CONTRIBUTION OF RESEARCH The research results of the study “The Determinants of Corporate Liquidity in Retail Industry: Evidence from Vietnam” in the period from 2015 to 2023 help firm
INTRODUCTION
THE RATIONALE BEHIND THE RESEARCH TOPIC
The phrase “cash is king” from Keynes (1937), Duchin et al (2010), and Subramaniam et al (2011) underlines the importance of cash flow for business According to Tran Manh Dung and Nguyen Nam Tai (2018), a business's liquidity refers to the financial capacity it has acquired to repay debts to individuals, organizations with loan relationships, or the business itself Furthermore, Nguyen Duc Viet et al (2019) define liquidity as the ability to swiftly convert resources into short-term cash requirements Alahdal et al (2016) indicated that a company with a low current ratio may struggle to fulfill its short-term financial obligations to creditors, service providers, and suppliers in a timely manner
Payability is a core concept for borrowers and creditors, who use financial ratios and other financial information to determine whether a potential borrower has sufficient resources to pay off their obligations If a business lacks the funds to sustain its operations, it may face liquidation or bankruptcy When a business's head decides to fund its operations with additional debt or equity, the risk of liquidity is one of the main considerations for managers As a result, a business requires a reasonable amount of turnover to cover its short-term or long-term debts
Asian firms, particularly those in developed countries, face constraints due to borrowing limits and the need to hold more cash for future investments Given the great opportunities and challenges in Vietnam, companies need to focus on cash management, which is the lifeblood of the company Therefore, the current question for listed companies on the Vietnam Stock Exchange is how to manage cash in order to improve operational efficiency and contribute to the company's value increase Simultaneously, the enterprise uses the liquidity assessment to ascertain the true extent of its impact on the business market In addition, it helps investors, suppliers, and banks assess their ability to pay their debts when they’re due
The retail industry serves as a fundamental sector, generating production materials that cater to the demands of both industry and infrastructure In recent years, the economic structure of our country has undergone a strong shift towards industrialization and modernization The retail industry has made significant strides over many years of construction and development
Our country's economic structure has undergone significant transformation towards industrialization and modernization in recent years The retail industry, a foundational sector, produces essential materials to meet the needs of various industries and infrastructure Over many years of construction and development, the retail industry has achieved remarkable success Particularly during and after the COVID-19 period, factors such as increased customer demand, urbanization, and digital transformation have played a pivotal role According to Zheng (2022), the study found that the pandemic has affected the firm’s pre-COVID-19 cash holding and financing activities, resulting in a decrease in cash holding and an increase in financing activities Moreover, Zhou et al (2022) find that the impact of the COVID-
19 outbreak is to increase the current cash holding and decrease the future cash holding
Numerous national and international studies have addressed the relationship between micro and macro factors and the liquidity of a business Researchers conducted these studies at different periods and with diverse research samples As a result, the research outcomes are not consistent To the best of the author's knowledge, there has been no published research on the retail industry's liquidity until now, especially after COVID-19 and digital transformation A good understanding of a business's liquidity will help the manager make the right decisions on how to use resources properly and limit the unfortunate risks that may arise Therefore, there is a current need for the study "The Determinants of Corporate
Liquidity in the Retail Industry: Evidence from Vietnam", and the author aims to contribute to the empirical research on the liquidity of retail businesses in Vietnam.
RESEARCH OBJECTIVES
This paper considers the factors affecting corporate liquidity (LIQ) in listed retail small and medium-sized enterprises (SMEs) on the Vietnam Stock Exchange
The thesis delineates specific objectives that stem from the overarching study purpose Firstly, identify the factors that affect Vietnamese retail companies' liquidity levels Secondly, the study measures the influence of factors on the retail industry's liquidity in Vietnam Thirdly, provides suggestions for improving liquidity stability and lowering liquidity risks for retail companies in Vietnam.
RESEARCH QUESTIONS
The author outlines a few possible research questions based on the three specific objectives described above
Firstly, what are the key factors influencing the liquidity levels of retail companies in Vietnam?
Secondly, how do different factors impact the liquidity of retail companies in Vietnam?
What are the strategies that Vietnam's retail companies can implement to enhance liquidity stability and reduce liquidity risks?
RESEARCH SUBJECT AND RANGE
The essay's primary focus revolves around examining the determinants that influence the liquidity of the retail industry listed on the Vietnam Stock Exchange from 2015 to 2023
The study's spatial scope consists of the retail industry listed on the Vietnam Stock Exchange from 2015 to 2023 To gather and analyze the data, 20 firms’s financial reports were collected, for a total of 180 observations Utilizing STATA 17 software, the data was analyzed with an emphasis on six independent variables: firm size (SIZE), debt ratio (DR), return on assets (ROA), asset structure (AS), inflation (INF), and gross productive product (GDP)
The research scope spans nine years, from 2015 to 2023, focusing on 20 retail industries listed on the Vietnam Stock Exchange.
RESEARCH METHODOLOGY
The paper uses quantitative, qualitative research methods and pooled OLS, FEM, REM, and FGLS models to conduct analysis and find correlations between factors affecting the liquidity of retail enterprises in Vietnam
The study investigates the factors affecting liquidity (LIQ), which include firm size (SIZE), debt ratio (DR), return on assets (ROA), asset structure (AS), inflation (INF), and gross productive product (GDP) The audited financial statements of retail enterprises from 2015 to 2023 serve as the basis for collecting and calculating the micro-independent variables, while the World Bank report synthesizes the macro- independent variables
The present investigation utilizes both quantitative and qualitative research methods to examine the factors that impact corporate liquidity in the Vietnamese retail sector
Qualitative method: The author provides a research model to test specific hypotheses after examining pertinent domestic and international studies Moreover, the author employs qualitative research, utilizing empirical data and the theoretical framework of liquidity to bolster the research and model recommendations
Quantitative method: From 2015 to 2023, the author collected data samples from
20 retail enterprises listed on the Vietnam Stock Exchange For data analysis, the study effort used STATA 17 and Excel software to calculate a total of 180 observations Finding out how much independent factors affecting liquidity in the retail industry mattered, making sure the model works with models like pooled OLS, FEM, and REM, and testing hypotheses all played a part in the results.
CONTRIBUTION OF RESEARCH
The research results of the study “The Determinants of Corporate Liquidity in Retail Industry: Evidence from Vietnam” in the period from 2015 to 2023 help firm administrators have an overview of the consumer market and provide suggestions for improving liquidity stability and lowering liquidity risks in retail companies in Vietnam The author researched the aforementioned project with the specific goal of providing practical benefits to the retail industry
Theoretically, the research contributes to the body of knowledge by outlining fundamental theories related to liquidity and identifying the factors that influence liquidity in the retail industry on the Vietnamese stock market
Practically, the findings enable firms to forecast development trends and devise strategies for adjusting their liquidity to be stable in the future Additionally, the proposed recommendations can assist managers in planning optimal liquidity to enhance financial performance.
LAYOUT OF THE RESEARCH
The thesis includes five chapters:
This chapter introduces the urgency and rationale for selecting the research topic, establishing the general and specific research objectives, exploring relevant literature, and defining the scope and research object Additionally, it outlines the research method and the thesis's overall layout.
LITERATURE REVIEW
RETAIL INDUSTRY
Adapt business strategies appropriately to thrive in the competitive landscape The Vietnam retail market offers a wide range of products, including food, beverages, tobacco products, personal care, household products, apparel, footwear, and accessories, as well as furniture, toys, hobbies, industrial goods, automotive products, electronics, and house appliances, among others The market is further divided by distribution channels: hypermarkets and supermarkets, which are large- scale retail business locations that offer many types of products under one roof; specialty stores that specialize in specific products, such as electronics or apparel; department stores, which are large retail stores housing different departments under one roof; e-commerce, which are online shopping websites and apps; and several other channels, such as convenience stores and direct selling
Several trends have emerged and continue to emerge in Vietnam's retail markets The growth of e-commerce is exploding, omnichannel retailing is integrating with physical stores, modern retail formats are emerging, the demand for healthy and wellness products is rising, people are shifting to sustainable and ethical consumption, technology is a radical addition to the retailing industry, experiential retail is emerging, foreign retailers are increasing, there is an influx of private labels, and the adoption of cashless payments is on the rise E-commerce, particularly with platforms such as Shopee, Lazada, and Tiki, has shown remarkable growth, with a double-digit increase in the number of users and volume of sales Omni-channel retailing is integrating online and offline channels to give customers a seamless shopping experience, whether in the form of BOPIS (buy online, pick up in-store)
Modern retail is a shift from traditional markets to modern retail formats, and there is a growing proliferation of chains like VinMart, Circle K, and B's Mart
Vietnamese consumers are increasing their demand for convenience shopping, which is driving the growth of convenience stores and e-commerce Being digitally savvy, value-conscious, and attracted to quality products at reasonable prices, promotions, and discounts becomes important, as do brand reputation, loyalty, and sustainability, while social influence and experiential retail play significant roles in their purchase decisions The adoption of digital wallets and mobile banking apps has made cashless payments mainstream In a country where urban lifestyles and modernity are on the rise, the need for modern retailers to adopt new technologies in order to improve shopping experiences and provide more product choices is ever- increasing
Vietnam, with its rapid economic growth, higher consumer spending, and fast- moving retail landscape, is the most promising destination for any retailer looking to establish entry Other strategic routes include market entry through local partnerships, franchising, direct investment, mergers and acquisitions, e-commerce and omnichannel strategies, localization, brand development and marketing, regulatory compliance, customer experience, and sustainable practices Collaborating with local entities, joint ventures, master franchises, and local suppliers will allow a retailer to gain valuable market insights and guide them through the rigors of regulatory hurdles Direct investments, such as greenfield investments, provide greater control over operations, increase brand presence, and allow for market positioning customization Market localization requires adjusting products and services to meet local tastes and cultural dimensions, while brand development and marketing include customized campaigns and corporate social responsibility Effective regulatory and compliance management requires an excellent understanding of the local regulations and proper relationship building with the regulating authorities
Urbanization, improving incomes, and middle-class growth have been the engines driving retail growth in Vietnam The retail market in Vietnam has seen significant growth over recent years Recent estimates place the retail market in Vietnam at approximately $170 billion in 2020, and projections suggest that this growth will continue due to increasing consumer spending, urbanization, and the growing middle class The general statistics office of Vietnam expected the market size to expand further, reaching around $230 billion by 2025 To give a closer overview of Vietnam’s retail growth, the author will compare Vietnam's retail sales with Singapore's retail sales
Source: tradingeconomics.com|General Statistics Office of Vietnam
Source: tradingeconomics.com|Statistics Singapore
Figure 2.1: Retail sales between Vietnam and Singapore
The two graphs indicated the Year-over-Year (YoY) retail sales of Vietnam and Singapore from 2015 to 2023 For a clearer analysis, the author will divide it into three periods: pre-pandemic, during the pandemic, and post-pandemic During the period pre-COVID-19 pandemic from 2015–2019, retail sales in Singapore showed comparatively stable growth, while those in Vietnam, although within a stable range, showed irregular fluctuations During the COVID-19 pandemic, both countries saw a steep drop in retail sales However, the recovery level differed in 2021: Singapore's was rapid due to pent-up demand and eased restrictions, while Vietnam saw a sharp decline in retail sales Both countries' retail sales underwent a transformation from post-pandemic to post-pandemic recovery and nurturing Singapore regained its stability, whereas Vietnam was even more stable compared to the pre-pandemic period
2.1.3 Future risks and prospects for the retail industry
Vietnam's retail sector is facing significant challenges alongside its developments The Vietnamese retail market is highly competitive, posing difficulties not only for local businesses but also for many renowned international retail brands such as AEON Mall and Emart Consequently, domestic enterprises must undergo digital transformation using Industry 4.0 technologies to enhance their competitiveness against globally recognized retail brands In recent years, many foreign investors in modern retail have entered the Vietnamese market through mergers and acquisitions, maximizing market scale Notable transactions include Berli Jucker Group (BJC, Thailand) acquiring Metro Vietnam's supermarket chain; Central Group (Thailand) purchasing Big C Vietnam's supermarket and shopping center operations; and acquiring a 49% stake in Nguyen Kim, Tran Anh, Citimart, and Fivimart electronics stores, among others (VnExpress)
The Vietnamese retail market exhibits weak interconnections among its participants Modern retail, which shifts from supply chain-focused to customer experience-focused models, demands unified efforts and close collaboration among manufacturers, intermediaries, and other stakeholders in order to effectively implement digital retailing However, this presents a significant challenge due to weak linkages within the supply chain and among related industries, thereby hindering high customer satisfaction Large convenience stores and supermarket chains still lack professional management technology, with few adopting advanced management technologies Many small and medium enterprises (SMEs) continue to employ traditional retail methods manually, presenting obstacles to widespread digital transformation across the Vietnamese retail landscape
Moreover, consumer habits in Vietnam, though evolving, remain a substantial challenge Lazada's research highlights growing opportunities in online shopping habits, but half of consumers still prefer traditional marketplaces and supermarkets for direct and conventional shopping experiences Effecting change in these habits is not straightforward Additionally, in 2022, marketing methods and customer interactions are expected to continue evolving, with social media platforms like TikTok and Instagram driving customer engagement, particularly among younger demographics, and boosting online sales for many companies
The COVID-19 pandemic has significantly reshaped the global and Vietnamese retail landscapes on an unprecedented scale The pandemic's closure of physical stores has forced many retail owners out of business in recent years, with closures likely continuing into 2022 Even as Vietnam's market transitions to a new normal, the rate of store closures may slow down, but many businesses still face this unfortunate outcome (VietNam Briefing).
LIQUIDITY
When capital demands develop, liquidity refers to the ability to get assets or financial resources at a fair cost Liquidity is defined as the capacity to make all required payments on time Cash performance refers solely to money flows; failure to meet the payment requirement will result in a lack of liquidity Liquidity is significant in financial statement analysis since it refers to the quantity of money equivalents or assets that a firm has and can convert into money in a brief period of time
Liquidity also refers to a company's ability to meet its obligations Many organizations that excel at manufacturing and handling operations might struggle with cash flow Assets are extremely liquid when the following requirements are met: the proper quantity is available; there is a trading market in the right location; trading periods are at the appropriate times; and the pricing is fair A capital source is considered highly liquid when the cost of obtaining more is minimal and it offers rapid returns (at the appropriate price)
The lack of liquidity lowers the company's chances of securing special discounts or increasing earnings At the same time, cash constraints restrict operational flexibility Loss of liquidity might also force the firm to sell off investment projects and assets, raise cash at a steep cost, or declare bankruptcy Furthermore, liquidity allows organizations to be flexible and gain an edge when market circumstances change, as well as adjust to competition plans (Brigham & Houston, 2003)
Over the last several decades, the rapid growth of financial markets, particularly the maturation of the secondary stock market, has resulted in an extension of the image of financial assets, including the concept of liquidity As a result, the stock market's operating features determine a new definition of market liquidity (Bervas, 2006) The duration of the stock transaction represents the transaction cost The market depth, or the number of transactions executed immediately without changing the limit price, is critical The market's recovery capability is the rate at which prices can reach equilibrium after an unintended disturbance in the trade flow
The concept of market liquidity highlights the shift in liquidity risk, which is now closely linked to the ability to generate a monetary fund that both institutional and retail investors seek In the financial markets, investors can always change their currency by selling current financial assets in their portfolio, issuing new financial instruments, or combining the two Changes in cash flows resulting from the sale of these financial assets may jeopardize the overall financial asset; the actual value realized varies from the projected value Investors are especially interested in this form of risk, which is closely related to market liquidity risk
The rapid development of the financial system has facilitated the securitisation of non-formally traded financial assets, such as bank loans, leading to the expectation among market participants that they can meet their liquidity needs by creating new financial assets or arranging the financial property they own The transferability of an asset is defined as the ability to sell it, transform it into money quickly at a minimal transaction cost, and have little effect on the asset's price
Today, market players consider liquidity to include not just the monetary base and the bank's immediate deposits, but the entire financial market Thus, according to Tirole (2008), the definition of "an asset that provides liquidity to the world if it can be used as a buffer to meet needs" is now being expressed by a broader set of credit and currency, and according to some authors (Adrian and Shin, 20007), liquidity includes all assets on the balance sheets of financial institutions
The first studies looked at the characteristics of corporate cash holdings, such as Ferreira and Vilela (2004), Kim et al (2018), and Opler et al (1999); Ozkan (2004) assumed that cash is the means of exchange, so companies need cash to conduct daily transactions; however, the demands for cash of different companies are not the same According to Nguyen et al (2016), when firms have insufficient internal funds or liquid assets, they will raise funds from external capital markets, liquidate existing assets, limit dividend payouts, and reduce investment opportunities However, all of these activities are costly
Business solvency is the financial capacity that a company develops to satisfy the requirement to repay obligations to persons or organizations linked to a business loan or debt That financial capacity is available in the form of money (currency, deposits, etc.), amounts to be collected from persons in corporate debt, and assets that may be promptly transformed into money, such as goods, completed products, and items sold
Business debts may include bank loans, commodity debts resulting from a sales transaction, revenue or outputs of business items owed to the seller or buyer, unpaid taxes to the state bank, or unpaid salary The current ratio, quick ratio, and cash ratio are all indicators of a company's capacity to repay creditors
Business owners are concerned about unrecoverable debts and unpaid payments
As a result, businesses must maintain a sufficient amount of working capital in order to pay short-term loans on time and retain inventory to ensure a smooth manufacturing process When businesses fail to meet their obligations, creditors may declare them bankrupt in countries around the world that have market systems based on corporate bankruptcy laws, and Vietnam's Corporate Law provides the same protection
Investors, commodities suppliers, lenders, and other stakeholders are particularly interested in solvency norms Investors, commodities suppliers, lenders, and other stakeholders are particularly interested in this index because they seek assurance that their investment in the firm is well-utilized and that it can fulfill its obligations punctually The following parameters show the business's capacity to make payments:
A company's short-term solvency is defined as the connection between its total short-lived assets and short-term debt
This coefficient measures the level of collateral between short-term assets and short-term debt During this time, enterprises that are required to pay short-term obligations must convert their current assets into money, which they then use to pay their debts The company most likely converts short-term assets, which it owns and has the right to utilize, into cash
If the capital ratio (CR) exceeds one, it implies that the company is capable of repaying its obligations An increase in this element suggests a greater amount of debt repayment guarantees, which minimizes the company's chances of bankruptcy This suggests a fortunate financial status However, if the component is too high, it is not a positive indicator; it reflects the company's abundance of payments, but it also affects capital efficiency owing to excessive investment in short-term assets, which might lead to a grave financial position
If CR is 1, it indicates that the business's solvency is poor, as its short-term assets are insufficient to meet its short-term and ongoing obligations
If CR falls to zero, they will be unable to repay their obligations; their financial condition is deteriorating; and they face bankruptcy
However, the disadvantage of this index is that the quantity of assets (short-term assets) includes assets that are difficult to convert into money to repay loans, such as hard-to-receive debts, low-quality goods, losses awaiting processing, and so on Some experts believe that the coefficient of 2 is the best; however, this is merely a guideline since it varies based on several elements and situations in each business But there are two contradictions:
THEORICAL FRAMEWORK OF LIQUIDITY
The liquidity of businesses is explained by popular theories including the Trade- off Theory (Myers, 1977), the Pecking Order Theory (Myers and Majluf, 1984), the Trade-credit theory (Petersen & Rajan, 1997)
According to the trade-off theory, there is an optimal amount of cash holdings with a given level of debt Corporations can determine this optimal level by weighing the marginal costs and advantages of keeping cash on hand (Oplet et al 1999) Holding cash would bear the “cost-of-carry”, assuming that managers seek to optimize shareholder capital The main cost of holding cash is often associated with the opportunity cost of the capital invested in liquid assets (Ferreiea and Vilela, 2004) The prime benefit of holding cash is to minimize the external capital raising costs and to avoid missing growth opportunities because of the shortage of liquid assets (Dittmar et al 2003; Faulkender and Wang, 2006) In addition, Ferreia and Vilela (2004) stated that cash holdings can protect firms from the likelihood of financial distress In Vietnam, bankruptcy-related costs are high, making the trade- off theory more supportive of cash-holding decisions (Al-Najjar, 2013) However, the cash reserve is not always beneficial for businesses Firms that stockpile cash levels more than the optimal balance might obtain a low rate of return on cash or liquid assets The agency cost of managerial discretion also increases the cost of cash holdings if managers maintain cash to keep more assets under their control for their interest rather than acting on shareholder wealth, according to Saddour (2006) Several studies used financial determinants of cash holdings to investigate the trade-off theory on cash holding behavior For example, Al-Najjar and Belghitar (2011), Ferreira and Vilela (2004), and Oplet et al (1999) employed leverage liquidity, dividend payout, firm size, and growth to empirically examine this theory Firstly, the Trade-Off Theory posits that firms seek to strike a balance between the benefits of liquidity and profitability In the context of Vietnamese retail firms, analyzing how they manage this trade-off is crucial Higher liquidity provides security against financial distress and operational shocks but may come at the cost of lower profitability if idle cash is not efficiently utilized Conversely, focusing too much on profitability might lead to lower liquidity levels, potentially increasing financial risk Studying how retail firms in Vietnam navigate this trade-off can provide insights into their liquidity management strategies
Secondly, according to the Trade-Off Theory, firms consider investment opportunities when determining their optimal liquidity levels Retail firms in Vietnam may face opportunities for expansion, technological upgrades, or market penetration Assessing how these firms balance liquidity needs with investment opportunities can reveal whether they adjust their liquidity positions based on potential returns from investments This analysis can highlight the relevance of investment decisions in influencing liquidity levels
Thirdly, the theory emphasizes that firms weigh the costs (e.g., interest payments, dilution of ownership) and benefits (e.g., increased liquidity, funding for growth) of external financing options (debt and equity) For retail firms in Vietnam, understanding how they evaluate these costs and benefits when managing liquidity is crucial Examining whether firms rely more on internal funds or external financing sources can indicate how they perceive the trade-off between maintaining liquidity and accessing external capital
Fourth, Trade-Off Theory also suggests that firms consider risk factors when making financial decisions In the retail industry of Vietnam, where economic conditions and market dynamics can be volatile, analyzing how firms integrate risk management into their liquidity strategies is essential This includes assessing whether firms adjust liquidity levels in response to perceived risks and how these adjustments impact their overall financial stability and performance
Fifth, Trade-Off Theory acknowledges that firm-specific characteristics and industry factors influence liquidity decisions Exploring how firm size, market competition, and regulatory environment in Vietnam's retail industry affect liquidity choices can provide context-specific insights For instance, smaller retail firms might face different trade-offs compared to larger chains due to access to resources and market positioning
To sump up, applying the Trade-Off Theory to the study of corporate liquidity in the Vietnamese retail industry involves examining how firms balance liquidity and profitability objectives, consider investment opportunities, evaluate costs and benefits of financing options, implement risk management strategies, and respond to industry-specific dynamics These analyses can help in understanding the determinants of liquidity and the financial decision-making processes within Vietnamese retail firms
Pecking order (or financial hierarchy) theory was first introduced by Donaldson (1961) and extended by Myers and Majluf (1984) This theory upholds the concept of funds priority order when firms decide which funds to use for financing investments The theory states that firms prefer to finance their projects with internal resources that can be accessed easily After that, they will adjust their dividend levels to exploit retained earnings (available liquid assets), even if the firms follow a sticky dividend policy (Tahir et al 2016) If the retained earnings ratio can no longer be adjusted, firms would tend to sell liquid assets, and external capital raising is only their last resort This theory focuses on using internal resources as the least expensive resource for a firm's financing; thereby firms can reduce capital costs Pecking order theory comes from asymmetric information and agency problems theories to minimize costs related to equity issuing The theory supports the idea that if a firm is profitable enough to finance its investment, there should be no or less external funding Moreover, the Pecking Order Theory can be applied in several ways: Firstly, according to the Pecking Order Theory, firms prefer internal financing (retained earnings) over external financing (debt and equity) due to information asymmetry and adverse selection costs In the context of corporate liquidity in the retail industry, firms may prioritize using internal cash flows to maintain liquidity rather than seeking external financing options This preference can be analyzed to understand how much firms rely on internal resources to manage their liquidity positions
Secondly, the theory suggests that firms prefer to issue debt over equity when external financing is necessary, as debt issuance is less costly in terms of adverse selection and signaling costs In the retail industry of Vietnam, understanding how firms manage their financial structure (debt levels) in relation to liquidity can provide insights into whether higher debt levels are associated with lower liquidity due to repayment obligations
Thirdly, Firms following the Pecking Order Theory tend to prioritize maintaining liquidity reserves to manage uncertainties and unforeseen expenses without resorting to external financing Examining how retail firms in Vietnam manage their liquidity buffers and whether these strategies align with the principles of the Pecking Order Theory can shed light on their liquidity management practices
Fourth, profitability, as per the theory, affects a firm's ability to generate internal funds Higher profitability typically implies greater internal cash generation, thereby potentially enhancing liquidity Conversely, lower profitability might compel firms to seek external financing options, impacting their liquidity ratios Analyzing the relationship between profitability and liquidity in Vietnamese retail firms can illustrate how profitability dynamics align with the Pecking Order Theory principles
In summary, applying the Pecking Order Theory to the study of corporate liquidity in the Vietnamese retail industry involves examining how firms prioritize internal financing, manage their financial structures, deploy liquidity management strategies, and respond to profitability fluctuations These insights can help in understanding the determinants of liquidity in this specific industry context
Trade credit theory, developed by Schwartz in 1974, suggests that non-financial firms often extend credit to customers for financial intermediation Trade credit is characterized by the purchase and sale of products and the delay in payment, which differs from bank credit in that it is a type of credit transferred between firms It has the advantage of saving time and transaction cost compared to the banking lending channel and is more advantageous due to its abstract nature
In the context of the retail industry in Vietnam, where access to conventional funding may be restricted, trade credit can become a critical tool for managing liquidity and the sustenance of business operations It allows retailers to better manage their cash flow since it provides flexibility in terms of payment, which can be very helpful in a volatile market environment Understanding the determinants of retail industry liquidity, such as trade credit practices, empowers Vietnamese firms to make informed decisions in the optimization of financial performance and sustained growth
Firsly, the Trade Credit can help retailers in Vietnam enhance their general financial health and resilience to face market fluctuations This can also help retailers in Vietnam broaden their product offerings and reach out to a larger customer base, thus increasing revenues and profitability Furthermore, by establishing a reputation for reliable payment practices, retailers can attract more suppliers and establish a stronger position in the industry In general, trade credit can become a valuable tool for retailers in Vietnam to enhance their financial stability and competitiveness in the market
Secondly, the Trade Credit Theory, firms often use trade credit (credit extended by suppliers) as a short-term financing tool to enhance liquidity In the context of Vietnamese retail firms, examining how these businesses utilize trade credit from their suppliers can reveal insights into their liquidity management strategies Specifically, studying whether firms strategically manage trade credit terms (e.g., extending payment periods or negotiating discounts) to optimize their cash flow and liquidity positions is crucial
LITERATURE REVIEW
Many relevant articles have extensively discussed the motivations to hold a firm's cash, together with empirical evidence The following section explores and reviews essential papers and their findings to develop research hypotheses based on important determinants and research gaps
Al-Homaidi, Tabash, and Al-Ahdal (2019) delved into the factors influencing the liquidity of Indian listed firms over a six-year period, spanning from 2010 to
2016 Utilizing pooled, fixed, and random effects models, the study examines a sample of 2150 firms selected from the 5129 listed on the Bombay Stock Exchange Internal variables such as capital adequacy, firm size, leverage ratio, return on assets, return on equity, and firm age, alongside external factors including GDP, inflation rate, and exchange rate, are scrutinized to understand their impact on liquidity
Dang Thu Hang (2020) investigated the impact of liquidity using a dataset comprising 6700 observations from Vietnam's stock market spanning from 2008 to
2019 It explores the influence of various internal factors such as firm size, capital adequacy, profitability, and leverage ratio, along with external variables including GDP, inflation rate, exchange rate, and interest rate Employing a robust regression method within the framework of fixed effects linear panel data analysis, the study aims to elucidate these relationships comprehensively
Duong Ngan Ha et al (2023) conducted to determine the impact of macro and micro factors on the short-term solvency of listed enterprises in Vietnam The study used data from 305 companies listed on the Ho Chi Minh City Stock Exchange (HOSE) in the period from 2011 to 2021, along with a fixed regression model The results showed that company size, debt ratio, and interest rates have a negative impact while asset growth, profit growth, and asset structure have a positive impact on liquidity
Lyroudi and Bolek (2012) analyzed the Polish market regarding liquidity and profitability concerns, aiming to ascertain which variables exert a greater influence on market value Building upon prior research into financial models such as asset structure, return on equity, return on assets, and economic value added (EVA), the study delves into how investors interpret corporate liquidity and profitability ratios on the Warsaw Stock Exchange throughout the years 2000-2009 With a dataset comprising 696 observations, the study employs statistical evaluation via the t-test Additionally, the main indicators of liquidity utilized are the current ratio (CR), the quick ratio (QR), and the acid test (AT)
Nguyen Duc Viet et al (2019) examined how a number of factors impact the liquidity of listed steel firms on the Vietnam Stock Exchange Audited financial books of 25 listed food processing firms from 2014 to 2017 were selected for this research This research is conducted by utilizing the least-squared forms together with associated tests on the impacts of the selected variables on the sample listed corporations' liquidity The results show that returns on assets (ROA), operating period (AGE), and asset structure (AS) have a positive impact on liquidity, while the debt ratio and business size have an opposing impact
Nguyen Huu Tan (2021) investigated the determinants of corporate liquidity among Vietnamese steel-listed firms spanning the period from 2015 to 2020 Employing both Fixed Effect and Random Effect regression methods, the study aims to gauge the influence of various factors on the liquidity of steel-listed companies in Vietnam Subsequently, the author conducts Hausman tests to compare the effectiveness of these estimation techniques The research categorizes factors into firm-specific variables such as firm size, debt ratio, profitability, asset and revenue growth rates, asset structure, cash flow to total liability, market value, years of operation, and microeconomic factors including GDP and inflation rate
Tran Manh Dung and Nguyen Nam Tai (2018) conducted to assess factors' influence on the solvency of food processing enterprises listed on the Vietnamese stock market The data were collected based on 31 food processing companies whose financial statements had been audited and these companies were listed during the period from 2012 to 2016 The OLS methodology was used to obtain the data The obtained results tend to show that the size of a company, its asset return (ROA), its income return (ROS), its operating period (AGE), and its asset structure (AS) have the same impact as liquidity On the contrary, the return on equity (ROE) and the debt ratio have the opposite effect
Tran Thi Thu Huyen and Dao Thu Ha (2024) investigated the factors affecting solvency of real estate firms that have been listed on the Vietnam Stock Exchange The data was obtained from the financial statements of 80 enterprises related to the real estate sector listed on the Vietnam Stock Exchange over the period 2018-2022 and used through the SPSS20 software According to the analysis, four factors have an impact on solvency: return on assets (ROA), net profitability (ROS), asset structure (AS), and the ratio of working capital (WCR)
Tran Van Hai (2021) examined the short-term solvency of Vietnamese securities companies The data was collected from 48 securities companies between 2012 and
2019 The research used the STATA14 software package to choose the regression model, assess and estimate the regression model, and check the number table of the array data on 235 observations The findings showed that the company size and the quick ratio have a positive impact, meanwhile the debt ratio and the working capital turnover have a negative effect on the short-term solvency of the securities business
Truong Thanh Hang (2017) examined the determinants of liquidity among manufacturing enterprises listed on the Vietnam stock market from 2011 to 2015 Utilizing financial reports from 100 enterprises, data collection and analysis were conducted Initially comprising 500 observed variables, the dataset was refined to
460 observations after eliminating entries with missing information Analysis was conducted using STATA 12 software, with a focus on six independent variables: enterprise scale, return on assets, asset structure, inventory turnover, quick ratio, and average collection period
The authors have synthesized studies at Vietnam and abroad that relate to the liquidity impact factors of retail in particular and other sectors in different time and spaces Below is a synthesis of studies published over a period of 5 to 10 years from the time the author carried out this thesis
Table 2.1: Synthesis of studies published
SIZE Al-Homaidi, Tabash, Al-
Nguyen Duc Viet et al
Dang Thu Hang (2020), Duong Ngan Ha et al (2023), Nguyen Huu Tan (2021), Tran Manh Dung and Nguyen Nam Tai (2018), Tran Thi Thu Huyen and Dao Thu Ha (2024)
Al-Homaidi, Tabash, Al- Ahdal (2019), Duong Ngan
Ha et al (2023), Nguyen Duc Viet et al (2019), Nguyen Huu Tan (2021), Tran Manh Dung and Nguyen Nam Tai (2018), Tran Van Hai (2021)
Tran Thi Thu Huyen and Dao Thu Ha (2024)
Viet et al (2019), Tran Manh
Dung and Nguyen Nam Tai
(2018), Tran Thi Thu Huyen and Dao Thu Ha (2024)
Dang Thu Hang (2020) Duong Ngan Ha et al (2023),
Nguyen Huu Tan (2021), Truong Thanh Hang (2017)
Duong Ngan Ha et al
(2023), Nguyen Duc Viet et al (2019), Tran Manh Dung and Nguyen Nam Tai
(2018), Tran Thi Thu Huyen and Dao Thu Ha (2024),
GDP Al-Homaidi, Tabash, Al-
Duong Ngan Ha et al (2023), Nguyen Duc Viet et al (2019), Nguyen Huu Tan (2021), Tran Manh Dung and Nguyen Nam Tai (2018), Tran Thi Thu Huyen and Dao Thu Ha (2024), Tran Van Hai (2021)
INF Al-Homaidi, Tabash, Al-
Dang Thu Hang (2020), Nguyen Duc Viet et al (2019), Nguyen Huu Tan (2021), Tran Van Hai (2021)
Source: Collected by the author.
RESEARCH METHODOLOGY
RESEARCH PROCESS
The research is done step-by-step through supported software such as STATA 17 and Excel The procedure is as follows:
Step 1: First, the author carries out an analysis of the theoretical foundations and previous studies that have similarities with the subject, including research in Vietnam and internationally Then the author analyzes and discusses the gaps in the previous study Through this step, the author identifies the design direction of the research model
Step 2: Based on the theoretical knowledge and the studies of archaeologists in
Step 1, the author begins building a suitable model that includes research objectives,
Previous research and theoretical foundations
Develop a model that corresponds to the research objectives
Analyse data and identify research patterns.
Analysis of the Research Model
Evaluate the regression assumptions of the research model.
Based on regression, discussions, evaluations, and conclusions anticipating regression equations, designing models, using suitable variables, building research hypotheses, and synthesizing data
Step 3: After completing Step 2, the author proceeds to run the data and identify the appropriate study pattern If the existing sample meets the research objective, proceed to step 4 Furthermore, the author will collect and process the data of the variables to adjust to the study model that was built in Step 2 in case the current study sample does not meet the requirements
Step 4: With data sets that have been compiled and processed, use STATA 17 for statistical analysis This process involves performing descriptive statistics, correlation testing, and conducting panel data regression analysis Regression models such as Pooled OLS, FEM, and REM are used to analyze the relationship between variables in the model
Step 5: The author performs the table regression using Pooled OLS, FEM, REM methods and uses tests like F-test, and Hausman to choose the model that is most suitable for the study and test the theories of the regression model, from which the result is obtained
Step 6: Based on the identified model, proceed to verify the defects of the model such as multicollinearity, autocorrelation, and heteroscedasticity; these issues can lead to the results of the estimates from the obtained variables and affect the analysis results If there is one of these defects, the author will use the GLS (Generalized Least Squares) method to correct it The research will perform tests to ensure that the model has no deviations and estimates of effectiveness
Step 7: Based on the regression results, the author discusses the results, presents the factors affecting liquidity, and provides appropriate suggestions, recommendations, and policies to address the research questions and hypotheses.
RESEARCH DATA
The study investigates the factors affecting LIQ: SIZE, DR, ROA, AS, INF and GDP The micro-independent variables are collected and calculated on the basis of the audited financial statements of retail enterprises over the years from 2015 to
2023, the macro-independent variables are synthesized based on the World Bank report
In this study, Microsoft Excel software was used to process data and calculate and sort the values of the variables mentioned in the study To estimate the impact of independent variables on dependent variables, the author uses different regression models and performs tests to select the appropriate model, correcting the defects of the model
Qualitative Method: The author conducts a review of domestic and international studies related to the research topic, thereby proposing a research model and hypotheses
Descriptive statistics: The author uses the descriptive statistical method to describe the basic properties of the incoming data, this includes the number of observations, the mean value, the standard deviation, the smallest value, and the greatest value From there, a general assessment, a comparison of variables, and the state of the observations
Testing correlation: The correlation analysis of the research model This analysis examines the model's accuracy, providing effective solutions through the Pearson correlation coefficient To consider the outcome of the correlation between independent and dependent variables, the coefficient must be lower than 0.8 and higher than -0.5 If this coefficient is higher than 0.8, this indicates that the model is highly likely to encounter multicollinearity, causing some consequences for the ordinary least squares (OLS) estimation Furthermore, when using the Variance Inflation Factor (VIF) to test multicollinearity in the model, VIF values are usually between +1 and -1 The higher the VIF value, the stronger the multicollinearity In this thesis, if an independent variable's VIF exceeds 10, it leads to multicollinearity and necessitates reconsideration in the regression model
Regression analysis: Regression analysis is used to evaluate the direction and magnitude of the impact on the research subject The authors used a range of models, including Pooled Ordinary Least Squares (OLS), Fixed Effects Model (FEM), and Random Effects Model (REM) Then proceed to a comparison between the two Pooled OLS and FEM models, Pooled Ols and REM, FEM and Rem, respectively, using F-test and Hausman test to select a suitable model When a suitable model has been selected, it is necessary to analyze autocorrelation and heteroscedasticity If this phenomenon exists, select the Feasible Generalized Least Squares model (FGLS)
On the contrary, without these two phenomena, the previous selection pattern remains the same.
RESEARCH RESULTS
DESCRIPTIVE STATISTICS OF VARIABLE
Descriptive statistics of the variables in the research model are presented in a table including observations (Obs), mean value (Mean), standard deviation (Std dev.), minimum value (Min), and maximum value (Max)
Table 4.1: Statistics describe the variables in the model
Variable Obs Mean Std dev Min Max
Source: Compiled by the author from STATA 17
Data from 20 retail companies for the period 2015–2023 is shown in Table 4.1, indicating 180 observations The statistical results are explained as follows:
Liquidity (LIQ) has an average value of 1.9020 during the period from 2015 to
2023, with a moderate standard deviation of 1.1242 The highest value of 7.0343 in
2017 (AFX) and the lowest value of 0.2729 in 2023 (HTT)
Firm size (SIZE) has an average value of 11.9598 during the period from 2015 to
2023 with a moderate standard deviation of 0.5748 The highest value of 12.9767 in
2023 (PET) and the lowest value of 10.4608 in 2020 (DKC)
Debt ratio (DR) has an average value of 0.4783 from 2015 to 2023 with a moderate standard deviation of 0.2167 The highest value of 0.9062 in 2016 (PSD), and the lowest value of 0.0802 in 2017 (AFX)
Return on assets (ROA) has an average value of 0.0513 from 2015 to 2023 with a moderate standard deviation of 0.0634 The highest value of 0.2450 in 2018 (AST) and the lowest value of -0.2557 in 2021 (AST), indicating that the firm was not performing efficiently that year However, the positive average value of 0.0667 shows that most companies are still profitable
Asset structure (AS) has an average value of 0.6265 with a moderate standard deviation of 0.2091 The lowest value was 0.0915104 in 2018 (HTT), and the highest was 0.9979568 in 2015 (PSD)
Inflation (INF) has a minimum value of 0.0063 and a maximum value of 0.0354
The mean is 0.0273 and the standard deviation is 0.0089 The mean value is greater than the standard deviation (0.0273 > 0.0089), which shows good data dissemination
Gross Domesticd Product (GDP) has a minimum value of 0.0256 and a maximum value of 0.0801 The mean is 0.0598 and the standard deviation is 0.0191 The mean value is greater than the standard deviation of (0.0598 > 0.0191), which shows good data dissemination.
CORRELATION ANALYSIS OF THE RESEARCH MODEL
The correlation matrix demonstrates the relationship between pairs of variables through the Pearson correlation coefficient To consider the correlation results between independent and dependent variables, this coefficient must be in the range from -1 to +1 The correlation analysis results are shown in Table 4.2.
Table 4.2 Pearson correlation coefficient results
LIQ SIZE DR ROA AS INF GDP
Source: Calculated by the author using STATA 17
The Pearson correlation coefficient is used to demonstrate the relationship between pairs of variables in the correlation matrix To consider the correlation results between independent and dependent variables, this coefficient must be in the range of -1 to +1 Table 4.2 displays the results of the correlation analysis
Where firm size is concerned, the Pearson correlation matrix in Table 4.2 shows liquidity has a negative meaning (-0.2285); this implies that large firms may struggle to attain good liquidity The debt ratio has been -0.7475, which means that higher debt affects liquidity in a significantly negative manner It is positive and significantly related to the return on assets (ROA) at 0.2510; this shows that profitability in the form of effective use of assets increases liquidity On the other hand, asset structure has a negative coefficient of -0.2189, which suggests that a greater proportion of fixed assets may decrease liquidity The study reveals a positive coefficient of 0.0658 for inflation, indicating a slight positive impact on liquidity, potentially due to retailers raising their selling prices and boosting their revenues The study found a negative correlation between liquidity and GDP, implying that economic growth might not improve liquidity due to heightened competition or rising costs In conclusion, it is crucial for any business to manage debts and assets
To demonstrate whether the model has multicollinearity issues, the authors conducted a variance inflation factor test (VIF) If the VIF of a variable is greater than 10, then the variable presents this phenomenon, and the greater the VIF, the higher the occurrence
Table 4.3 Testing the variance inflation factor (VIF)
Source: Calculated by the author using STATA 17
The test results show that all variables have a variance inflation factor and the mean-variance inflation factor (Mean VIF) is less than 10 However, in the fields of economics and finance, the variance inflation factor VIF of the explanatory variables in the model is greater than 2, which is considered multicollinearity between variables With the variables in the research model through testing the variance magnification factor VIF presented in Table 4.3 shows that the coefficients of the variables and the average value of VIF are all less than 2.3 Therefore, multicollinearity does not exist in this model.
CONCLUSIONS AND RECOMMENDATIONS
CONCLUSIONS
Research hypothesis Variable Expected relationship Research results Level of statistical significance
Source: Collected by the author
Liquidity is a crucial indicator for companies, both in operations and in building investor confidence The assessment of company risk also heavily weighs on liquidity Therefore, controlling liquidity helps stabilize companies, promotes better growth, and enables capital mobilization at lower costs This study aimed to identify factors influencing liquidity in Vietnam's retail sector using a research sample of 20 retail companies and a total of 180 observed variables from 2015 to 2023 Employing appropriate methods for variable selection, the study identified liquidity (LIQ) as the dependent variable and six independent variables: firm size (SIZE), debt ratio (DR), return on assets (ROA), asset structure (AS), inflation (INF), and gross domestic product (GDP) The study successfully selected the most suitable model and achieved its research objectives
The research used the F-test and Hausman test to determine the appropriate model among the Pooled Least Squares (OLS), Fixed Effects Model (FEM), and Random Effects Model (REM) for analyzing the data effectively Upon selecting the appropriate model, the study addressed autocorrelation and heteroscedasticity issues
If these phenomena were present, the Feasible Generalized Least Squares model (FGLS) was selected
Based on the FGLS model results, the study found that among the factors influencing liquidity in the retail industry in Vietnam, debt ratio and return on assets have a negative impact on liquidity in the retail industry in Vietnam, while asset structure and firm size have a positive impact These findings enable the authors to propose appropriate policies aimed at improving the liquidity situation of retail enterprises.
RECOMMENDATIONS
Liquidity analysis is a crucial tool for retail businesses in Vietnam to manage their liquidity and mitigate financial risks It helps in minimizing financial risks and promoting development, but it should be viewed with prudence Effective debt management policies and support for small and medium-sized retail enterprises (SMEs) can significantly enhance liquidity and overall financial health Supporting SMEs with easier access to capital and financial management training can also help to improve liquidity
Larger firm sizes have a positive impact on liquidity, indicating that bigger firms can better manage their financial resources and maintain stable cash flows However, expansion leads to increased operating costs, including labor, management, marketing, and other operational expenses A firm may reduce liquidity if it fails to control these costs or if investments do not yield quick returns Additionally, during expansion, retail firms often need to increase inventory levels and extend trade credit (receivables) to customers While this boosts short-term assets, not all of them are highly liquid Inventory and receivables cannot be immediately converted into cash, thus reducing liquidity For instance, from 2015 to 2023, retailers in Vietnam, like local pharmacies and clothing shops, faced liquidity challenges when expanding rapidly without sufficient financial controls
To mitigate these risks and enhance liquidity, retail businesses should aim to expand their operations strategically Retail businesses can achieve this by engaging in mergers and acquisitions and increasing their production capabilities Diversifying revenue streams by offering a broader range of products and entering new markets can also buffer against market fluctuations and ensure steady cash flows Leveraging economies of scale is crucial; larger firms can negotiate better terms with suppliers, reducing per-unit costs and securing discounts and more favorable credit terms Moreover, investing in advanced technologies and streamlining processes can significantly improve operational efficiency Better inventory management, customer relationship management, and supply chain optimization can help maintain liquidity In the context of small and medium-sized enterprises (SMEs) in Vietnam from 2015 to 2023, these principles have proven essential For example, AFX, a small retail firm specializing in food distribution, strategically expanded its operations by opening new branches and scaling up production capabilities By diversifying its product offerings and entering new markets, AFX increased its revenue streams and buffered against market fluctuations, ensuring steady cash flows Similarly, DHT, a pharmaceutical distributor, leveraged economies of scale by negotiating better terms with suppliers, which reduced per-unit costs and enhanced liquidity Both AFX and DHT invested in advanced technologies to improve operational efficiency, including better inventory management and customer relationship management (CRM) These strategies allowed these firms to navigate the challenges of growth while maintaining liquidity, demonstrating the importance of strategic growth, diversification, and operational efficiency for SMEs in the Vietnamese retail industry
Debt management is crucial to maintaining a firm's liquidity The debt ratio (DR) has a strongly negative impact on liquidity, indicating that firms with high debt ratios often struggle to maintain financial stability High levels of debt can place excessive pressure on a company's finances, making it difficult to sustain healthy cash flows
To mitigate this risk, retail businesses should consider reducing their debt levels or optimizing their debt structure Loan policies should be carefully reviewed to ensure that debt does not place undue strain on the firm's financial health
To address these issues, retail businesses should prioritize debt reduction by using retained earnings for funding operations and growth instead of taking on new debt Refinancing existing debt to obtain lower interest rates and longer repayment terms can also alleviate immediate liquidity pressures Furthermore, implementing strict criteria for taking on new debt is essential Loans should only be considered if they are vital for strategic growth and if the company has a clear repayment plan that does not compromise liquidity
Effective debt management has been critical for small and medium-sized enterprises (SMEs) in Vietnam from 2015 to 2023 For instance, the retail firm BTT faced significant liquidity challenges due to high debt levels By prioritizing debt reduction and using retained earnings to fund operations, BTT improved its liquidity Similarly, PET managed to refinance its existing debt, securing lower interest rates and longer repayment terms, which helped alleviate liquidity pressures Both firms also adopted prudent borrowing practices, ensuring that any new loans were essential for strategic growth and had clear repayment plans These strategies enabled BTT and PET to maintain healthier financial positions and navigate the challenges associated with high debt levels, highlighting the importance of debt management in the Vietnamese retail industry
ROA has a negative impact on liquidity, as it suggests that profits are often reinvested into fixed assets rather than enhancing short-term assets To address this, firms should focus on improving the efficiency of asset utilization to enhance ROA while maintaining it through effective use of capital and prudent cost management
To mitigate the negative impact of ROA on liquidity, retail businesses should balance reinvestment strategies between fixed assets and working capital, which is also crucial Allocating a portion of profits towards maintaining or increasing current assets can enhance liquidity By adopting lean management practices and investing in technologies that streamline processes and reduce waste, operational efficiency can be improved, resulting in higher asset turnover rates and improved liquidity Implementing robust cash flow management practices, such as better inventory control and efficient receivables collection, is essential to ensuring that profits translate into readily available cash
In the context of small and medium-sized enterprises (SMEs) in Vietnam from
2015 to 2023, these principles have proven essential For instance, by focusing on efficient asset utilization, AST, a goods distribution specialized company, improved its liquidity They regularly reviewed asset performance and sold underperforming assets, which freed up cash DHT, which is pharmaceutical distribution successfully balanced reinvestment strategies between fixed assets and working capital, allocating a portion of profits towards current assets to enhance liquidity HTM, which is Specialized Goods Distribution, improved operational efficiency by adopting lean management practices and investing in technologies that streamlined processes and reduced waste, enhancing asset turnover rates and liquidity These strategies allowed AST, DHT, and HTM to navigate the challenges associated with ROA, demonstrating the importance of efficient asset utilization, balanced reinvestment, operational efficiency, and robust cash flow management in the Vietnamese retail industry
The research findings indicate that asset structure (AS) positively impacts liquidity, suggesting that a rational asset structure can enhance a company's ability to meet short-term obligations This was particularly significant for retail businesses in Vietnam from 2015 to 2023, which faced numerous market fluctuations and challenges, including economic downturns and changing consumer behaviors Therefore, firms should maintain a balance between fixed and current assets to ensure liquidity Investing in current assets can be especially beneficial during economic downturns by providing the necessary cash flow to sustain operations Firms are advised to maintain a higher proportion of current assets such as cash, inventory, and receivables that can be quickly converted to cash Optimizing inventory levels to avoid overstocking while maintaining sufficient stock to meet customer demand is critical Implementing advanced inventory management systems can provide real-time tracking and efficient stock management Strengthening credit policies and procedures to ensure timely receivable collection, offering early payment discounts, and implementing strict credit terms can significantly improve cash flow
Periodically reviewing the asset portfolio to identify and liquidate non-essential or underperforming fixed assets can release funds for reallocation to current assets This strategic approach not only improves liquidity but also enhances financial flexibility and operational efficiency
Practical examples from Vietnamese retail businesses like BTT, CMV, DKC, and HTM, which faced liquidity challenges due to significant investments in fixed assets for expansion, demonstrate the importance of efficient current asset management For instance, BTT and CMV struggled with overstocking issues that tied up significant capital in inventory, while DKC and HTM faced delays in receivables collection, impacting their cash flow By adopting these recommendations, these companies can improve short-term payment capabilities, mitigate financial risks, and enhance competitive strength in the market, ensuring sustained growth and stability in a dynamic retail environment.
LIMITATION AND FUTURE RESEARCH DIRECTION
This study on the determinants of corporate liquidity in Vietnam's retail industry focuses primarily on businesses in the consumer goods and electronic equipment sectors This limited scope may not fully capture the diverse factors influencing liquidity across other retail sectors Additionally, the sample size, consisting mainly of small and medium-sized enterprises (SMEs), may not provide a comprehensive view applicable to larger firms with different financial dynamics
Future studies should aim to expand the scope by including a broader range of retail sectors and a larger sample size, particularly firms with larger capitalizations This approach will provide a more holistic understanding of the determinants of liquidity across the retail industry Regular financial analysis is essential for monitoring the impact of firm size, debt levels, asset utilization, and asset structure on liquidity Utilizing these insights to make informed strategic decisions can enhance the robustness of future research
Developing comprehensive financial plans that include liquidity forecasts and risk management strategies will allow businesses to anticipate and prepare for potential liquidity challenges Additionally, investing in financial management technologies such as ERP systems, automated accounting software, and data analytics tools can significantly improve financial monitoring, reporting, and decision-making capabilities Future studies could investigate the impact of these technologies on liquidity management and overall financial performance in the retail industry
Few empirical studies have examined the determinants of corporate liquidity in Vietnam's retail industry This research attempts to bridge a current gap in the literature For example, SMEs like DHT have demonstrated that strategic financial planning and technology investment can improve liquidity management Expanding future research to include more diverse and larger samples will contribute to a deeper and more comprehensive understanding of liquidity management in the Vietnamese retail sector from 2015 to 2023
Chapter 5 synthesizes factors affecting the capital structure of real estate enterprises listed on the Vietnam Stock Exchange The author pointed out the limitations that the research topic still lacks in the data processing process The project has encountered several limitations, including incomplete data updates and research time lags caused by firms still in the data completion phase Furthermore, the study incorporates endogenous variables and solely concentrates on crucial capital structure factors without specifically examining the ratio of short-term debt to total assets or the ratio of long-term debt to total assets As a result, certain factors were unable to accurately explain cause and effect in the regression model Based on the research results presented in Chapter 4, the author has identified some inadequacies and provided recommendations for firms and state agencies
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1 AFX An Giang Agriculture and Foods
2 AST Taseco Air Services JSC HOSE
3 BTT Ben Thanh Trading & Service Joint
4 CMV Ca Mau Trading Joint Stock
5 COM Materials - Petroleum Joint Stock
6 CTF City Auto Corporation HOSE
7 DHT Ha Tay Pharmaceutical Joint Stock
8 DKC Lang Son Market Joint Stock
9 HTM Hanoi Trade Joint Stock Corporation UPCOM
10 HTT Ha Tay Trading Joint Stock
11 KGM Kien Giang Import & Export Joint
12 LBC Long Bien Joint Stock Company UPCOM
13 PET Petrovietnam General Services JSC
14 PIT Petrolimex International Trading Joint
16 SAS Southern Airports Services JSC UPCOM
17 SBV Siam Brothers Vietnam JSC HOSE
18 SFC SaiGon Fuel Joint Stock Company HOSE
19 SVC Saigon General Service Corporation HOSE
20 TTH Tien Thanh Service and Trading Joint
Variable Obs Mean Std dev Min Max
sum LIQ SIZE DR ROA AS INF GDP
LIQ SIZE DR ROA AS INF GDP
corr LIQ SIZE DR ROA AS INF GDP
Testing multicollinearity for Pooled OLS
LIQ Coefficient Std err t P>|t| [95% conf interval]
Source SS df MS Number of obs = 180
reg LIQ SIZE DR ROA AS INF GDP
Heteroskedasticity test for Pooled OLS
Serial correlation test for Pooled OLS
Cameron & Trivedi's decomposition of IM-test
Wooldridge test for autocorrelation in panel data
xtserial LIQ SIZE DR ROA AS INF GDP
F test that all u_i=0: F(19, 154) = 3.80 Prob > F = 0.0000 rho 31440879 (fraction of variance due to u_i) sigma_e 58823678 sigma_u 3983519
LIQ Coefficient Std err t P>|t| [95% conf interval] corr(u_i, Xb) = -0.2527 Prob > F = 0.0000
Group variable: COMPANY Number of groups = 20
Fixed-effects (within) regression Number of obs = 180
xtreg LIQ SIZE DR ROA AS INF GDP, fe
REM regression results rho 28754818 (fraction of variance due to u_i) sigma_e 58823678 sigma_u 37370571
LIQ Coefficient Std err z P>|z| [95% conf interval] corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000
Group variable: COMPANY Number of groups = 20
Random-effects GLS regression Number of obs = 180
xtreg LIQ SIZE DR ROA AS INF GDP, re
Hausman test for FEM and REM
Serial correlation test for REM
Test of H0: Difference in coefficients not systematic
B = Inconsistent under Ha, efficient under H0; obtained from xtreg. b = Consistent under H0 and Ha; obtained from xtreg.
SIZE 2774745 3346334 -.0571589 2787674 fe re Difference Std err.
Wooldridge test for autocorrelation in panel data
xtserial LIQ SIZE DR ROA AS INF GDP
LIQ[COMPANY,t] = Xb + u[COMPANY] + e[COMPANY,t]
Breusch and Pagan Lagrangian multiplier test for random effects
Model misspecification test by the GLS method
LIQ Coefficient Std err z P>|z| [95% conf interval]
Estimated autocorrelations = 0 Number of groups = 20
Estimated covariances = 20 Number of obs = 180
Cross-sectional time-series FGLS regression
xtgls LIQ SIZE DR ROA AS INF GDP, panels(h)
Summary of 4 estimated regression methods
estab pool fe re gls, star (* 0.10 ** 0.05 *** 0.01) nonumbers mtitles (“POOL” “FEM” “REM” “GLS”)
esttab pool fe re gls, star(* 0.10 ** 0.05 *** 0.01) nonumbers mtitles("POOL" "FEM" "REM" "GLS")
MaCK Nam LIQ SIZE DR ROA AS INF GDP
AFX 2015 128,12% 11,8728 52,05% 0,10% 66,60% 0,63% 6,98% AFX 2016 172,15% 11,7187 33,46% 1,43% 57,35% 2,67% 6,69% AFX 2017 703,44% 11,6060 8,03% 5,72% 55,67% 3,52% 6,94% AFX 2018 252,53% 11,6952 26,56% 1,21% 66,84% 3,54% 7,46% AFX 2019 286,52% 11,6752 24,25% 0,03% 69,28% 2,79% 7,35% AFX 2020 500,35% 11,6389 13,30% 4,34% 66,01% 3,22% 2,86% AFX 2021 186,67% 11,8079 37,97% 3,27% 70,88% 1,83% 2,56% AFX 2022 150,04% 12,0391 61,14% 2,62% 91,67% 3,16% 8,01% AFX 2023 139,09% 12,0626 61,06% 2,30% 84,88% 3,25% 5,05% AST 2015 127,91% 10,7061 46,44% 2,16% 59,40% 0,63% 6,98% AST 2016 144,04% 11,5659 45,18% 11,86% 51,44% 2,67% 6,69% AST 2017 196,33% 11,7969 25,09% 23,65% 42,66% 3,52% 6,94% AST 2018 178,17% 11,8235 16,68% 24,51% 29,73% 3,54% 7,46% AST 2019 203,04% 11,9414 26,28% 24,31% 53,37% 2,79% 7,35% AST 2020 434,43% 11,7959 13,55% -8,25% 58,13% 3,22% 2,86% AST 2021 322,38% 11,7012 17,92% -25,57% 56,03% 1,83% 2,56% AST 2022 302,99% 11,7626 21,32% 5,84% 60,46% 3,16% 8,01% AST 2023 303,69% 11,8815 22,79% 19,78% 68,35% 3,25% 5,05% BTT 2015 236,99% 11,5725 23,50% 10,24% 33,59% 0,63% 6,98% BTT 2016 167,81% 11,6217 30,46% 7,44% 22,60% 2,67% 6,69% BTT 2017 217,36% 11,6613 32,22% 8,40% 27,18% 3,52% 6,94% BTT 2018 214,26% 11,6909 31,92% 10,73% 28,35% 3,54% 7,46% BTT 2019 220,30% 11,7289 31,72% 10,01% 26,83% 2,79% 7,35% BTT 2020 274,94% 11,6947 26,48% 3,40% 17,00% 3,22% 2,86% BTT 2021 535,54% 11,6656 22,69% 1,05% 19,03% 1,83% 2,56% BTT 2022 489,70% 11,6729 22,39% 3,76% 26,02% 3,16% 8,01% BTT 2023 366,88% 11,7062 23,71% 9,72% 32,32% 3,25% 5,05% CTF 2015 124,62% 11,6155 51,25% 2,93% 63,06% 0,63% 6,98% CTF 2016 112,94% 11,8322 66,62% 4,10% 74,73% 2,67% 6,69% CTF 2017 111,87% 11,8974 70,88% 2,88% 79,12% 3,52% 6,94% CTF 2018 111,39% 11,9849 72,34% 5,87% 80,32% 3,54% 7,46% CTF 2019 117,82% 12,1895 65,58% 2,84% 77,05% 2,79% 7,35% CTF 2020 111,37% 12,1983 66,28% 0,09% 61,33% 3,22% 2,86% CTF 2021 135,43% 12,2460 53,93% 2,94% 67,89% 1,83% 2,56% CTF 2022 117,68% 12,4026 63,40% 4,65% 68,62% 3,16% 8,01% CTF 2023 116,74% 12,5632 70,62% 1,20% 70,87% 3,25% 5,05% CMV 2015 115,61% 11,7929 72,79% 5,06% 83,85% 0,63% 6,98% CMV 2016 112,76% 11,8401 75,07% 3,90% 84,60% 2,67% 6,69% CMV 2017 113,84% 11,8260 74,03% 3,62% 84,20% 3,52% 6,94% CMV 2018 112,40% 11,8520 74,71% 3,14% 83,97% 3,54% 7,46% CMV 2019 111,15% 11,8654 75,19% 2,50% 83,33% 2,79% 7,35% CMV 2020 120,00% 11,7822 67,34% 3,05% 80,72% 3,22% 2,86% CMV 2021 162,86% 11,6747 46,88% 6,03% 76,20% 1,83% 2,56% CMV 2022 159,31% 11,6890 48,74% 4,91% 77,50% 3,16% 8,01% CMV 2023 146,19% 11,7250 53,88% 2,79% 78,21% 3,25% 5,05% COM 2015 338,46% 11,6526 11,94% 22,25% 39,39% 0,63% 6,98% COM 2016 265,19% 11,7239 19,68% 20,72% 48,37% 2,67% 6,69%
MaCK Nam LIQ SIZE DR ROA AS INF GDP
COM 2017 478,70% 11,7238 11,84% 17,96% 44,80% 3,52% 6,94% COM 2018 580,73% 11,7552 11,56% 15,86% 48,46% 3,54% 7,46% COM 2019 373,57% 11,7121 13,71% 9,90% 39,99% 2,79% 7,35% COM 2020 433,17% 11,6978 11,23% 7,43% 36,37% 3,22% 2,86% COM 2021 467,97% 11,7284 11,74% 7,51% 42,61% 1,83% 2,56% COM 2022 303,19% 11,7210 17,36% 0,24% 44,49% 3,16% 8,01% COM 2023 391,86% 11,6904 10,41% 7,03% 40,78% 3,25% 5,05% DHT 2015 141,33% 11,6400 61,02% 9,27% 85,40% 0,63% 6,98% DHT 2016 146,96% 11,7109 60,63% 11,14% 88,13% 2,67% 6,69% DHT 2017 151,54% 11,8026 59,95% 12,15% 89,90% 3,52% 6,94% DHT 2018 158,10% 11,8056 56,82% 13,29% 88,73% 3,54% 7,46% DHT 2019 151,49% 11,8682 57,72% 12,19% 86,43% 2,79% 7,35% DHT 2020 129,70% 11,9638 62,00% 10,45% 79,71% 3,22% 2,86% DHT 2021 210,25% 12,0926 39,13% 5,77% 81,39% 1,83% 2,56% DHT 2022 179,52% 12,1672 45,64% 6,73% 68,79% 3,16% 8,01% DHT 2023 169,24% 12,2644 41,95% 4,84% 54,87% 3,25% 5,05% DKC 2015 261,15% 10,5109 22,72% 3,21% 59,18% 0,63% 6,98% DKC 2016 259,49% 10,5031 21,74% 2,84% 56,24% 2,67% 6,69% DKC 2017 237,73% 10,5228 24,38% 3,55% 57,85% 3,52% 6,94% DKC 2018 226,16% 10,5269 24,94% 3,82% 56,31% 3,54% 7,46% DKC 2019 243,74% 10,5235 24,31% 4,04% 59,19% 2,79% 7,35% DKC 2020 270,03% 10,4608 20,73% -5,07% 55,89% 3,22% 2,86% DKC 2021 298,76% 10,5360 21,90% 11,43% 65,39% 1,83% 2,56% DKC 2022 310,19% 10,5841 24,35% 11,37% 70,40% 3,16% 8,01% DKC 2023 316,96% 10,6014 23,41% 13,57% 72,78% 3,25% 5,05% HTM 2015 117,66% 12,7019 57,71% 0,44% 50,53% 0,63% 6,98% HTM 2016 123,54% 12,6572 52,15% 1,04% 48,35% 2,67% 6,69% HTM 2017 133,54% 12,6261 43,67% 0,86% 47,31% 3,52% 6,94% HTM 2018 135,46% 12,6215 42,00% 0,79% 45,87% 3,54% 7,46% HTM 2019 165,73% 12,5303 31,46% 3,55% 43,63% 2,79% 7,35% HTM 2020 362,07% 12,4862 25,57% 0,00% 37,74% 3,22% 2,86% HTM 2021 309,60% 12,4808 24,75% -0,06% 35,46% 1,83% 2,56% HTM 2022 320,22% 12,4699 23,28% -0,44% 34,26% 3,16% 8,01% HTM 2023 174,66% 12,5153 31,81% -0,92% 40,92% 3,25% 5,05% HTT 2015 81,66% 11,7018 73,06% 1,32% 13,09% 0,63% 6,98% HTT 2016 194,03% 11,6720 52,96% 2,74% 28,55% 2,67% 6,69% HTT 2017 65,53% 11,5988 43,67% 0,65% 23,93% 3,52% 6,94% HTT 2018 29,09% 11,4671 31,81% -8,10% 9,15% 3,54% 7,46% HTT 2019 32,61% 11,4519 34,29% -4,91% 11,02% 2,79% 7,35% HTT 2020 54,03% 11,4118 38,58% -5,09% 20,81% 3,22% 2,86% HTT 2021 41,14% 11,3614 35,79% -4,77% 14,68% 1,83% 2,56% HTT 2022 36,43% 11,3406 37,23% -4,59% 13,56% 3,16% 8,01% HTT 2023 27,29% 11,3144 39,93% -6,60% 9,28% 3,25% 5,05% KGM 2015 90,51% 11,7943 61,56% -2,25% 55,19% 0,63% 6,98% KGM 2016 93,99% 11,9868 73,27% 1,48% 68,70% 2,67% 6,69% KGM 2017 105,91% 12,0229 72,92% 2,89% 72,59% 3,52% 6,94% KGM 2018 99,56% 12,0225 74,56% 1,23% 73,81% 3,54% 7,46%
MaCK Nam LIQ SIZE DR ROA AS INF GDP
KGM 2019 100,78% 12,0124 75,02% 0,13% 75,34% 2,79% 7,35% KGM 2020 108,10% 11,9593 69,16% 2,61% 74,54% 3,22% 2,86% KGM 2021 109,46% 11,9623 70,47% 1,51% 77,14% 1,83% 2,56% KGM 2022 108,57% 12,0036 73,81% 0,56% 79,84% 3,16% 8,01% KGM 2023 110,53% 12,0242 74,36% 1,18% 81,87% 3,25% 5,05% LBC 2015 142,30% 10,6228 55,75% 7,40% 78,53% 0,63% 6,98% LBC 2016 117,95% 10,6547 55,87% 6,64% 65,21% 2,67% 6,69% LBC 2017 128,14% 10,6410 52,25% 6,26% 66,08% 3,52% 6,94% LBC 2018 209,27% 10,6608 35,35% 6,06% 72,65% 3,54% 7,46% LBC 2019 213,86% 10,7075 37,27% 7,62% 78,52% 2,79% 7,35% LBC 2020 259,30% 10,7128 32,34% 8,97% 82,55% 3,22% 2,86% LBC 2021 230,64% 10,7503 32,63% 8,43% 74,02% 1,83% 2,56% LBC 2022 227,31% 10,8126 35,71% 8,45% 80,06% 3,16% 8,01% LBC 2023 245,23% 10,8528 34,03% 9,80% 82,30% 3,25% 5,05% PET 2015 134,53% 12,7114 69,40% 4,12% 88,29% 0,63% 6,98% PET 2016 114,01% 12,7943 73,54% 2,68% 76,14% 2,67% 6,69% PET 2017 116,76% 12,7905 73,10% 2,33% 77,54% 3,52% 6,94% PET 2018 121,75% 12,7453 70,87% 2,34% 77,85% 3,54% 7,46% PET 2019 123,61% 12,6960 66,97% 2,56% 74,81% 2,79% 7,35% PET 2020 116,01% 12,8008 73,69% 2,22% 80,42% 3,22% 2,86% PET 2021 112,26% 12,9291 77,16% 3,67% 84,35% 1,83% 2,56% PET 2022 113,06% 12,9561 77,18% 1,85% 85,80% 3,16% 8,01% PET 2023 114,07% 12,9768 76,94% 1,47% 87,13% 3,25% 5,05% PIT 2015 110,88% 11,9085 77,05% -0,98% 85,38% 0,63% 6,98% PIT 2016 115,61% 11,8480 72,51% 1,12% 81,30% 2,67% 6,69% PIT 2017 104,83% 11,7836 75,88% -7,77% 77,40% 3,52% 6,94% PIT 2018 100,09% 11,5917 71,30% -7,72% 67,67% 3,54% 7,46% PIT 2019 106,21% 11,4726 60,00% 2,26% 62,66% 2,79% 7,35% PIT 2020 110,54% 11,5226 63,79% 0,56% 69,61% 3,22% 2,86% PIT 2021 114,92% 11,5180 65,06% -1,72% 73,74% 1,83% 2,56% PIT 2022 128,80% 11,4353 56,57% 1,16% 71,62% 3,16% 8,01% PIT 2023 124,86% 11,4563 61,75% -3,13% 75,95% 3,25% 5,05% PSD 2015 110,69% 12,3731 90,17% 2,86% 99,80% 0,63% 6,98% PSD 2016 109,16% 12,4185 90,62% 2,35% 98,86% 2,67% 6,69% PSD 2017 112,27% 12,3927 88,15% 2,82% 98,93% 3,52% 6,94% PSD 2018 115,57% 12,3403 85,61% 2,91% 98,94% 3,54% 7,46% PSD 2019 119,82% 12,2907 82,62% 2,36% 98,98% 2,79% 7,35% PSD 2020 116,16% 12,3839 85,52% 1,90% 99,29% 3,22% 2,86% PSD 2021 116,14% 12,4127 83,39% 5,36% 96,76% 1,83% 2,56% PSD 2022 114,56% 12,5354 85,10% 3,29% 97,46% 3,16% 8,01% PSD 2023 118,29% 12,4937 82,44% 2,03% 97,43% 3,25% 5,05% SAS 2015 159,18% 12,2995 33,43% 0,58% 53,21% 0,63% 6,98% SAS 2016 207,83% 12,3062 27,95% 11,57% 57,79% 2,67% 6,69% SAS 2017 217,24% 12,3324 28,25% 13,50% 59,63% 3,52% 6,94% SAS 2018 203,35% 12,3451 30,48% 15,41% 61,91% 3,54% 7,46% SAS 2019 196,10% 12,3706 32,41% 15,87% 63,12% 2,79% 7,35% SAS 2020 340,81% 12,2567 16,30% 8,28% 55,03% 3,22% 2,86%