STATE BANK OF VIETNAM MINISTRY OF EDUCATION & TRAINING HO CHI MINH UNIVERSITY OF BANKING ********************* Vo Minh Tin THE IMPACT OF CAPITAL STRUCTURE ON FIRM PERFORMANCE EVIDENCE FROM RETAIL COMP[.]
INTRODUCTION
REASON OF CHOOSING
The relationship between capital structure theory and firm performance has been a focal point of research in corporate finance for decades A company's capital structure indicates its reliance on debt versus equity to finance assets This balance of debt influences managerial behavior and financial decisions, ultimately affecting business performance (Harris & Raviv, 1991) Understanding the connection between capital structure and corporate performance is crucial, as a well-established capital structure can enhance operational efficiency and maximize shareholder wealth, a primary objective for business managers.
A study investigating the influence of capital structure on firm performance utilized data from developed countries Roden and Lewellen (1995) analyzed the capital structure of 48 US firms from 1981 to 1990, revealing a positive correlation between capital structure and firm performance.
Research indicates that profitable firms tend to utilize more debt, a trend that has also been investigated in developing countries Majumdar and Chhibber (1999) explored the connection between capital structure and efficiency, revealing that in India, there is a negative correlation between corporate debt and profitability among firms.
The retail sector in Vietnam has seen remarkable progress in recent years, with the General Statistics Office of Vietnam reporting an impressive average annual growth rate of 10-12% in retail revenue from 2015 to 2020.
A recent survey by Vietnam Report revealed that 53.8% of retail businesses in Vietnam have reached or exceeded their pre-pandemic efficiency levels According to Ngoc Quynh (2023), the retail sector's growth has played a crucial role in the country’s economic recovery amidst global uncertainties Notably, many companies in the Vietnamese retail market have accelerated their digitalization efforts in management, operations, logistics, and distribution as they strive to recover from the impacts of the Covid-19 pandemic.
In 2023, the retail industry is evolving due to shifts in consumer shopping behavior post-pandemic and the integration of digitalization The sector is witnessing transformations in purchasing habits, technology, labor models, and sales channels, leading to innovative business models and unique shopping experiences This evolution makes the retail market increasingly competitive and attractive The author focuses on "The impact of capital structure on firm performance: Evidence from retail companies on the Vietnam stock market" for their graduate thesis, offering policy recommendations aimed at improving the operational efficiency of retail businesses in Vietnam.
THE OBJECTIVE RESEARCH
This thesis aims to examine how capital structure influences the performance of retail companies listed on the Vietnam Stock Market Additionally, it seeks to offer recommendations to enhance the performance of these firms.
The thesis has some following specific objectives to achieve the above overall objective:
Firstly, suggesting the model research on the impact of capital structure on firm performance
Secondly, assessing the impact of capital structure on retail enterprises‘s performance in the Vietnam Stock Market
Thirdly, suggesting related implications to improve firm performance effectively of retail enterprises listed on the Vietnam Stock Market.
RESEARCH QUESTION
To achieve the research objective mentioned above, this thesis is conducted to answer the following research questions in turn:
Which research model is used to analyze the impact of capital structure on the operational efficiency of businesses?
How does the capital structure affect firms' financial performance in retail enterprises in the Vietnam Stock Market?
What are the critical implications of the relationship between capital structure and firm performance for retail enterprises in the Vietnam Stock Market?
OBJECT AND SCOPE OF THE STUDY
The object of the study is the impact of capital structure on firm performance evidence from retail companies on the Vietnamese Stock Market
As of May 2023, Vietnam's stock market features over 37 listed companies, with 15 on the Ho Chi Minh Stock Exchange (HOSE), seven on the Hanoi Stock Exchange (HNX), and 15 on the UPCOM market This study focuses on 25 retail companies selected for their market capitalization and financial performance, ensuring a comprehensive representation of the retail sector's size, influence, and robust financial metrics.
The research spans from 2015 to 2022, chosen to provide a comprehensive overview of Vietnam's retail sector, which has seen explosive growth during this period, according to the General Statistics Office of Vietnam This timeframe also captures the significant impact of the COVID-19 pandemic on the economy from 2019 to 2022, making it relevant as the research begins in early 2023.
METHODOLOGY
The thesis employs quantitative research methods such as Pooled OLS, Fixed Effects Model (FEM), and Random Effects Model (REM) to assess the impact of capital structure on firm performance The selection of the most suitable model is based on the results obtained, followed by tests to verify model accuracy To tackle issues of heteroscedasticity and autocorrelation, the Generalized Synthetic Control (FGSL) method is applied Additionally, when faced with heteroscedasticity, autocorrelation, and endogeneity simultaneously, the study utilizes the System-GMM (S-GMM) model to enhance estimation accuracy and address model inaccuracies.
CONTRIBUTION OF THE THESIS
The research offers empirical insights into the connection between capital structure and firm performance, making it a valuable reference for understanding how capital structure influences the efficiency of business operations.
Research on the link between capital structure and firm performance in the retail sector reveals key recommendations for businesses To optimize capital resources and improve operational efficiency, companies should implement strategic financial planning and consider the impact of their capital structure on overall performance.
THESIS STRUCTURE
Besides the Introduction, Acknowledgments, an abstract of research papers, references and appendices, and tables illustrating the content, the research paper is presented in 5 chapters:
Chapter 1 provides an introduction to the research topic and highlights the importance of the study by setting out the main research objectives through research questions and presenting the research methodology used This chapter also emphasizes the contributions of the study to both practical and academic domains.
THEORETICAL FRAMEWORK AND LITERATURE REVIEW
THEORETICAL FOUNDATIONS OF BUSINESS PERFORMANCE
Productive efficiency, as defined by Samuelson and Nordhaus (1985), is achieved when an economy cannot increase the output of one good without decreasing the output of another, indicating that it operates at its production frontier This concept highlights the importance of optimizing resource allocation to maximize production efficiency within the economy.
Economist Adam Smith (1776) defines efficiency in economic activity as the revenue generated from the consumption of goods This implies that efficiency is closely linked to business performance indicators, especially when considering the rising costs associated with expanding production resources Therefore, a business can still be deemed efficient if it achieves the same outcome at varying prices.
The performance of a business is indicative of how effectively it utilizes resources such as labor, equipment, and capital to achieve its objectives Essentially, it reflects the relationship between the business outcomes and the costs incurred to attain those results A greater disparity between these two elements signifies higher efficiency in the firm's operations.
Demsetz and Lehn (1985) evaluate enterprise performance through a combination of Return on Assets (ROA), Return on Equity (ROE), and Tobin's Q Similarly, Shleifer and Vishny (1988) utilize Tobin's Q, highlighting that this index indicates the market value of stocks and serves as a measure of the market's evaluation of a firm's performance.
Tobin's Q is a financial ratio that assesses the relationship between a company's market value and its replacement value, named after economist James Tobin, who introduced the concept in his influential 1969 research on investment theory.
Tobin's Q is a crucial metric in empirical research, frequently utilized to examine firm performance aspects like investment choices, capital structure, and mergers and acquisitions It offers essential insights into the interplay between market value and asset value, aiding investors, managers, and researchers in comprehending firm performance and market dynamics more effectively.
Research examining the relationship between capital structure and firm performance has predominantly used Tobin's Q ratio for analysis across diverse sectors and countries Notable studies include Berger and Bonaccorsi di Patti (2006), who analyzed the banking industry in Italy, and Erol and Nabi (2016), who focused on manufacturing firms in Turkey Furthermore, Saeed et al (2017) and Raza et al (2018) explored financial firms and publicly listed companies within the Pakistani stock market.
According to Nguyen Thi Canh (2009), Return on Assets (ROA) is a key financial metric that informs investors about the after-tax profits generated from total assets, indicating the profit earned per dollar of investment This metric reflects the efficiency of a company in converting its debt and equity capital into profit A higher ROA signifies that a company is effectively generating more income with less investment, highlighting its operational efficiency and financial health.
Recent studies have focused on the impact of capital structure on firm performance in emerging economies and different countries For example, Smith et al
Recent studies by Al-Tamimi et al (2018) and 2019 explored the relationship between capital structure and firm performance in European nations and Gulf Cooperation Council countries, respectively Similarly, Li et al (2017) focused on Chinese listed companies to analyze how capital structure influences firm performance All these studies employed Return on Assets (ROA) as the key metric for assessing firm performance.
Return on Equity (ROE) is a key financial metric that indicates the profitability of owners' investments, as noted by Nguyen Thi Canh (2009) This ratio is frequently compared to the Bank's deposit rate, which serves as an opportunity cost for investors ROE effectively measures the profit generated per dollar invested, allowing investors to assess and compare companies within the same industry A higher ROE suggests more effective management in utilizing shareholders' capital, making it a crucial criterion for evaluating potential stock investments.
Recent studies highlight the significance of Return on Equity (ROE) in evaluating the relationship between capital structure and operational efficiency across various industries Aivazian et al (2017) found a negative impact of capital structure on ROE in publicly listed companies within the Russian stock market Similarly, Sharma and Goyal (2019) reported that capital structure adversely affected ROE in Indian manufacturing companies Additionally, Nnadi et al (2020) discovered a negative correlation between capital structure and ROE in Nigeria's oil and gas sector, indicating that capital structure significantly influences the financial performance of these businesses.
From the criteria that the author mentioned before, the author uses the ROA indicator to measure corporate performance for several reasons:
ROA, or Return on Assets, evaluates the profitability of all business assets, providing a broader perspective than ROE, which focuses solely on equity capital Unlike ROE, which can fluctuate with changes in external financing or debt, ROA offers a clearer picture of a company's operational efficiency In contrast, Tobin's Q assesses the relationship between a firm's market value and its replacement value, indicating its potential to create shareholder value, though its calculation is complex and requires detailed market data Conversely, ROA relies on easily accessible financial information, emphasizing asset profitability and efficient resource utilization, thus reflecting effective operational management.
THE CONCEPT OF CAPITAL STRUCTURE
Capital structure is interpreted in various ways by researchers globally Myers (1984) defines it as the selection between debt, equity, and hybrid securities for financing a company's operations, while Abor (2005) describes it as a mix of different types of securities Additionally, Tran Hoang Tho et al (2007) contribute to this understanding, highlighting the complexity of capital structure in financial decision-making.
Capital structure refers to the blend of short-term and long-term financing options, including debt and equity, that a firm employs to support its investment decisions According to 2007, it encompasses short-term debt, long-term debt, preferred shares, and common shares Gill et al (2011) emphasized that it represents the mix of debt and equity capital utilized in business operations Additionally, Nirajini and Priya (2013) described capital structure as the combination of long-term capital, such as common and preferred shares along with bank loans, and short-term debt, including trade credits and accounts payable.
Capital structure refers to the strategic arrangement of various funding sources a company utilizes to finance its operations and investments This involves the careful selection and combination of debt, equity, and other securities to maximize financial advantages and align with the firm's strategic goals.
Effective management of capital structure is essential for a business's financial health, as a well-optimized capital structure boosts profitability, strengthens debt repayment ability, and fosters growth opportunities In contrast, a poorly balanced capital structure can lead to financial risks, restrict investment potential, and jeopardize business stability.
2.2.2 Indicators proxy for capital structure
Capital structure refers to the mix of debt and equity that a company utilizes to finance its assets This study focuses on capital structure as the primary variable, particularly in relation to leverage ratios, as highlighted by Nguyen Thi Canh.
In the literature, several metrics are used to assess capital structure, including the debt ratio, which compares total debt to total assets, the short-term debt ratio that evaluates short-term debt against total assets, and the long-term debt ratio that measures long-term debt in relation to total assets.
The debt ratio measures the proportion of a company's assets that are financed through debt, reflecting its ability to meet financial obligations A lower debt ratio indicates stronger security for creditors in the event of bankruptcy, while a higher ratio suggests a diminished capacity for the business to repay its debts.
Numerous studies have explored the connection between capital structure and business efficiency, often using the Debt ratio as a key analytical measure For instance, Huang and Song (2018) analyzed 1,445 Chinese companies and found that high debt levels negatively affect financial efficiency, reducing profitability and earning capacity Similarly, Antoniou et al (2008) examined 1,324 European companies and reported that the Debt ratio adversely impacts business efficiency, especially in competitive industries These findings underscore the crucial role of a company's debt levels in influencing its overall performance and financial results, reinforcing the importance of the Debt ratio in understanding capital structure and its effects on business operations.
The short-term debt ratio measures the percentage of a company's total assets that is financed by short-term debt, providing insight into the enterprise's debt situation A high short-term debt ratio may indicate a greater risk of insolvency, while a low ratio suggests a more sustainable financial position This metric is essential for assessing the overall financial health and sustainability of a business.
Several recent studies have investigated the relationship between capital structure and firm performance using the Short-term Debt ratio One such study conducted by
Bancel and Mittoo (2017) identified that a higher Short-term Debt ratio negatively impacts financial efficiency in Canadian-listed companies, particularly affecting small firms and those in volatile industries Similarly, Ali et al (2019) found that in Pakistan, the Short-term Debt ratio adversely influenced the performance of larger companies in competitive sectors These studies indicate that an overreliance on short-term debt can significantly impair a company's economic performance and profitability.
Khan et al (2020) investigated the link between capital structure and financial performance in Malaysia, revealing that a higher Short-term Debt ratio negatively affects economic efficiency, especially in larger firms and volatile industries This indicates that excessive short-term debt may jeopardize a company's financial stability and operational effectiveness Overall, their findings underscore the importance of the Short-term Debt ratio in assessing the relationship between capital structure and firm performance.
The long-term debt ratio measures the proportion of a company's total assets that is financed by long-term debt, highlighting its reliance on long-term borrowing Unlike short-term debt, which requires more immediate repayment and poses greater risk, long-term debt is typically less concerning for businesses due to its extended repayment period.
Recent studies have explored the connection between capital structure and business performance, emphasizing the Long-term Debt ratio as a key indicator These investigations highlight how long-term debt influences business efficiency, with notable research by Chen et al (2017) analyzing listed companies to understand this relationship.
Research in China indicates that a high Long-term Debt ratio negatively impacts business performance, limiting financial flexibility and investment capacity Similarly, a study by Ahmad et al (2019) on listed companies in Malaysia found a negative correlation between Long-term Debt ratios and business performance, particularly in larger firms within competitive industries This research highlights the risks of elevated long-term debt levels, such as diminished risk management abilities and greater reliance on borrowing, further supported by findings from Li et al.
A study conducted in 2021 on listed companies in China revealed a negative impact of the Long-term Debt ratio on business performance, especially in highly competitive industries It emphasized the necessity of managing long-term capital structure to enhance overall business efficiency The findings highlight the critical role of the Long-term Debt ratio in evaluating the connection between capital structure and business performance, illustrating the potential disadvantages of excessive long-term debt, such as diminished financial flexibility, decreased investment capacity, and compromised risk management.
THEORETICAL BACKGROUND
The connection between capital structure and business performance has garnered considerable interest in finance, with theories like Modigliani and Miller, trade-off, and pecking order providing insights into how capital structure influences business efficiency.
The Modigliani and Miller (1958) theory, commonly known as the MM theory, posits that a firm's capital structure does not significantly impact its overall value This theory asserts that the value of an enterprise is not influenced by the proportion of equity or debt it utilizes; rather, it is determined by property rights Consequently, any mix of debt and equity will not correlate with the firm's value.
The Modigliani-Miller (MM) theory, introduced by Modigliani and Miller in 1958, asserts that in a tax-free environment, a firm's capital structure is irrelevant, meaning that the overall value of a firm is dictated solely by the profitability and risk associated with its assets In such perfect capital markets, the method of financing—whether through debt or equity—does not influence the firm's value.
No transaction costs: Buying and selling stocks and borrowing do not incur any fees
No bankruptcy costs: There are no costs associated with financial distress or bankruptcy for a firm
Symmetric information: All investors and firms have the same information and evaluate risks similarly
Equal tax rates: Tax rates are the same for interest and dividend income
Unlimited borrowing capacity: Firms can borrow at similar interest rates and face no limitations on borrowing
No market imperfections: No price fluctuations or market factors influence the value of assets and capital
The Modigliani-Miller (MM) theory posits that in perfect capital markets—characterized by the absence of transaction costs, bankruptcy costs, and information asymmetry—investors can borrow and lend at identical rates, and all firms face the same cost of capital In this ideal scenario, rational investors and managers focused on maximizing shareholder value lead to the conclusion that a firm's value remains unaffected by its capital structure's debt-equity mix in a tax-free environment.
Modigliani and Miller (1963) highlighted the significance of tax advantages associated with debt in enhancing firm value within a taxable environment They posited that the deductibility of interest payments on debt creates tax shields, which lower the firm's overall tax burden and subsequently increase its value.
MM theory suggests that the optimal capital structure involves maximizing the use of debt to take advantage of the tax benefits This view of Modigliani and Miller is controversial
A study by Stiglitz (1974) challenged the notion of no bankruptcy costs and uniform profit cycles among firms Ward (1999) analyzed the capital structure decisions of companies in France, Germany, Japan, the United Kingdom, and the United States, revealing that although average leverage ratios were similar across these nations, the firms' capital structure choices differed This divergence was linked to variations in tax policies, agency costs, and the asymmetric information that exists between shareholders and debt holders.
While the Modigliani and Miller theory may not completely align with real-world scenarios, it is still crucial as it established a foundational framework that has influenced further advancements in modern financial economics.
The trade-off theory posits that firms must balance the advantages and disadvantages of debt financing when establishing their capital structure This theory suggests that an optimal debt level can enhance a firm's value by leveraging tax benefits while also accounting for the risks of financial distress and bankruptcy As noted by Myers, increased debt usage can amplify tax shield advantages, but this comes with the trade-off of elevated financial distress costs.
According to Graham (2003), debt financing offers tax advantages by allowing interest deductions, which can lower a company's tax burden and enhance its overall value Nonetheless, as debt levels rise, the associated risks of financial distress and bankruptcy increase, leading to costs like elevated interest rates, restricted credit access, and lost business opportunities.
The trade-off theory highlights the need to weigh the benefits and costs of debt financing when establishing a firm's optimal capital structure It acknowledges that there is no universal solution, emphasizing the importance of evaluating the balance between tax benefits and financial risks to enhance the firm's overall value.
The pecking order theory, proposed by Myers and Majluf in 1984, suggests that leverage improves market perceptions of a firm's value, ultimately enhancing overall firm value (Abdullah and Tursoy, 2019) This theory outlines how companies prioritize their capital utilization by selecting various financing sources in a specific order.
The pecking order theory posits that firms prioritize internal funding sources, such as retained earnings and operational cash flows, before seeking external financing options like debt This preference arises because internal funds eliminate transaction costs and the risks associated with external borrowing, allowing firms to maintain greater flexibility and control over their financial activities Myers (1984) articulated the core principles of this theory, emphasizing the importance of utilizing internal resources first.
Internal Funding: Businesses prioritize using accumulated profits or internal cash flows to meet their capital requirements
Short-Term Debt: If the business lacks sufficient cash from internal sources, it may opt for short-term borrowings, such as short-term bank loans or credit lines
Long-Term Debt: If the capital needs are still unmet, the business may explore long-term borrowing options, including issuing corporate bonds or obtaining long-term bank loans
Preferred Stock: In cases where other funding sources are inadequate, the business may consider issuing preferred stock to existing shareholders or external investors
Common Stock: As a last resort, the business may consider issuing common stock to existing shareholders or the general public when other funding options are not viable or available
Numerous empirical studies validate the pecking order theory, particularly in the context of privately held non-listed firms in Brazil, where Zeidan et al (2018) found that over 50% of owners favored internal funding over alternative financing options, including subsidized loans This indicates that the pecking order theory is relevant for small and medium-sized enterprises in Brazil Additionally, Allini et al (2018) examined 106 listed companies on the EGX stock exchange from 2013-2014, revealing that firms with strong profitability also prioritize internal funds over external financing sources.
Agency cost theory, as developed by Jensen and Meckling (1976) and others, highlights the conflicts of interest between stakeholders, such as principals and agents, leading to agency costs that can impact business value These costs are categorized into agency costs of equity, arising from conflicts between shareholders and managers, and agency costs of debt, stemming from conflicts between shareholders and bondholders The agency cost of equity suggests that managers may prioritize personal goals over shareholder interests, while high leverage can pressure managers to focus on profitable investments to ensure cash flow for interest payments Research indicates that increased debt can lower owners' agency costs by aligning managerial actions with shareholder interests, thereby potentially enhancing firm value However, excessive debt can also elevate the agency cost of debt, negatively affecting overall firm value.
In 1977, it was proposed that lenders typically require higher interest rates to offset the risks linked to high leverage Consequently, agency theory posits a strong correlation between a firm's capital structure and its performance.
EMPIRICAL EVIDENCE ON THE IMPACT OF CAPITAL STRUCTURE
The relationship between capital structure and firm performance is a crucial topic in financial management that has garnered significant attention from researchers and experts This relationship plays a vital role in the development and success of a business organization, leading to two distinct perspectives on the matter.
Research consistently indicates a positive correlation between capital structure and firm performance Nasimi's (2016) study of London Stock Exchange-listed companies reveals that an effective capital structure enhances business performance by optimizing debt utilization, providing tax benefits, and facilitating investment and expansion Additional empirical studies in developed countries, including those by Abor (2005), Berger and Di Patti (2006), Gill et al (2011), and Riaz et al (2021), further support the notion that capital structure positively influences firm performance, underscoring the significance of maintaining an optimal capital structure for achieving business efficiency.
Research indicates a negative correlation between financial leverage and firm performance, as highlighted by studies from Javed et al (2014), Chadha and Sharma (2016), Le and Phan (2017), and Vieira et al (2019) Excessive financial leverage can surpass a firm's debt repayment capacity, resulting in financial strain, decreased efficiency, and heightened bankruptcy risk Consequently, it is essential for firms to meticulously assess the costs and risks linked to their capital structure to maintain balance and optimize business operations.
Research on the link between capital structure and firm performance has gained global interest, leading to numerous studies across different countries These investigations focus on how a company's capital structure affects its operational efficiency By analyzing the findings and methodologies from these studies, we can gain a deeper understanding of the international dynamics between capital structure and firm performance.
Kajananthan and Nimalthasan (2013) studied the relationship between capital structure and performance of 25 listed manufacturing companies in Sri Lanka from
From 2008 to 2012, the author employed descriptive statistics and regression analysis to explore the relationships among various financial metrics, including the debt-to-assets ratio, debt-to-equity ratio, gross profit, net profit, return on equity (ROE), and return on assets (ROA) The findings indicated that the debt-to-equity ratio had a negative effect on gross profit ratio (GPR), net profit ratio (NPR), ROE, and ROA, while the debt-to-assets ratio positively influenced GPR, NPR, and ROE but adversely impacted ROA The author advised businesses to make prudent financial choices, minimize excessive financial leverage, prioritize retained earnings for investments, and consider leverage as a last resort However, the study's limited scope, utilizing only two independent variables against multiple dependent variables, raised concerns about omitted variable bias, compounded by a small sample size of just 100 observations.
In a study conducted by Sorana (2015), the relationship between capital structure and the performance of 196 manufacturing companies listed on the Bucharest Stock Exchange was analyzed over eight years, from 2003 to 2010 The research utilized descriptive statistics, correlation analysis, and regression analysis, employing the least squares method, fixed effect model, and random effect model Key performance indicators, Return on Assets (ROA) and Return on Equity (ROE), were used to assess enterprise efficiency, alongside the Debt Ratio as a critical variable.
The capital structure of an enterprise is influenced by the Short-term Debt Ratio, Long-term Debt Ratio to Total Assets, and Possible Equity Ratio, with fixed assets over total assets serving as a control variable Findings indicate that total debt, short-term debt, and a high fixed asset ratio negatively impact Return on Assets (ROA) and Return on Equity (ROE), while long-term debt is rarely utilized by companies Additionally, the study highlights the limited use of independent variables amidst the significant effects of high taxes and inflation on enterprise performance in Romania.
Dawar (2014) explored the effects of capital structure on firm performance in
A panel data regression analysis utilizing a fixed effects model was conducted on data from 78 firms across various economic sectors in India, excluding financial institutions, that were listed on the Bombay Stock Exchange from 2003 to 2012 The findings indicate that capital structure has a negative impact on firm performance, as measured by Return on Assets (ROA) and Return on Equity (ROE).
A study by Riaz et al (2021) examined the influence of debt on the behavior of 167 manufacturing firms in G7 countries from 2007 to 2018 Utilizing GMM regression, the research found a significant positive relationship between profitability and the capital structure of these enterprises, irrespective of the type of debt—total, short-term, or long-term.
Saeedi and Mahmoodi (2011) investigated the link between capital structure and the performance of 320 firms on the Tehran Stock Exchange from 2002 to 2009 Utilizing four performance metrics—return on assets (ROA), return on equity (ROE), earnings per share (EPS), and Tobin's Q—and three capital structure indicators—long-term debt, short-term debt, and total debt to total assets, the study revealed that EPS and Tobin's Q had a significantly positive correlation with capital structure Conversely, a negative relationship was identified between capital structure and ROA, while no significant connection was found between ROE and capital structure.
Azeedz et al (2015) investigated the impact of financial leverage on the performance of 200 companies listed on the US stock exchange over a decade, spanning from 2003 to 2012 Their study analyzed three distinct periods: the pre-crisis phase (2003-2006), the crisis period (2007-2008), and the post-crisis phase (2009-2012).
Research from 2012 revealed a negative correlation between financial leverage, measured by the debt-to-equity ratio, and return on assets (ROA) The analysis focused on two distinct periods: before the economic crisis (2003–2006) and after it (2009–2012) Findings indicated that a 1% increase in the debt-to-equity ratio led to a decrease in return on equity (ROE) of 0.362% prior to the crisis, while this decline intensified to 1.13% in the aftermath of the crisis.
Salim and Yadav (2012) investigated the effects of capital structure, indicated by short-term, long-term, and total debt-to-asset ratios, on the operating performance of 237 Malaysian companies listed on the Bursa Malaysia Stock Exchange from 1995 to 2011 Their findings revealed a negative correlation between business performance, as measured by ROA, ROE, and EPS, and capital structure Conversely, they identified a significant positive relationship between Tobin's Q ratio and both short-term and long-term debt ratios.
Chadha and Sharma (2016) analyzed the effect of capital structure on the financial performance of 422 manufacturing firms listed on the Bombay Stock Exchange from 2003 to 2013 Their study utilized return on assets (ROA), return on equity (ROE), and Tobin's Q as indicators of financial performance The findings revealed that financial leverage did not influence ROA and Tobin's Q, but it had a significant negative correlation with return on equity (ROE).
A study by Vieira et al (2019) analyzed the corporate performance of 37 non-financial companies in Portugal from 2010 to 2015 using GMM regression The research found that structural discovery research capital significantly negatively impacted corporate performance as measured by Tobin's Q However, this negative effect was not significant when evaluating corporate performance through the ROA and ROE ratios.
THE RESEARCH GAPS
Recent empirical studies in Vietnam indicate a wealth of research on the negative relationship between capital structure and corporate performance (Le and Phan, 2017; Nguyen and Nguyen, 2015) While existing studies highlight the adverse effects of financial leverage on firms in sectors such as food, healthcare, public utilities, and industry, there is a notable gap in research concerning the retail sector, which has been rapidly growing in Vietnam This study aims to evaluate the impact of capital structure on the performance of retail enterprises in Vietnam.
This chapter explores essential concepts of corporate performance and the theoretical foundations of capital structure, highlighting research from both domestic and international scholars It emphasizes that various factors, including company size, revenue growth rate, liquidity, and macroeconomic elements like economic growth and inflation, significantly influence business performance This foundational understanding sets the stage for the author's continued exploration in the subsequent chapter.
METHODOLOGY
METHODOLOGY
This study employs quantitative methods to examine the influence of independent variables on a dependent variable, utilizing Ordinary Least Squares (OLS) regression alongside Fixed Effects Model (FEM) and Random Effects Model (REM) approaches To tackle potential regression issues such as multicollinearity, autocorrelation, heteroscedasticity, and endogeneity, the research also incorporates Feasible Generalized Least Squares (FGLS) regression Furthermore, the System Generalized Method of Moments (S-GMM) model is applied to effectively address endogeneity among the independent variables.
The article presents an overview of the statistical analysis, detailing the number of observations, mean value, maximum and minimum values, as well as the standard deviation of various variables The author provides a summary of these statistics and offers general insights based on the findings, highlighting key trends and patterns observed in the data.
The correlation coefficient matrix analysis is used to identify multicollinearity among model variables If the correlation coefficients are below approximately 0.8, it suggests a lack of pairwise correlation However, this method can be misleading, as small correlation coefficients may still indicate the presence of multicollinearity To enhance accuracy, it is advisable to utilize the variance-inflating factor (VIF).
Regression analysis quantifies the impact of independent variables on a dependent variable, allowing for an understanding of both the direction and magnitude of this influence According to Baltagi (2005), the general form of panel data regression is outlined as follows.
Trong đó: i = 1, 2, , N represent the ith business; t = 1, 2, , T represent the time periods;
Yit represents the dependent variable of the ith business at time t;
Xit represents the value of X for the ith business at time t; Βit represents the slope coefficient; àit represents the random error of the ith company at time t
Gujarati (2011) offers many panel data regression models; the models used in this study: Pooled OLS, FEM, and REM
The Pool OLS model is a straightforward regression approach that overlooks temporal and spatial factors in data, resulting in constant coefficients across time and enterprises A significant limitation of this model is its susceptibility to autocorrelation, often indicated by a low Durbin-Watson coefficient (Gujarati and Porter, 2009) In this context, 'i' represents the ith cross-unit, 't' denotes time t, 'α' is the slope constant, and 'àit' signifies the random error.
In the FEM model, we recognize that the slope of origin is unique to each firm, remaining constant over time While the source may vary among enterprises, the foundational aspects do not change The differing angles for each firm can be attributed to their distinct characteristics, including management style (Gujarati and Porter, 2009; Gujarati, 2011).
The FEM model is presented as follows: i :: ith cross-unit, t: time t; àit: Random error
In the REM model, β1i is assumed to be a random variable with mean β1 Firm- to-business differences can be attributed to random errors (Gujarati, 2011)
The REM model is presented as follows:
In there, i is the random noise term with mean 0 and variance σ 2
Substituting the above formula, we have the following equation:
: Error component of the cross-unit;
The combined error component of the cross-unit and time series
Feasible Generalized Least Square – FGLS
The Feasible Generalized Least Squares (FGLS) model is employed to address misestimations in variable method structures, particularly in authoritarian feedback models where objects may be misrepresented due to automatic changes or similarities This method, which builds upon Ordinary Least Squares (OLS), assumes that all cases exhibit errors and corresponding changes By calculating the variance matrix and the covariance of these errors, the FGLS method effectively estimates parameter values and transforms the original variables within the model.
System Generalized Model of Moments (S-GMM)
The study uses S-GMM to solve the problem of endogeneity and autocorrelation
It can compare the results with FGLS to have a solid research model on capital structure affecting the performance of selling enterprises odd
According to Nguyen Thi Doan Trang (2019), the estimation of the GMM method is used in the following cases:
Panel data with many observations while having few datums (large N, small T)
There is a linear relationship between the dependent variable and the explanatory variable
The problem of variable variance or autocorrelation in idiosyncratic disturbances
Independent variables can be correlated with residuals (current or previous)
Variable variance and autocorrelation exist within each subject (but not between subjects)
SGMM addresses the endogeneity issue of certain explanatory variables by employing instrumental variables Hansen's test is utilized to assess the correlation between the instrumental variable and the model's residuals, ensuring the validity of the instruments used.
H0: the instrumental variables are consistent, and no endogenous phenomenon occurs Accepting the hypothesis Ho (P-value >10%) means that the instrumental variables used in the model are appropriate
The study employs the second-order correlation test (AR2) to evaluate the second-order correlation of model residuals, testing the null hypothesis (Ho) that no second-order correlation exists If the p-value exceeds 10%, the hypothesis Ho is accepted, indicating that the model residuals do not exhibit second-order correlation, thereby confirming that the model meets the necessary requirements.
Tests for selecting and correcting model defects
Check the pairwise correlation between variables (additive polylinear test)
Gujarati and Porter (2009) used the variance-inflating factor (VIF) to detect multicollinearity
If the correlation coefficient is close to 1, the larger the VIF is, the phenomenon of multicollinearity occurs
In case there is no multicollinearity between variables, then VIF = 1
Gujarati (2011) proposes two hypotheses when testing correlation recommender: H0: no autocorrelation
The author uses the Wooldridge test for the automatic correlation test If p-value
< significance level, reject hypothesis HO; if p-value > significance level, accept hypothesis Ho concluding that there is no autocorrelation phenomenon
Gujarati and Porter (2009) performed the Hausman test to choose to use two models, FEM and REM Two hypotheses are put forward:
H0: There is no correlation between the error component of the cross-unit and the explanatory variable
H1: There is a correlation between the error component of the cross-unit and the explanatory variable
If p-value < significance level, reject hypothesis H 0 ; the FEM model is suitable If p-value > significance level, accept hypothesis H 0 suitable REM model
When variable variance is present, the Ordinary Least Squares (OLS) estimates of the coefficients remain unbiased; however, the variance and covariance of these estimated coefficients are biased To address this issue, White (1980) introduced a robust standard error method that retains the OLS coefficient estimates while re-estimating their variance Implementing this approach eliminates heteroskedasticity from the analysis.
The Hansen test was used to check whether the dummy instrumental variables used in the S-GMM (Systematic Generalized Model of Moments) model fit the model with the theory
H0: Instrumental variables are appropriate (significantly over-determined)
H1: Instrumental variables are not suitable (unworthily over-determined)
When the p-value exceeds 10%, we accept the null hypothesis (HO), indicating that the instrumental variables utilized in the model are appropriate Conversely, if the p-value is less than 10%, we reject HO in favor of the alternative hypothesis (H1), suggesting that the instrumental variables are unsuitable for the model.
The Sargan test is used to test that the instrumental variables used in the S-GMM model are exogenous, the hypothesis
H0: Variable tools are exogenous variables
H1: Instrumental variables are not exogenous
In statistical analysis, if the p-value exceeds 10%, we accept the null hypothesis (H0), indicating that the instrumental variables in the model are exogenous Conversely, a p-value below 10% leads to the rejection of H0 and the acceptance of the alternative hypothesis (H1), suggesting that the instrumental variable is not exogenous.
The second-order autocorrelation test (AR2) is to test the quadratic correlation of residuals in the model with the following hypothesis:
H0: No quadratic correlation of residuals
H1: There is a quadratic correlation between the residuals
When the P-value is greater than 10%, we accept the null hypothesis (HO), indicating that the model's residuals do not exhibit second-order autocorrelation, thus confirming the model's suitability Conversely, if the P-value is less than 10%, we reject the null hypothesis, suggesting that the model's residuals display second-order autocorrelations, indicating that the model is not a good fit.
This thesis outlines a research model to analyze the impact of capital structure on the performance of 25 retail businesses from 2015 to 2022, following a structured methodology.
Step 1: Review the existing theoretical and empirical studies in Vietnam and abroad and discuss previous research to identify research gaps and determine the direction for designing the research model
Step 2: Based on the theoretical foundation and empirical evidence, The thesis designs the research model, forecasts the regression equation, explains the variables, and formulates the research hypotheses
Summary of the theoretical framework and empirical evidence
Determining the research sample, processing research data
Analyze regression results, discuss research results
Step 3: Determine a suitable research sample for the research objectives, subjects, and scope Then, collect and process data according to the research model
Step 4: Determine the methodology with specific analysis and estimation techniques: descriptive statistics, correlation analysis, and regression analysis of panel data using OLS, FEM, and REM methods
Step 5: Test the research hypotheses, using either the F-test or t-test at significance levels of 1%, 5%, and 10%, to determine the statistical significance of independent variables in explaining the dependent variable Additionally, compare the Pooled OLS and REM models using an F-test with the null hypothesis H o : Choose the Pooled OLS model Use the Hausman test to compare the FEM and REM models with the null hypothesis HO: Choose the REM model, and select the most suitable model based on the test results
Step 6: Conduct the model diagnostics, including tests for multicollinearity, autocorrelation, and heteroscedasticity If these issues are not present, proceed to Step
RESEARCH MODEL
Recent empirical studies indicate that firm performance is significantly impacted by factors beyond capital structure, with liquidity, asset size, economic growth, and utilization also playing crucial roles This research employs a regression model to analyze these influences comprehensively.
The general framework investigates the influence of capital structure on firm performance in a linear form, drawing on the research conducted by Sheikh and Wang
Performance it : The measurement of the business performance of firm i at time t, as measured by the variable ROA it
Capital structure it : The capital structure of firm i at time t, denoted as Capital structureit, is measured by the Debt ratio (Total liabilities / Total Assets)
The control variable j for firm i at time t is assessed through several key indicators, including liquidity (LIQ it), firm size (Size it), business growth (Grow it), tangible assets (Tang it), economic growth (GDP it), and the inflation rate (INF it).
3.2.1 Explain the variables and develop the thesis hypothesis
This study aims to investigate the impact of capital structure on firm performance, focusing on the return on assets (ROA) as the key dependent variable ROA, which is calculated by dividing after-tax income by total assets, serves as a crucial metric for assessing and comparing firm performance, particularly for those with low return on equity (ROE) due to leverage and high equity Empirical studies, including those by Sheikh and Wang (2013) and Hasan et al (2014), highlight the significance of ROA in reflecting management's efficiency in generating profits and managing resources effectively Notably, ROA remains unaffected by high equity levels, as indicated by Rivard and Thomas (1997) Consequently, this research exclusively employs the ROA ratio to evaluate enterprise performance.
Capital structure refers to the mix of debt and equity a company utilizes to finance its assets (Geske et al., 2016) In this study, capital structure, particularly the leverage ratio, serves as the main explanatory variable, measured by the ratio of total debt to total assets.
The relationship between capital structure and firm performance has been extensively studied in finance, yielding varied and sometimes contradictory findings Research indicates a positive correlation between capital structure and performance in developed countries Notable analyses by Berger and Di Patti (2006), Gill et al (2011), Margaritis and Psillaki (2010), and Abdullah and Tursoy (2019) in the US, France, and Germany demonstrate that higher debt ratios are linked to improved firm performance This is attributed to the fact that increased debt can minimize agency costs of equity and incentivize managers to align their actions more closely with shareholder interests.
Numerous studies indicate a negative correlation between capital structure and firm performance in emerging markets and developing countries Research conducted by Chadha and Sharma (2016), Chaleeda et al (2019), and Ullah et al (2020) in India and Pakistan, along with findings from Vietnam by Le and Phan (2017) and Nguyen and Nguyen (2015), highlight that excessive debt can lead to significant financial challenges These challenges include difficulties in debt repayment and increased financial risk, which ultimately impede effective business management Companies burdened with high debt levels often experience financial strain and elevated capital costs, resulting in diminished operational efficiency.
Le and Phan (2017) and Nguyen and Nguyen (2015) recognize that capital structure negatively impacts the performance of Vietnamese enterprises, which aligns with findings in prior literature Based on this, the author proposes the following hypothesis:
H1: Capital structure is negatively related to firm performance
The liquidity ratio (LIQ) is a key financial metric that compares current assets to short-term liabilities, indicating a company's ability to meet its short-term financial obligations, such as accounts payable and short-term loans A higher liquidity ratio signifies stronger liquidity, enhancing the firm's capacity to settle short-term debts Conversely, a declining current ratio suggests a liquidity shortfall, often resulting from financing fixed assets with short-term debt Such liquidity deficits can adversely impact corporate performance and, ultimately, profitability.
The importance of liquidity management in organizations, regardless of their size, was emphasized by Uyar (2009) Additionally, the studies conducted by Ding et al
Research by Enqvist et al (2014) and others indicates that effective working capital management significantly influences firm performance, with liquidity being a key determinant Despite varying findings, studies by Deloof (2003), Goddard et al (2015), and Palazzo (2012) reveal a positive correlation between cash holdings and profitability Safdar et al (2016) further confirmed this relationship in Pakistan, highlighting the beneficial impact of liquidity on ROA and ROE ratios This underscores the potential for managers to boost corporate profits and shareholder value through strategic investment in current assets Additionally, Le and Phan (2017) recognized liquidity as a vital performance factor for Vietnamese enterprises, reinforcing its positive effects These insights lead to the formulation of the following hypothesis.
H2: Liquidity is positively related to firm performance
Similar to previous studies, this study uses the logarithm of total assets as a measure of firm size (yang and Chen, 2009, Pantea et al., 2013; Le and Phan, 2017):
The size of a business is a critical determinant of its performance, with larger enterprises generally achieving higher profits compared to smaller firms (Abbasi and Malik, 2015) These larger organizations benefit from abundant resources and high production capacities, enabling them to diversify their operations, minimize risk, and enhance overall performance (Alvarez and Barney, 2001) According to Lee (2009), larger firms experience improved efficiency through economies of scale, leading to increased output and reduced average costs Additionally, large enterprises have better access to financial resources, often receiving loans at lower interest rates due to their strong repayment capabilities (Vieira et al., 2019) Research by Yang and Chen (2009) and Pantea et al (2013) supports the notion that firm size positively influences performance, while Zeitun and Saleh (2015) and Chen et al (2017) highlight that larger firms tend to achieve greater operational efficiency Thus, it can be hypothesized that there is a positive relationship between the size of a firm and its performance.
H3: The firm size is positively related to firm performance
Similar to previous empirical studies, many studies show a negative relationship between tangible assets and corporate performance (Sheikh and Wang, 2013; Vătavu,
2015) According to the studies of Margaritis and Psillaki (2010); Le va Phan (2017), TANG is calculated as the ratio of tangible fixed assets to total assets
Multiple studies indicate a negative correlation between tangible assets and operational efficiency across various industries Demsetz et al (2008) found that, in U.S manufacturing companies, increased tangible assets did not align with growth or operational efficiency Similarly, Beck and Demirgỹỗ-Kunt (2006) revealed a negative relationship between tangible assets and operational efficiency in banks globally, suggesting that such assets do not ensure growth Additionally, Chisti et al (2008) reported that tangible assets failed to enhance operational efficiency in Bangladesh's insurance sector.
A study by Brounen, Kok, and Quigley (2012) revealed a positive correlation between investment in tangible assets like real estate and financial efficiency within the real estate sector These findings highlight the importance of tangible assets in fostering competitive advantages, enhancing labor productivity, and boosting overall business performance Consequently, the author proposes the following hypothesis:
H4: TANG is positively related to firm performance
Growth can be measured through various methods, including the percentage change in sales (Fosu, 2013) and the difference in the book value of produced assets (Soumadi and Hayajneh, 2012) Sheikh and Wang (2013) suggest calculating growth as the cost of capital divided by total assets Empirical studies by Salim and Yadav (2012) and Sheikh and Wang (2013) indicate that firm growth has a positive effect on performance.
In which: Capital Expenditures = Fixed Assets i – Fixed Assets i-1
Research has shown a strong link between business growth and operational efficiency Delmar and Davidsson (2000) found that companies with higher operational efficiency typically experience faster growth Similarly, Wagner (2007) identified a reciprocal relationship, indicating that enhanced operational efficiency leads to quicker development and that growth can also improve efficiency These findings underscore the importance of optimizing operational processes for sustained business success.
Research has shown that management efficiency is crucial for business growth, with studies indicating that innovation and flexibility also enhance operational efficiency and development These findings establish a positive correlation between business growth and operational efficiency, emphasizing the importance of management efficiency, innovation, and flexibility in fostering business advancement Consequently, the author proposes the following hypothesis based on these empirical insights.
H5: Business Growth is positively related to firm performance
DATA
This study analyzes the annual financial statements of 25 Vietnamese retail companies listed on HOSE and HNX from 2015 to 2022, utilizing data sourced from the General Department of Taxation The research incorporates information from audited consolidated financial statements and annual reports, while also referencing GDP and CPI statistics provided by the General Statistics Office of Vietnam.
Chapter 2 lays the groundwork for the empirical research model developed in Chapter 3, where the author details the data collection and processing methods, as well as the calculation techniques for the variables involved This section establishes the implementation model and research framework for the study The author then applies various models, including Pool OLS, FEM, REM, FGLS, and SGMM with robust options, conducting necessary tests to identify the most suitable model The findings from these models will be thoroughly discussed in Chapter 4.