ratios Different researchers have different ways of classifying them, making it a complex subject matter The classification of ratios has been a contentious trouble among researchers because of the lack of a common technique to selecting relevant ratios that encompass all aspects of a firm's information (Khan and Riaz 2019) To decide whether or not these financial ratios can be employed to differentiate sub-sectors inside the manufacturing industry, (Kalayci, S 2005) classified financial indicators into four groups: profitability, financial leverage/solvency, liquidity, and activity
For the development of prediction models (Altman, 1968, Beaver, 1966, Ohlson,
1980), financial indicators can be labeled into seven categories: solvency, profitability, cash flow ratios, capital structure ratios, turnover ratios, growth, and others (Liang, Lu et al 2016) The five primary categories of financial ratios determine profitability, efficiency, leverage, liquidity, and market value ratios (Sami
RM MUSALLAM 2017) In this research, to investigate the impact of Vietnam's steel industry financial indicators on investment returns, the author will classify
8 financial indicators into four groups: Profitability ratios, Liquidity ratios, Leverage ratios, and Efficiency ratios
Author bases this classification of financial indicators on the study paper: The effect of financial ratios on the firm value: Evidence from Turkey by (Karaca, S S., & Savsar, A 2012)
Author utilizes four classes of financial indicators in this research because of their popular applicability These four groups of financial indicators are drastically used in financial analysis and are depended on by way of investors, managers, and other stakeholders Moreover, the four classes of Profitability ratios, Liquidity ratios, Leverage ratios, and Efficiency ratios include important financial indicators that reflect diverse facets of a firm's financial health
Analyzing those four classes of ratios simultaneously allows the author to advantage a complete expertise of the economic scenario of Vietnam's steel industry and the impact of these elements on investment returns
Table 2.1: Classification of financial ratios
Return on average equity (ROEA)
Total liability/total asset (TL/TA)
Return on average assets (ROAA)
Interest coverage ratio (ICR) Accounts receivable turnover (ART)
Earning before interest, tax, depreciation and amortization margin (EBITDA margin)
Investment returns
According to (Bodie, Z., Kane, A., & Marcus, A J.2022), In the context of finance and making an investment, investment return usually known as total profit or loss from an investment over a time frame It includes 2 factors: income earnings earned from the investment (e.g dividends, interest payments) and any capital appreciation or depreciation that the investment asset itself experiences
2.2 Relationship between financial indicators and investment returns
In the investment industry, an entire knowledge about the relationship between financial indicators and investment returns is crucial for making informed decisions Financial signs function an angle on a firm's overall economic, financial condition and performance, providing valuable insights into its earnings-producing capacity for investors
Investment returns are affected by fundamental factors: price adjustments and earning yields (dividends) Price adjustments discuss with the increase in the marketplace price of a stock through the years Price adjustments are pushed with the aid of various factors, inclusive of organization-particular information, average market traits, and investor sentiment While marketplace factors can have an effect on stock expenses within the brief term, it is the essential factors that power lengthy-time period price movements and, therefore, long-term investment returns These essential elements are deeply rooted in a corporation's economic overall performance and are reflected through various financial indicators
Dividends constitute the earnings generated from owning shares, in the form of dividends paid to shareholders Dividend presents a steady stream of returns for investors and might make contributions extensively to standard investment returns Dividend specially relies upon on results of company's business, and whether or not business results effects are precise or not, relies upon on financial indicators
Financial indicators are not in reality separate numbers, but they're connected to each different and mirror the overall situation and prospects of a firm When these signs paint a tremendous picture of a firm, it signals destiny boom potential and therefore better stock investment returns.
Empirial research
The relationship between financial indicators and investment returns continually a topic of interest across many different investment sectors Therefore, many studies have explored the impact of financial ratios on stock returns
(LKC Chan 1991) examines the relationship between various characteristics and returns of Japanese stocks They explore the impact of four key variables: a measure of profitability (earnings yield), firm size, a valuation indicator (book-to-market ratio), and a measure of cash flow (cash flow yield) The study uses a comprehensive dataset of Japanese stocks from 1971 to 1988, including companies of different industries and sizes listed on both major stock market segments Using various statistical techniques, the analysis shows a significant association between these variables and expected returns in the Japanese market Notably, among the four variables examined, the book-to-market ratio and cash flow yield exhibit the most substantial positive influence on stock returns
(Mukerji, Dhatt, and Kim 1997) looked at how different factors affected stock returns in Korea between 1982 and 1993 They found that stocks with higher ratios of book value to market value (potentially undervalued), sales to price, and debt to equity tended to have better returns Conversely, larger companies generally had lower returns Interestingly, the ratio of earnings to price and a measure of stock volatility (beta) did not have a significant impact on returns in this study The researchers also concluded that for Korean stocks specifically, the book-to-market ratio and sales-to-price ratio were better indicators of a stock's value than the earnings-to-price ratio
(Stefano, Kevin 2015) carried out to find the potential link between financial ratios and the high stock returns observed in the Indonesian property industry The
11 researchers employ judgment sampling to gather financial ratio data from 18 property companies listed on the Indonesia Stock Exchange Multiple linear regression analysis is then used to analyze the data The findings reveal a statistically significant collective influence of financial ratios on stock returns However, when examining individual ratios, only Return on Assets (ROA) demonstrates a significant positive impact on stock returns within the Indonesian property sector
The research of (Dwi, 2009) investigates how well accounting information explains a company's stock performance The study considers profitability, liquidity, leverage, market valuation, firm size, and cash flow as measures of accounting information It then analyses both abnormal returns and market-adjusted returns to capture stock performance The study focuses on actively traded manufacturing companies listed on the Indonesia Stock Exchange from 2003 to 2006 The findings reveal that profitability, asset turnover, and market valuation ratios significantly influence stock returns Especially, ROE, DER, PBV, CFO have positive impact on return, while total assets turnover (TATO), has negative correlation with return
(Ozturk, H and Karabulut, T.A., 2020) explores the influence of financial ratios: Price-to-Sales, Earnings per Share (EPS), Debt-to-Equity, and EBITDA Margin on the returns of technology and telecommunication stocks listed on the Istanbul Stock Exchange The study utilizes panel data analysis, employing both the Parks-Kmenta estimator and the Two-way Mixed Effects Model Companies with higher EPS and EBITDA Margin tend to experience higher returns in subsequent quarters, while lower Price-to-Sales ratios are associated with increased returns in following periods Interestingly, the Two-way Mixed Effects Model suggests a negative correlation between the Debt-to-Equity ratio and stock returns, implying that higher debt levels might lead to lower returns
(Al-Lozi, 2016) examizes the link between financial indicators and stock returns for manufacturing companies listed on the Amman Stock Exchange The research team analyzes data from 65 companies over a ten-year period (2001-2011) Profitability metrics include Net Profit Margin, Gross Profit Margin, Return on Assets, Return
12 on Equity, and Earnings per Share Leverage is assessed using Debt Ratio, Debt to Equity Ratio, and Interest Coverage Ratio Statistical methods like correlation analysis, multiple regression, and descriptive statistics are employed to explore these relationships Data is sourced from published annual reports and monthly bulletins issued by the Amman Stock Exchange The findings reveal that Gross Profit Margin, Return on Assets, Return on Equity, and Earnings per Share have a statistically significant connection to stock returns Conversely, Net Profit Margin and leverage ratios (Debt Ratio, Debt to Equity Ratio, and Interest Coverage Ratio) do not exhibit a significant association with stock returns in this context
In a study investigating the factors affecting stock returns of manufacturing companies listed on the BEJ, (Sparta and Februwaty 2005) analyzed the influence of three financial ratios: return on equity (ROE), earnings per share (EPS), and cash flow from operations (CFO) Their findings, based on data from 32 companies between 1999 and 2002, revealed that only ROE had a statistically significant positive impact on stock returns (at a 5% significance level) Conversely, EPS and CFO exhibited statistically insignificant negative effects on stock returns
(Kennedy 2003) examined how various financial ratios influence stock returns for companies listed on the LQ45 index of the BEJ between 2001 and 2002 The study considered factors like return on assets (ROA), return on equity (ROE), earnings per share (EPS), profit margin, asset turnover, deferred tax assets (DTA), and debt-to- equity ratio (DER) While the analysis revealed positive effects on stock return from total asset turnover (TATO), ROA, EPS, and DER, and negative effects from ROE and DTA, surprisingly, none of these relationships were statistically significant In other words, the study found no conclusive evidence that these financial measures directly impacted stock returns in the timeframe examined
In Vietnam, there have also been studies that assess the impact of financial ratios on stock returns Some examples include:
(Hong Minh, Phuong Linh, 2022) evaluate the impact of basic financial information on the stock prices of pharmaceutical companies This relationship is assessed with a lag of three months from the end of the fiscal year to the time the information is
13 reflected in the stock price The results show that earnings per share (EPS), book value per share (BVPS), and the price-earnings ratio (PE) have a positive impact on stock prices
(Hung, D N., Ha, H T V., & Binh, D T 2018) investigate how accounting information impacts the stock prices of energy companies listed on Vietnam’s stock market This study investigates how factors like a company's profitability (measured by return on assets or ROA), its financial structure (represented by capital structure or LV), its size, current ratio (CR), and accounts receivable turnover (Turnover) affect stock price It does this by using two statistical methods: ordinary least squares (OLS) regression and quantile regression The findings indicate that ROA, Size, CR, and Turnover are positively associated with stock prices However, capital structure (LV) does not significantly affect stock prices
(Huong Giang 2019) investigates the impact of accounting information on financial statements on the stock prices of listed companies on the Ho Chi Minh City Stock Exchange (HOSE) The author collects information on the variables earnings per share (EPS), book value per share (BVPS), return on assets (ROA), dividend per share (DPS), financial leverage (FL), company size (SIZE), and growth
(SALEGROWTH) of listed companies on the HOSE in the period from 2012 to
2017 Using the quantitative method with panel data through the regression analysis technique according to the methods of ordinary least squares regression (OLS), fixed effects regression model (FEM), random effects regression model (REM), GMM estimation regression model supported by STATA 13 software to measure the impact of independent variables on the dependent variable in the model The research results based on the GMM model show that the EPS variable does not have an impact on stock prices, the BVPS variables, DPS, ROA, SIZE, SALEGROWTH have a positive impact and the FL variable has a negative impact on stock prices
RESEARCH METHODOLOGY AND RESEARCH MODEL 16 1 Research Model
Research Processes
This study employs various statistical methods to analyze the relationship between financial indicators and investment returns for steel companies listed on the
Vietnamese stock market The specific steps involved in the research process are as follows:
- Step 1: Descriptive Statistics: Analyze the distribution of the variables using descriptive statistics
- Step 2: Correlation Analysis: Examine the correlation matrix to assess the strength and direction of the relationships between the variables
- Step 3: Regression Analysis: Employ three regression models: Pooled OLS, Fixed Effects Model (FEM), and Random Effects Model (REM) using Stata 17.0
+ Apply F-test to choose between the Pooled OLS and FEM
+ Apply the Breusch-Pagan test to select between the Pooled OLS and REM + Utilize the Hausman test to choose between the FEM and REM
- Step 5: Diagnostics: Conduct autocorrelation tests to check for serial correlation in the error terms, examine heteroscedasticity tests to assess whether the variance of the error terms is constant
- Step 6: Model Correction: If necessary, apply the Generalized Least Squares (GLS) method to address any identified issues with the model
- Step 7: Interpretation and Evaluation: After implementing the aforementioned techniques in Stata 17.0, the study will analyze and evaluate the research findings.
Research Data
The study utilizes annual financial statement data from 14 steel companies listed on the Vietnamese stock market during the period 2014-2023 The companies are listed on all three stock exchanges: Ho Chi Minh Stock Exchange (HoSE), Hanoi Stock Exchange (HNX), and Unlisted Public Company Market (Upcom) According to the author's survey, 14 steel companies totally met the criteria and had sufficient information available as of 2023 to serve the research purposes and in the context of Vietnam stock market is quite new, 10-year period of steel industry ensures the availability of reliable and consistent financial data for 14 steel companies under investigation
The financial indicators employed in this research include: Earnings Per Share, Return on Equity, Return on Assets, Quick Ratio, Current Ratio, Inventory
Turnover, Asset Turnover, Net Profit Margin, Account Receivable Turnover, Debt- to-Equity Ratio, Total Liability/Total Asset, Interest Coverage Ratio, EBITDA Margin These indicators were collected from annual reports, financial reports of each company and the website: vietstock.vn data source.
Research Metholodogy
To achieve the research objective of examining the impact of Vietnam's steel industry financial indicators on investment returns, the author employs the following research methods:
Utilizing Stata 17 software, the author analyzes the descriptive statistics of the data collected and synthesized from the annual reports and financial statements of 14 steel companies listed on the Vietnamese stock market This involves calculating measures such as the total number of observations, maximum value, mean value, and minimum value of the research sample The descriptive statistics provide an
20 initial overview and assessment of the financial performance of the listed steel companies
Employing Stata 17 software, the author constructs a correlation matrix to determine the strength, direction, and linear relationship between the dependent variables, the independent variables, and among the independent variables themselves Based on the correlation matrix results, the author can identify the correlation relationships between the variables and establish a foundation for analyzing and discussing the research model outcomes
Panel data regression approaches are utilized in this thesis The regression estimations include the Ordinary Least Squares (OLS) method, the Fixed Effects Model (FEM) method, and the Random Effects Model (REM) approach After carrying out the vital checks, if the models have heteroscedasticity or autocorrelation, the Generalized Least Squares (GLS) method is carried out to cope with those problems
A pooled OLS regression model is a regression method for estimating coefficients of selected variables based on data that includes both time series and cross-sectional data (no difference between cross-sectional units), with constant α all cross- sectional unit The assumption of this approach is justified only if there is equality between the cross-sectional units The assumption of this method is only correct when there is homogeneity between the cross-sectional units Several statistical models can be derived from the Pooled OLS model The R-square indicates the effect of the independent variable on the dependent variable A high R-square value indicates that the variation in the dependent variable is explained by the independent variable An F-test is conducted to test the significance of the effect of the independent variable on the dependent variable A P-value is a statistical test value and is typically compared to 0.05 If the P-value is less than 0.05, the effect of the independent variable on the dependent variable is very statistically significant
However, the problem with this approach is that, besides the possibility of autocorrelation in the data often or the limitation of the residuals causing the
Durbin-Watson value to be low, the limitation of cross-sectional units of the OLS model is also very robust in practice Therefore, it is important to use FEM and REM models to overcome the problems encountered in the Pooled OLS model The FEM and REM models would be more appropriate because they do not ignore temporal factors and individual factors
The fixed effects model (FEM) is a statistical approach that assumes that each group of samples has unique characteristics that can affect explanatory variables The FEM is used to examine the relationship between the residuals of each unit and the explanatory variables This analysis aims to monitor and isolate the effects of these unit-specific characteristics (which remain constant over time) from the expression variables, in order to account for the true effect of expression change types are obtained on the dependent variables and as a result the FEM handles well the case of omitted variables This regression model uses dummy variables to capture the omitted (latent) characteristics of each firm in the data The FEM still relies on the Pooled OLS method Significant statistics in the FEM model are similar to Pooled OLS, including R-square, F-test, and P-value However, a drawback of this method is the reduced degree of freedom in the model, especially when the number of dummy variables is large
The random effects model (REM) examines into the unit-specific differences in analyzed factors over time that contribute to the model and the common (invariant across cross-sectional units) effect of the explanatory variables and the resulting autocorrelation comes with a potential issue in the model that needs to be addressed One of the main advantages of using REM is its ability to eliminate issues of heteroscedasticity The main statistical method used is the Generalized Least
Squares (GLS) method The main difference between FEM and REM lies in the variation across units Although the FEM assumes that the cross-sectional units
22 differ in their fixed intercepts and that the variability between units is related to the independent variables in the FEM model, the REM resides in the error term and computes variability between units as with explanatory variables Thus, when unit- specific differences affect the dependent variable, the REM appears more appropriate than the FEM In REM, the residual of each item (uncorrelated with the explanatory variable) is treated as an additional explanatory variable
After obtaining the results from the three models, the author compares them to select the most appropriate model F-test is used to choose between Pooled OLS and FEM, Breusch-Pagan Lagrangian test is used to select between Pooled OLS and REM, and Hausman Test is used to select between FEM and REM
4.3.4 F-Test for Selecting between Pooled OLS and FEM
While the Pooled OLS model does not differentiate by year and therefore does not control for the individual characteristics of each firm, leading to potentially inefficient estimation results, the author still conducts the F-test to choose between the Pooled OLS or FEM model with the following hypotheses:
- H0: The Pooled OLS model is appropriate
- H1: The FEM model is appropriate
If the Prob > F value of the F-test is greater than 5%, then H0 is accepted and H1 is rejected, meaning that the Pooled OLS model is appropriate Conversely, if the Prob > F value is less than 5%, then H0 is rejected and H1 is accepted, meaning that the FEM model is appropriate
4.3.5 Breusch-Pagan Lagrangian Test for Selecting between Pooled OLS and REM
The Breusch-Pagan Lagrangian test is used to select between the OLS and REM methods for panel data regression, based on the assumption that H0: the error variance is homoscedastic (constant) since heteroscedasticity is the reason for the difference between OLS and REM
• H0: The Pooled OLS model is appropriate
• H1: The REM model is appropriate
If the Prob > chibar2 value of the Breusch-Pagan Lagrangian test is greater than 5%, then H0 is accepted and H1 is rejected, meaning that the Pooled OLS model is appropriate Conversely, if the Prob > chibar2 value is less than 5%, then H0 is rejected and H1 is accepted, meaning that the REM model is appropriate
4.3.6 Hausman Test for Selecting between FEM and REM
To choose between the REM and FEM models, the author uses the Hausman Test to check whether there is autocorrelation between Ɛit and the independent variables with the following hypotheses:
- H0: Ɛit is not correlated with the independent variables
- H1: Ɛit is correlated with the independent variables
If the P-value is less than 0.05, then H0 is rejected and H1 is accepted, indicating that Ɛit is correlated with the independent variables and the fixed effects model (FEM) should be used Otherwise, H0 is accepted and H1 is rejected, indicating that Ɛit is not correlated with the independent variables and therefore the random effects model (REM) should be used
This section discusses the diagnostics conducted to check for potential issues in the estimated models, including multicollinearity, heteroscedasticity, and autocorrelation
RESEARCH RESULTS AND DISCUSSION
Descriptive Statistics of Research Data
The results of the descriptive statistics for the measurement variables in the regression model of the research thesis are presented in Table 4.1 below:
Table 4.1: Descriptive Statistics of Variables in the Research Model
Variable Obs Mean Std.dev Min Max
Source: Results processed using STATA 17 software
Table 4.1 summarizes the variables in the model The data is from 14 steel companies listed on the Vietnamese stock market from 2014 to 2023 The results show the following:
Investment returns of steel companies during the period 2014-2023 have an average value of 0.3115697 Investment return has a minimum value of -0.743295 belonging to Thanh Thai Group Joint Stock Company (KKC) in 2022 and a maximum value of 3.205607 belonging to Dai Thien Loc Joint Stock Company (DTL) in 2021 In addition, investment return has a standard deviation of 0.3115697 among companies in the industry
Earning per share (EPS) has an average value of 1814.159 and a standard deviation of 3544.579 for companies in the period 2014-2023, indicating a high level of variability in earning per share among companies in the same industry EPS has a minimum value of -12027.27 and a maximum value of 14345.99, both belonging to SMC Trading Investment Joint Stock Company (SMC) in 2023 and 2021, respectively
The profitability of steel companies during the period 2014-2023, as measured by the ROEA and ROAA ratios, has average values of 0.0432337 and 0.0064463, respectively Return on average equity (ROEA) has a minimum value of
0.00000441 belonging to Tien Len Steel Group Joint Stock Company (TLH) in
2023 and a maximum value of 0.5389028 belonging to SMC Trading Investment Joint Stock Company (SMC) in 2023 The standard deviation of ROEA is 0.0802 among companies in the industry For Return on average asset (ROAA), the minimum value is 0.00000081 belonging to Thai Nguyen Iron and Steel Joint Stock Company (TIS) in 2022 and the maximum value is 0.0647703 belonging to Thanh Thai Group Joint Stock Company (KKC) in 2016 The standard deviation of ROAA is 0.0113194 among companies in the industry
Quick ratio (QR) has an average value of 0.2218571 for companies in the period 2014-2023 Quick ratio has a minimum value of 0.01 belonging to Dai Thien Loc Joint Stock Company (DTL) in 2020 and 2021, Thanh Thai Group Joint Stock Company (KKC) in 2023, Central Mental Joint Stock Company (KMT) in 2017,
2021 and 2023, Viet Duc Steel Pipe Joint Stock Company VG PIPE (VGS) in 2014,
2017 and 2019, Thai Nguyen Iron and Steel Joint Stock Company (TIS) in 2017 Quick ratio has a maximum value of 1.69 belonging to Thu Duc Steel Joint Stock Company - Vnsteel (TDS) in 2023 The standard deviation of QR is 0.3045874 among companies in the industry
Current ratio (CR) has an average value of 1.515357 for companies in the period 2014-2023 Current ratio has a minimum value of 0.37 belonging to Thai Nguyen Iron and Steel Joint Stock Company (TIS) in 2023 and a maximum value of 13.16 belonging to Thu Duc Steel Joint Stock Company - Vnsteel (TDS) in 2022 The standard deviation of CR is 1.209115 among companies in the industry
Inventory turnover (IT) has an average value of 10.96836 for companies in the period 2014-2023 Inventory turnover has a minimum value of 0.98 belonging to Dai Thien Loc Joint Stock Company (DTL) in 2021
Asset Turnover (AT) has an average value of 2.954571 for companies in the period 2014-2023 Asset turnover has a minimum value of 0.64 belonging to Dai Thien Loc Joint Stock Company (DTL) in 2021 and a maximum value of 9.75 belonging to Viet Duc Steel Pipe Joint Stock Company VG PIPE (VGS) in 2018 The asset turnover ratio exhibits a significant difference between the minimum value of 0.64 and the maximum value of 9.75, indicating that the efficiency of asset utilization varies considerably among companies in the industry The minimum value of the variable is 0.64, suggesting that the company is not using its assets effectively to generate revenue
Account Receivable Turnover (ART) has an average value of 12.66186 for companies in the period 2014-2023 Account receivable turnover has a minimum value of -0.05 belonging to Dai Thien Loc Joint Stock Company (DTL) in 2018 and a maximum value of 31.94 belonging to VICASA Steel Join Stock Company -
VNSTEEL (VCA) in 2016 The standard deviation of ART is 6.456464 among companies in the industry
Debt-to-equity ratio (DE) has an average value of 1.999426 for companies in the period 2014-2023 DE has a minimum value of 0.2674745 belonging to Thu Duc Steel Joint Stock Company - Vnsteel (TDS) in 2022 and a maximum value of
6.744954 belonging to SMC Trading Investment Joint Stock Company (SMC) in
2023 The standard deviation of DE is 1.450273 among companies in the industry
Total Liability/Total Asset (TLTA) has an average value of 0.5990149 for companies in the period 2014-2023 TLTA has a minimum value of 0.2110295 belonging to Thu Duc Steel Joint Stock Company - Vnsteel (TDS) in 2022 and a maximum value of 0.8708837 belonging to SMC Trading Investment Joint Stock Company (SMC) in 2015 The standard deviation of TLTA is 0.154431 among companies in the industry
Net Profit Margin (NPM) has an average value of 0.0249689 for companies in the period 2014-2023 NPM has a minimum value of -0.1452856 belonging to Thanh Thai Group Joint Stock Company (KKC) in 2022 and a maximum value of
0.2472508 belonging to Hoa Phat Group Joint Stock Company (HPG) in 2021 The standard deviation of NPM is 0.0544492 among companies in the industry
Earning before interest, tax, depreciation and amortization margin (EBITDA margin) has an average value of 0.0586014 for companies in the period 2014-2023 EBITDA margin has a minimum value of -0.1136 belonging to Thanh Thai Group Joint Stock Company (KKC) in 2022 and a maximum value of 0.3508 belonging to VNECO Steel Structure Manufacturing Joint Stock Company (SSM) in 2016 The standard deviation of EBITDA margin is 0.0671343 among companies in the industry
Interest Coverage Ratio (ICR) has an average value of 0.044804 for companies in the period 2014-2023 ICR has a minimum value of -1.395494 belonging to Thanh Thai Group Joint Stock Company (KKC) in 2022 and a maximum value of
0.5643736 belonging to Thu Duc Steel Joint Stock Company - Vnsteel (TDS) in
2017 The standard deviation of ICR is 0.2038571 among companies in the industry
Correlation Analysis
Correlation analysis measures the degree of linear correlation between variables According to Kennedy (2008), if the absolute values of the correlation coefficients in the correlation matrix are less than 0.8, it indicates that the independent variables are not linearly correlated with each other In this thesis, the author analyzes the correlation according to the model between the dependent variable Investment return and the independent variables in the model
Table 4.2: Correlation matrix of the model with the dependent variable Investment return
IR EPS ROEA ROAA QR CR IT
AT ART DE TLTA NPM EBITDA margin
Source: Results processed using STATA 17 software
Based on the results of Table 4.2, regarding the correlation matrix between the variables in the Investment return model, it can be seen that the correlation coefficients between the variables are all less than 0.8, so there is no linear correlation between the independent variables in the model
In particular, with a significance level of 10%, the variables EPS, ROEA, ROAA, NPM, EBITDA margin, ICR have a positive correlation (+) with Investment return with coefficients of 0.4950, 0.2730, 0.2861, 0.4144, 0.2653, 0.3247, respectively This means that the higher the Earning per share, Return on average equity, Return on average asset, Net profit margin, Earning before interest, tax, depreciation and amortization margin, Interest coverage ratio, the higher the investment return
At the same time, CR, IT, AT, DE, TLTA have a negative correlation (-) with
Investment return with coefficients of -0.1029, -0.0661, -0.1440, -0.0566, -0.0034, respectively This means that when Current ratio, Inventory ratio, Asset turnover, Debt-to-equity ratio, Total Liability/Total Asset decrease, Investment return will increase.
Regression Analysis
3.1 Results of Pooled OLS, FEM, REM Regression
The regression results for the Investment returns model are presented in the form of Pool OLS, FEM, and REM regression models The results are shown in Table 4.3
Table 4.3: Results of Pooled OLS, FEM, REM Regression Model
Source: Results processed using STATA 17 software
Based on the results obtained in Table 4.3, the results of the three models Pooled OLS, FEM, and REM all show that Earning per share (EPS), Return on average
Comparing Regression Results between Pooled OLS and REM
Table 4.5: Results of Choosing between the Pooled OLS and REM Models Breusch and Pagan
Lagrangian test chibar2(01) Prob > chibar2
Source: Results processed using STATA 17 software
The author used the Breusch and Pagan Lagrangian test to choose between the Pooled OLS and REM models The test result shows P-value = 1.0000 > 0.05
(greater than 5%) Therefore, the Pooled OLS model is more suitable for estimation than the REM model.
Comparing Regression Results between FEM and REM models
Table 4.6: Results of Choosing between the FEM and REM Models
Hausman test chi2 Prob > chi2
Source: Results processed using STATA 17 software
To determine the most suitable model between FEM and REM, the author conducted the Hausman test The test result shows that with a significance level of α
= 5%, Prob = 0.0011 < 5% (less than 5%) Therefore, the FEM model is more suitable for estimation than the REM model
Conclusion: Based on the results of the three tests above, the FEM model is the most suitable model for estimating the Investment return model among the three regression models.
Model Deficiency Check
To address the issue of multicollinearity, one possible approach is to check for multicollinearity using the Variance Inflation Factor (VIF) As a rule of thumb, a lower VIF value indicates a lower likelihood of multicollinearity, while a higher value suggests a higher likelihood According to Gujarati and Porter (2004), a VIF value greater than 10 indicates that the corresponding variable is definitely suffering from multicollinearity The results are shown in Table 4.7
Table 4.7: Results of Multicollinearity Test using VIF
Source: Results processed using STATA 17 software
Results from the table above show that all the variance inflation factors (VIFs) of the independent variables, except for TLTA, are less than 10 Therefore, it can be concluded that the model does not have a serious multicollinearity problem For the independent variable TLTA, the author will remove this variable from the model to avoid affecting the model results
After conducting three tests: F-test, Breusch and Pagan Lagrangian test, Hausman test, and concluding that FEM is the most suitable model, the author uses the
Modified Wald test to test for heteroscedasticity
Table 4.8: Results of Heteroscedasticity of the model
Modified Wald test chi2 (14) Prob>chi2
Source: Results processed using STATA 17 software
Based on the results in Table 4.8, it can be seen that: Prob > chi2 = 0.0000 < 0.05, therefore the research model has heteroscedasticity
To check whether the model has autocorrelation or not, the author uses the
Table 4.9: Results of Autocorrelation Test of the model
Source: Results processed using STATA 17 software
Based on the results in Table 4.9, it can be seen that: Prob > F = 0.5088 > 0.05, therefore the research model does not have autocorrelation.
Addressing Model Deficiencies
5.1 Estimating the Model Using the FGLS Method
After testing the FEM model with the dependent variable Investment return and the independent variables: Earning per share (EPS), Return on average equity (ROEA), Return on average asset (ROAA), Quick ratio (QR), Current ratio (CR), Inventory turnover (IT), Asset turnover (AT), Account receivable turnover (ART), Debt-to- equity ratio (DE), Net profit margin (NPM), Earning before interest, tax, depreciation and amortization margin (EBITDA margin), Interest coverage ratio (ICR), it can be seen that the FEM model is not highly effective and the regression results show heteroscedasticity To address this deficiency, the study uses the Feasible Generalized Least Squares (FGLS) estimation method to overcome this issue
Table 4.10: Regression Results Using the FGLS Method
Source: Results processed using STATA 17 software
Based on Table 4.10, the FGLS estimation results for the investment return model indicate that six independent variables have a statistically significant impact on investment return Specifically, Earning per share (EPS), Return on average equity (ROEA) have a significance level of 1%, Inventory turnover (IT), Asset turnover (AT), Debt-to-equity ratio (DE), and Interest coverage ratio (ICR) have a significance level of 5% The remaining independent variables do not have statistical significance
Therefore, the final result of the investment return model is:
IR it = 0.4423 + 0.000072EPS it + 2.7395ROEA it + 0.0066IT it - 0.0506AT it -
Discussion of Research Results
The results of this research show that six of twelve financial indicators have a significant relationship with investment return are: EPS, ROEA, IT, AT, DE, ICR Where EPS, ROEA, IT, ICR have positive impact on investment return, conversely,
AT and DE have negative impact on investment return The other six financial indicators don’t have a significant relationship with investment return are: ROAA,
QR, CR, ART, NPM, EBITDA margin
Table 4.11: Summary and compare results with other research
Financial ratios Result Similar results Different results
Asset turnover (AT) Negative (-) (Dwi, 2009) (Kennedy 2003) Debt-to-equity ratio
(Al-Lozi, 2016) Return on Asset
2019) Quick ratio (QR) Not statistically significant Current ratio (CR) Not statistically significant Account receivable turnover (ART)
EBITDA margin Not statistically significant
The reason for the differences in research results can be attributed to the main factor: data and methodology
Apart from that, existing other factors such as: differences in industry dynamics company-specific factors, market conditions and investor behaviours, or even accounting standards and regulations between different countries and stock markets
CONCLUSION AND MANAGERIAL IMPLICATIONS
Conclusion
The study was conducted with three objectives: (i) identify the most significant financial indicators in the Vietnamese steel industry that influence investment returns, (ii) analyse the degree of influence exerted by each financial indicator on investment returns, (iii) propose effective investment strategies based on the financial indicators of Vietnam's steel industry for investors and managers
Through the theoretical basis of the topic and the analysis of related studies reviewed to identify research gaps, the research sample was developed The author collected and compiled data from vietstock.vn on 14 listed steel companies listed on the Vietnam Stock Exchange in 10 years Finally, the data were processed using STATA 17 software and the following analysis was obtained
Table 5.1: Summary of Research Results
EPS ROEA IT AT DE ICR
ROAA QR CR ART NPM EBITDA margin
Not Statistica lly Significa nt
Not Statistical ly Significa nt
Not Statistical ly Significa nt
Not Statistical ly Significa nt
Not Statistical ly Significa nt
Not Statistical ly Significa nt
Objective 1: Identify the important financial indicators in the Vietnamese steel industry that affect the return on investment
The study investigated the impact of Vietnam's steel industry financial indicators on investment returns and identified the following financial indicators that have an impact on investment returns: 1- Earning per share (EPS), 2- Return on average equity (ROEA), 3- Inventory turnover (IT), 4- Asset turnover (AT), 5- Debt-to- equity ratio (DE), 6- Interest coverage ratio (ICR) Specifically, when EPS, ROEA,
IT, and ICR increase, investment returns also increase Conversely, investment returns increase when AT and DE decrease
Objective 2: Use each financial indicator to analyze the effect on the return on investment
The study investigated the impact of Vietnam's steel industry financial indicators on investment returns and found that:
- Earning per share (EPS) has a positive impact on investment returns with a significance level of 1%
- Return on average equity (ROEA) has a positive impact on investment returns with a significance level of 1%
- Inventory turnover (IT) and interest coverage ratio (ICR) have a positive impact on investment returns with a significance level of 5%
- Asset turnover (AT) and debt-to-equity ratio (DE) have a negative impact on investment returns with a significance level of 5%
This suggests that the financial indicators belonging to efficiency ratios and profitability ratios have a strong impact on investment returns
Objective 3: Propose investors and managerial implications
The author will present specific recommendations to improve the investment returns of steel companies in Vietnam in Section 2 Investors and Managerial Implications.
Investors and Managerial Implications
This thesis can serve as a valuable reference for investors, particularly those following fundamental analysis principles, emphasizing the importance of basing investment decisions on company value as stock prices ultimately determine investment returns
- Guided by the thesis findings, investors should prioritize companies with high EPS, ROEA, IT and ICR for investment opportunities This is because strong economic indicators generally coincide with higher returns on investments Inventory (IT) is of particular importance in the Vietnamese steel industry due to the prevalence of manufacturing and commodity trading in Vietnamese firms, which requires rapid turnaround time
- Investors should not avoid companies with high asset turnover (AT), but should focus on avoiding high AT companies driven only by asset sales
- Investors should diversify their portfolios, considering the high cyclicality and volatility of the steel industry Diversification can include adding stocks from retail, consumer goods, and other sectors to their portfolios
- For risk-tolerant investors, the steel industry can also be considered as a selection to short-term trading with volatile nature
• Strengthen Cost Management: Implement cost-saving measures across production, management, and sales processes Optimize production costs,
43 minimize waste, and utilize raw materials efficiently to improve earnings per share (EPS)
• Optimize Inventory Management: Employ scientific inventory management methods to minimize inventory risks and optimize inventory turnover (IT) Utilize ERP systems, FIFO/LIFO methods, and strict inventory control to save warehousing costs, reduce obsolescence risks, and enhance capital efficiency
• Improve Labor Productivity: Invest in employee training and development, and implement advanced technologies to automate production and boost labor productivity Enhance employee skills and expertise, utilize robots and automation systems to reduce labor costs, and increase production output
• Reduce Debt Ratio: Carefully utilize debt financing sources and avoid overreliance on bank loans Diversify funding sources, issue equity or corporate bonds to lower borrowing costs, improve interest coverage ratio (ICR), and enhance debt repayment capacity
• Utilize Equity Efficiently: Focus on effective equity utilization to increase return on average equity (ROEA) Invest in high-yielding projects, strengthen asset management, and minimize waste to enhance shareholder returns.
Limitations of the Thesis and Directions for Future Research
Despite the limitations of time, practical experience, and capacity, the thesis has achieved certain research results However, the study still has some limitations, such as:
First, the scope of the study is restrained to 14 steel companies listed on the
Vietnamese stock market Therefore, the results do not fully assess the picture of factors affecting the investment returns of steel companies in Vietnam
Second, the limited pool of companies suitable for research, coupled with the significant variations in market capitalization, revenue, and other financial indicators among these companies, poses challenges for conducting comprehensive industry analysis
Third, Vietnam stock market is quite new, the research period of the thesis is still quite limited, so there are no comprehensive conclusions
First, future research should extend the study timeframe beyond the current 10-year limit to encompass a longer period, enabling a more comprehensive assessment of financial indicators from Vietnam's steel industry and its investment returns over time
Second, the 13 financial indicators can be categorized into four groups: profitability, efficiency, leverage, and liquidity Analyzing the impact by group, rather than individual ratios, provides a more holistic perspective Additionally, incorporating risk-adjusted investment returns enhances the evaluation
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