INTRODUCTION
Rationale
The real estate sector in Vietnam significantly impacts the economy, evidenced by a substantial rise in capital inflows into real estate enterprises This surge in investment has led to a remarkable expansion of the industry, making it ten times larger than other sectors in the market.
In 2021, Vietnam's real estate sector faced significant challenges, marked by an imbalance between supply and demand and soaring market prices Each wave of the Covid-19 pandemic triggered a surge in land prices, culminating in a historic auction of land lots in Thu Thiem that set unprecedented records.
Vietnam's real estate sector is significantly dependent on bank financing, with listed companies revealing that its debt-to-equity ratio surpasses that of other industries This highlights the substantial capital influx from banks into the real estate market (Phạm Hồng Chương et al., 2021).
Financial leverage serves as a double-edged sword in the market, fostering industry and national growth while simultaneously presenting risks related to interest rates and liquidity It not only drives development but also opens avenues for sectors like habitation and tourism, highlighting the balance between opportunity and potential financial pitfalls.
To enhance firm value and performance, business managers must conduct thorough research and provide targeted recommendations for addressing capital structure challenges faced by industry groups This proactive approach is crucial for ensuring sustainable economic development.
I have chosen to focus my graduation thesis on the "Impact of Capital Structure on the Profitability of Vietnamese Real Estate Firms Listed on the Hanoi Stock Exchange," driven by my motivation to explore this significant topic.
Research objectives
The key objectives of the studies include:
- Synthesize the theoretical framework of capital structure, profitability and interrelationship
- Model and analyze the impact of capital structure on the profitability of Vietnamese listed real estate firms on Hanoi Stock Exchange
- Enrich the research field and contribute valuable recommendations to the firms to improve their profitability.
Research subject and research scope
The object of the study is the effect of capital structure on the profitability of 34 real estate enterprises listed on the Hanoi Stock Exchange from 2018 to 2021.
Research method
This thesis employs a combination of qualitative and quantitative research methods to achieve its objectives The author reviews existing studies to develop an optimal model specification and utilizes regression techniques, including Pooled OLS, FEM, and REM models, to address the research questions Furthermore, the research incorporates synthesis and analysis methods based on data gathered from financial statements.
Thesis structure
The thesis has four chapters:
Chapter 4: Empirical Results and Discussion
LITERATURE REVIEW
Theoretical framework
This section examines key theories pertinent to the study of finance, including traditional finance theory, Modigliani and Miller propositions, pecking order theory, trade-off theory, and market timing hypothesis.
The capital structure of an enterprise, also known as financial leverage, is defined as the combination of debt capital and equity used to finance production and business activities (Ross, 2003) An optimal capital structure occurs when the cost of capital is minimized and the firm's value is maximized (Solomon & Weston, 1963) It explains the mix of securities and financing sources that corporations utilize for real investments, including internal sources like retained earnings and stock issuance, as well as external sources such as loans and bonds, which are essential for maintaining business operations and enhancing market survivability (Gangeni, 2006).
According to Brigham and Houston (2009), The optimal capital structure is the capital structure that maximizes stock value An optimal capital structure typically has a debt ratio between 40% and 50%
Total Liabilities Long-term Liabilities
Common stocks Preferred stocks Shareholders’ Equity
Corporate capital structure refers to the proportion of various funding sources utilized by a business, highlighting the relationship between debt and equity in financing total invested assets This concept is crucial for understanding how a company's management approaches funding decisions and balances its financial resources.
The three leverage ratios used to analyze capital structure are debt to assets, debt to equity and equity to assets
A lower debt ratio indicates that a business is less reliant on debt for financing, reducing its risk of financial difficulties Companies with low debt ratios find it easier to secure loans and benefit from lower interest rates compared to those with higher debt ratios.
A higher debt-to-equity ratio indicates the significant role of borrowed capital in a business's operations Typically, when this ratio exceeds 1, it signifies that the company's liabilities surpass its equity, highlighting a reliance on debt financing Conversely, a ratio below 1 suggests that equity is greater than debt.
The higher the self-financing ratio, the higher the financial independence, thus the lower the financial risk of the enterprise
2.1.2 Firm Profitability and its determinants
Business activities are primarily focused on generating profit, and companies continuously seek effective strategies to maximize their yields based on available resources and technical capabilities Profitability reflects a business's capacity to enhance its efficiency, boost cash flow, and optimize resource utilization, ultimately demonstrating its power to generate profits.
Profitability measures the profit a business generates relative to its costs or outputs, indicating overall performance Higher profitability signifies greater profits per unit of price or output, while lower profitability indicates reduced earnings per unit.
Profitability ratios are used to evaluate the company’s ability to generate profit compared to its expense and other costs associated with the generation of income during a particular period
This rate indicates the ability of management to generate a profit from a given level of sales efficiently A high rate shows an improvement in operating efficiency and vice versa
This rate indicates the efficiency of management and use of assets to generate profit for the business
This rate indicates the efficiency of management and use of equity to generate profit for the business
2.1.3 The relationship between capital structure and firm profitability
Miller and Modigliani (1958) posited that in a perfect market, a firm's capital structure does not influence its value, indicating that there is no ideal capital structure for any specific profession However, when considering taxes, a firm's value can increase and its average cost of capital may decrease as its debt ratio rises, suggesting that businesses should maximize their use of debt Nonetheless, the assumptions of a perfect market—such as the absence of transaction costs, taxes, asymmetric information, and a borrowing rate equivalent to the risk-free rate—do not accurately reflect the realities faced by businesses in practice.
The relationship between a business's value and its debt ratio is not straightforward, as increasing debt can lead to financial distress costs that diminish overall benefits When the debt ratio reaches a certain level, these financial distress costs can surpass the advantages of the tax shield, causing the value of the heavily indebted firm to decline Consequently, researchers analyze the impact of capital structure on firm value and performance.
Building on the foundational research of Miller and Modigliani, several structural theories of capital have emerged to elucidate the capital structure of firms These include the trade-off theory, which balances the benefits and costs of debt, the pecking order theory, which prioritizes financing sources based on information asymmetry, and the market timing hypothesis, which suggests that firms time their equity issuance based on market conditions.
The trade-off theory of capital structure posits that companies determine their optimal mix of debt and equity financing by weighing the associated costs and benefits Originating from the work of Kraus and Litzenberger (1973), this theory highlights the balance between the costs of bankruptcy and the tax advantages of debt Initially, financial leverage is positively correlated with profitability; however, this relationship shifts to negative once the costs of financial distress and the benefits from tax shields reach a critical threshold (Deboi et al., 2021).
The trade-off theory faces criticism due to instances where firms with low financial leverage achieve strong business performance, challenging the notion that increased debt is necessary for profit maximization This discrepancy highlights the importance of the pecking order theory, which offers a more nuanced understanding of financing decisions and their impact on business outcomes.
Pecking order theory posits that managers possess greater knowledge about their company's future prospects, risks, and value than external investors, leading to asymmetric information This disparity influences their preferences for financing options, prioritizing internal funding over external sources, and favoring debt issuance over equity Consequently, a specific hierarchy emerges for financing new projects.
Asymmetric information often leads companies to prefer debt over equity, as issuing debt demonstrates the board's confidence in profitable investments and suggests that the current stock price is undervalued Conversely, issuing equity may indicate a lack of confidence in the company's valuation, potentially causing a decline in share price However, this trend is not universal, particularly in high-tech industries where the high cost of debt makes equity financing more attractive due to intangible assets To prevent a drop in stock price, businesses typically follow a financing hierarchy: they first utilize internal capital through retained earnings, then resort to debt, and finally consider issuing new shares This approach suggests that firms may not achieve an optimal capital structure The pecking order theory highlights three key advantages in this financing strategy.
Profitable firms often maintain lower levels of debt because they do not require external funding; their strong internal capital allows them to rely on retained earnings instead This preference for internal financing over debt arises from the pecking order theory, which prioritizes retained earnings as the first choice for funding needs Consequently, less fortunate firms, lacking sufficient internal resources, resort to issuing debt as a secondary option.
Literature Review
Ahmad et al (2012) examined the impact of capital structure on business performance by analyzing the relationship between return on assets (ROA) and return on equity (ROE) with various types of debt, using financial data from 58 industrial and consumer companies listed on the Malaysian stock market between 2005 and 2010 The findings revealed that long-term debt and total debt negatively affected ROA, while short-term debt, long-term debt, and total debt significantly reduced efficiency as indicated by ROE.
Fekaku Agmas (2020) conducted an analysis of the top 30 construction companies in Ethiopia from 2011 to 2015, revealing that a significant positive correlation exists between debt-to-equity and long-term debt to total assets with return on equity (ROE) and return on assets (ROA) Conversely, the study found that the capital structure, when assessed by the debt-to-assets ratio, negatively correlates with ROE and ROA among the sampled construction firms in Ethiopia.
Pal Singh and Mahima Bagga (2019) studied 50 companies listed on India’s NSE and found a significant positive impact of capital structure on profitability
Ullah et al (2015) demonstrated that from 2008 to 2013, the manufacturing sector in Pakistan experienced a significant negative correlation between debt and profit, while the non-manufacturing sector showed a strong positive correlation between these two variables.
Abor (2005) studied the relationship between capital structure and profitability of
A study of 20 firms listed on the NSE of Ghana reveals a positive correlation between short-term debt and operating efficiency, as indicated by the Return on Equity (ROE) ratio Conversely, long-term debt exhibits a negative relationship with operating efficiency, also measured by the ROE ratio Additionally, research by Abor (2005) indicates a positive association between total debt and performance, as reflected in the ROE ratio.
Muhammad (2017) ascertained the impact of capital structure on the profitability of automobile companies in Pakistan The conclusion is that capital structure does have a statistically significant impact on firms' profitability
Le Thi My Phuong (2017) investigated the relationship between capital structure and financial performance during Vietnam's integration period, utilizing audited financial statements from 207 listed manufacturing enterprises between 2010 and 2015 The study revealed a positive correlation between return on equity (ROE) and capital structure Additionally, while firm size and solvency negatively influenced financial performance, the ratio of fixed assets and growth rate exhibited positive effects.
Nguyen Anh Hien (2019) conducted a study on the factors influencing profit quality among companies listed on the Ho Chi Minh Stock Exchange (HOSE), utilizing audited financial statements from 192 enterprises between 2010 and 2018 The sample excluded banks, insurance, and securities companies, ensuring a diverse representation The research employed STATA 14 to analyze the data through conventional regression, random effects model (REM), and fixed effects model (FEM) The findings revealed that profitability, financial leverage, and liquidity significantly affect the quality of profits in these firms.
Tran Thi Bich Ngoc et al (2017) conducted an analysis of 130 joint-stock companies in Thua Thien Hue between 2010 and 2014, revealing that capital structure negatively affects profitability, as measured by ROA, ROE, and EPS Additionally, the study found that a firm's scale has a positive impact on profitability, while growth rate and asset structure also play significant roles in influencing profitability.
Nguyen Hoang Anh et al (2017) conducted a study on the factors influencing the income quality of companies listed on the Ho Chi Minh City Stock Exchange between 2012 and 2016 The research revealed that firms characterized by high growth rates, strong liquidity, and smaller operational scales tend to exhibit better income quality.
12 high foreign ownership ratio would have a high quality of income, while firms with high financial leverage or high asset investment ratio would have low earnings quality
Tran Nguyen Hong Ngoc (2020) conducted a study on real estate firms listed on the HOSE from 2013 to 2020, revealing that capital structure and asset structure negatively impact profitability Conversely, the size and growth rate of these firms positively influence their profitability.
Nguyen Minh Ngoc et al (2016) examined the influence of capital structure on the business performance of 25 real estate firms listed on the Ho Chi Minh City Stock Exchange (HOSE) from 2011 to 2018 Their findings indicate that capital structure negatively affects business performance Furthermore, the study reveals that tangible assets (TANG) positively influence the performance of real estate companies, as demonstrated by the results of the FGLS model.
Recent studies have explored the relationship between capital structure and enterprise efficiency across various countries and timeframes, utilizing the principles of financial leverage and capital structure However, there is a notable lack of research examining this relationship in the context of the COVID-19 pandemic Therefore, it is essential to investigate how capital structure impacts the profitability of real estate companies listed on the Vietnamese stock market from 2018 to 2021, offering a unique perspective and scope compared to existing studies.
DATA AND METHODOLOGY
Research methodology
This research employs various regression methods, including the Pooled OLS model, Fixed Effects Model (FEM), and Random Effects Model (REM), to analyze the impact factors and their significance within the data The Pooled OLS regression model features constant coefficients, allowing researchers to combine all data for an ordinary least squares analysis In contrast, Fixed Effects Models assume that the levels of independent variables remain constant, while the dependent variable varies in response Meanwhile, Random Effects Models incorporate random variation in certain parameters, highlighting the distinction between systematic and unsystematic components of the observed variables (Wooldridge, Jeffrey M, 2002).
In this research model, only one method proves reliable, necessitating specific tests to identify the most meaningful results The F-test is employed to differentiate between Ordinary Least Squares (OLS) and Fixed Effects Model (FEM), while the Hausman test is utilized for selecting between FEM and Random Effects Model (REM) Additionally, regression flaw tests are conducted to enhance the reliability and relevance of the research findings.
Common problems in regression are multicollinearity, heteroskedasticity, and autocorrelation:
Multicollinearity in a multiple regression model occurs when two or more independent variables exhibit strong linear correlations This strong correlation means that these variables do not contribute additional unique information, potentially compromising the model's effectiveness.
14 impossible to determine the separate effect of each independent variable on the dependent variable
Heteroskedasticity can disrupt the probability distribution of T and F statistics in regression models, leading to unreliable confidence intervals for regression coefficients To identify this issue, the Breusch-Pagan test was employed.
Autocorrelation testing is crucial, as it can lead to consistently inflated t-statistics for regression coefficients This issue results in biased variances of estimators, which ultimately undermines the validity of T and F tests, rendering the model's results unreliable.
To diagnose those flaws in the model, some common tests are utilized, including:
The Variance Inflation Factor (VIF) is utilized to assess multicollinearity, while the Breusch-Pagan Lagrangian Multiplier (LM) test identifies heteroskedasticity, and the Wooldridge test detects autocorrelation In instances of multicollinearity, a practical solution involves decomposing the model into sub-models to isolate the problematic variables To address issues of heteroskedasticity and autocorrelation, the author employs the Feasible Generalized Least Squares (FGLS) method for standard error correction.
Inheriting previous researches, the study considers factors that are expected to affect the profitability of enterprises ROE will be regressed on mentioned variables, therefore the result will be discussed:
ROE it = β 0 + β 1 DA it + β 2 SIZE it +β 3 AS it +β 4 AG it + β 5 CR it +u it
Where: i and t: company i in year t uit is the error term β: constant
Table 3: Variables in the regression estimation model
Variables
Return on Equity (ROE) serves as a key measure of a company's profitability and efficiency in generating profits (Fernando, 2021) In this research, ROE is utilized as the primary indicator of profit performance and is designated as the dependent variable.
The capital structure is determined by the debt ratio, which is measured by debt-to- assets:
In the same sector, companies are classified by size, which significantly influences their competitiveness Larger companies enjoy advantages in resources and reputation, making them more formidable in the market Additionally, their high credit scores facilitate easier access to extensive debt borrowing, further enhancing their financial capabilities.
Size is measured by the natural log of the firm’s total assets at the end of the fiscal year
Fixed assets play a crucial role in businesses as they serve as collateral for loans, positively impacting capital structure according to trade-off theory Pecking order theory suggests that a higher ratio of fixed assets facilitates easier borrowing, enhancing trust with creditors and reducing lenders' risk associated with agency costs of debt (Titman & Wessels, 1988) However, some companies with a high fixed asset ratio may experience low business efficiency due to significant investments in fixed assets, which can hinder cash flow and ultimately impede business performance (Zeitun & Tian, 2007).
To sustain significant growth, enterprises must invest substantial capital, which often exceeds the capacity of internal funding sources Consequently, these firms will increasingly rely on external loans to finance their investments.
The pecking order theory indicates a positive correlation between a firm's growth rate and its debt ratio (Culata, 2012) This theory suggests that businesses tend to prioritize debt when seeking external capital Companies that experience high growth rates typically demonstrate strong performance and hold a significant market position, reflecting their ability to generate profits from operations.
A company with strong profitability enjoys enhanced cash flow, which is essential for securing the necessary inputs for production and operations Furthermore, maintaining high liquidity fosters trust among creditors, enabling businesses to access loans that can be leveraged for growth and increased profitability.
Research Data
The analysis focuses on 34 real estate companies listed on the Hanoi Stock Exchange from 2018 to 2021, utilizing secondary data sourced from audited annual financial statements and vietstock.vn, a website dedicated to financial data for Vietnam's stock exchanges A total of 136 observations were collected and analyzed, with the selected data spanning the years 2018 to 2021, a period marked by significant economic challenges due to the COVID-19 recession.
EMPIRICAL RESULTS AND DISCUSSION
Descriptive Statistics
The table shows a general view of both dependent and independent variables for
136 samples of real estate firms
Over a four-year period, the average return on equity (ROE) was 4.485%, with a peak of approximately 29.91% and a low of -22.08%, highlighting significant disparities in profitability among firms.
The average total debt to total assets ratio stands at 53.049%, indicating that over half of the assets are financed through debt In this industry, the highest ratio reaches 86.35%, reflecting the significant leverage employed by some companies Conversely, a few firms maintain a minimal debt presence, with a low ratio of approximately 5.01%.
The asset size of firms (SIZE) is from 5.23 to 12.96, with a mean of 8.34, which is just ordinary compared to other industries
Variable N Minimum Maximum Mean Std.Deviation
Oppositely, the appropriation of assets records an average of 5.916%, while the minimum is 0.03% and the maximum is 60.25%
The medium current ratio stands at 2.13, indicating a strong capacity to meet short-term obligations With a maximum ratio of 9.72 and a minimum of 0.61, this performance reflects positively on the industry's financial health.
The average assets growth (AG) stands at 15.86%, with a standard deviation of 28.14% Notably, the highest recorded growth rate is 135.65%, while the lowest falls at -37.05% This variance is typical, as companies adopt diverse strategies during varying periods, with larger firms demonstrating a quicker recovery following the pandemic.
Correlation Analysis
ROE DA SIZE AG AS CR
The table illustrates the Pearson correlation between various variables, revealing that Return on Equity (ROE), which represents firms' profitability, shows a positive correlation with SIZE, AS, CR, and AG Conversely, Debt to Assets (DA) exhibits a negative correlation.
All of the variable’s coefficients are below 0.8, so there is no multi-collinearity This is a supportive sign to conduct regression analysis with Pool OLS, FEM, and REM
To be more specific, the author uses the variance inflation factor (VIF method) to detect the severity of multicollinearity If the value is greater than 10, there is multicollinearity (Pallant,2016)
Table 4 3: VIF test for multicollinearity between variables
Variable DA SIZE AS CR AG
The results prove that there is no value greater than 10, so there is no multicollinearity.
Regression Results
The study uses the panel data regression method with Pooled OLS, FEM, and REM models
Note: P_value of estimated regressors are presented in parentheses
DA and ROE’s relationship is negative in all three models DA also has P-value smaller than 5% within three models
In the Pooled OLS model, the independent variables account for 17% of the variation in the dependent variable, Return on Equity (ROE), as indicated by an R-squared value of 0.17 However, the variables SIZE and CR lack statistical significance, as their P-values exceed the 5% threshold.
In FEM model, ROE is impacted negatively by DA, SIZE, and CR SIZE, AS, and
CR have no statistical value in this model
In REM model, SIZE, AS, and CR has no statistical significance
The author conducts Fisher's test to test whether the OLS or FEM model is a more suitable model for the study Hypothesis:
H0: There is no difference between the subjects or times
H1: There is a difference between different objects or times
The P-value for the Return on Equity (ROE) variables is 0.0000, which is less than the significance level of 0.05 This indicates that the null hypothesis (H0) is rejected, suggesting that the Fixed Effects Model (FEM) is more appropriate for estimation compared to the Ordinary Least Squares (OLS) model for both variables.
This test is used for choosing a suitable model between FEM and REM Hypothesis:
H0: There is no correlation between the explanatory variables and the random components (the REM model is suitable)
H1: There is a correlation between the explanatory variables and the random elements (the FEM model is suitable)
P-value is greater than 5%, so H1 is rejected REM is chosen in this test
Test Statistical Value P-value Model
The Hausman test indicates that the Random Effects Model (REM) is the appropriate choice for this research, and the subsequent discussion will primarily focus on the regression outcomes derived from the REM analysis.
The result of REM model is also diagnosed with Heteroskedasticity test and autocorrelation test The test resuts are provided in the table 4.6
As shown in the table 4.6, the results reveal the non-existence of autocorrelation and multicorrelation, but prove the emergence of heteroskedasticity
Based on the above model selection test results, the author continues to check if the model has flaws and make corrections
Prob>chibar2=0.0000 < 0.05 At the 5% level of significance, this model has Heteroskedasticity (Table 4.6)
Wooldridge test results show Prob = 0.0204 < 0.05 At the 5% level of significance, this model has no autocorrelation (Table 4.6)
The Collin test results indicate that all variables in the regression model have Variance Inflation Factor (VIF) coefficients below 10, confirming the absence of multicollinearity in the model (Table 4.6).
Table 4 6: Multicollinearity, Heteroskedasticity and Autocorrelation test results
Lagrange Multiplier Prob>chibar2 = 0.0000 Heteroskedasticity Woodridge Prob>F = 0.6177 No Autocorrelation
VIF Mean VIF =1.23