The impact of liquidity and other factors on firm performance of manufacturing industry... To help business managers better manage their companies and shareholders gain deeper understand
The Index Industrial for Production 2019-2023
Necessity of the thesis
Corporate operational efficiency is crucial for managers and investors alike, as it significantly impacts overall business performance Numerous factors play a vital role in improving operational efficiency, ultimately enhancing a company's success.
These factors necessitate astute analysis and measurement by seasoned management to craft optimal strategies
In the wake of the Covid-19 crisis, both the global and Vietnamese economies are experiencing recovery and growth; however, many businesses are still facing challenges such as resource shortages and a lack of resilience This dissertation aims to examine how liquidity affects operational effectiveness and to identify measurable metrics related to this impact.
Current research, both domestically and internationally, highlights the diverse impact of liquidity on economic events across different countries This variation emphasizes the need to investigate how liquidity influences corporate operations in Vietnam, especially in high-growth sectors such as manufacturing Although these industries currently contribute minimally to the national GDP, they demonstrate slower growth compared to other sectors, indicating a potential area for further exploration.
The thesis explores the factors influencing liquidity and its effect on the performance of manufacturing firms in Vietnam, aiming to assist business managers in effectively managing their companies and providing shareholders with a clearer understanding of their operations.
Objective and Scope of Study
This thesis examines how various liquidity factors, including average inventory turnover period, average accounts receivable collection period, average accounts payable payment period, and cash conversion cycle, influence key performance indicators such as return on assets, return on equity, quick ratio, inventory turnover ratio, current ratio, and operating cash ratio, ultimately leading to actionable recommendations.
With the aforementioned research objective, the author delineates the following issues necessitating explication in this study:
(1) Reviewing the theoretical basis on the factors that impact of liquidity on firm performance of manufacturing industry
(2) Measuring the impact of factors on liquidity on firm performance of manufacturing corporates in Viet Nam
(3) Proposing solution based on the model’s conclusion to decrease the impact of the factors in the future
This article employs Stata17 software to analyze the influence of liquidity on corporate sector performance by comparing several statistical models, including the Pearson correlation matrix, ordinary least squares regression (OLS), random effects model (REM), and fixed effects model (FEM).
This article examines manufacturing companies listed on the Ho Chi Minh Stock Exchange (HOSE) and analyzes data from 2020 to 2023.
Research Structure
The structure of the research included 5 chapters, namely:
Chapter 2: Theoretical overview on the impact of liquidity on firm performance
Chapter 3: Research methods and research data:
Chapter 4: Model of testing the impact of liquidity on firm performance of manufacturing industry: Chapter 5: Conclusion and some recommendations of liquidity management and enhancing the manufacturing industry’s firm performance:
THEORETICAL OVERVIEW ON THE IMPACT OF LIQUIDITY ON
Oversea researches
Liquidity is a critical factor in business research, defined as the ease and speed with which assets can be converted into cash without significant loss in value Highly liquid assets can be sold quickly and are often in demand, making them attractive for companies looking to minimize risk and ensure smooth operations These assets constitute a substantial portion of a company's holdings, playing a vital role in effective financial management.
This article examines the payment capabilities of businesses, highlighting their importance for modern enterprises It evaluates whether investing in highly liquid assets is more beneficial than opting for less liquid alternatives Additionally, it addresses the ongoing debate among experts regarding the reasons firms maintain excess liquidity, with some suggesting it allows for seizing profitable investment opportunities, while others argue it serves as a safeguard against unforeseen contingencies.
One of the most influential studies on liquidity theory is John Maynard Keynes's "The General Theory of Employment, Interest, and Money" (1936), where he identifies three primary reasons for individuals to hold cash: transactions, precautionary, and speculative motives Keynes's insights have laid the foundation for extensive research by various authors in the field, solidifying the importance of understanding liquidity in economics.
A study by CW Waswa, MS Mukras, and D Oima (2018) examined the effect of liquidity on the financial performance of Kenya's sugar industry, revealing that a company's capacity to meet short-term debts with liquid assets is crucial, yet excessive liquidity can impair performance The findings indicate that simply having ample cash does not equate to financial strength, and insufficient ability to cover current debts adversely impacts firm performance Furthermore, the research highlights that many companies in the Kenyan sugar sector face challenges such as low or negative cash flow, high debt levels, and ineffective asset and liability management strategies, all contributing to poor financial outcomes.
A study by Kariuki, Muturi, and Njeru (2021) analyzed the impact of liquidity on the financial performance of insurance companies in Kenya, revealing that liquidity becomes a critical concern during major disasters when claims surge To mitigate these risks, insurance firms adopt strategies like additional coverage and risk diversification The research also established a positive relationship between liquidity and key financial performance indicators, including Return on Assets (ROA) and Return on Equity (ROE) Notably, liquidity is viewed as a lesser threat in life insurance compared to the banking sector, due to the more frequent monetary transactions in banking operations.
A study by Farooq, Omar, and Fatima Zahra Bouaich (2012) analyzed the relationship between liquidity and firm performance in the MENA region during the global economic recession from 2007 to 2009 The findings revealed that higher liquidity levels correlate with enhanced firm performance, as investors favor stocks that offer comprehensive information, thereby reducing perceived risks associated with managerial misconduct This preference allows investors to sell their stocks quickly if they suspect unethical behavior, resulting in lower risks and higher stock prices for more liquid stocks Furthermore, the research highlights that in civil law countries, where investor protections and governance are typically weaker, liquidity becomes even more critical for investors concerned about potential managerial exploitation.
A study by Yameen, Farhan, and Tabash (2019) analyzed the impact of liquidity on the profitability of pharmaceutical companies listed on the Bombay Stock Exchange (BSE) The researchers utilized the current ratio and quick ratio as liquidity measures, while also considering other factors such as leverage, company size, and age, which were found to negatively influence profits Notably, the study revealed that, despite the adverse effects of leverage, company size, and age on profitability, both the current ratio and quick ratio demonstrated a significant positive correlation with return on assets.
A study by R Charmler et al (2018) highlights a positive correlation between liquidity and the performance of commercial banks in Ghana, particularly noting a stronger link with return on assets than return on equity However, a negative correlation was observed between liquidity assets and interest-bearing liabilities with return on equity, despite this association being statistically insignificant In contrast, Lartey et al (2013) found a weak positive relationship between bank liquidity and profitability among listed banks in Ghana from 2005 to 2010, attributing this correlation to increased liquidity reducing the risk of financial crises and enhancing banks' resilience to unexpected disruptions Additionally, T Wuave et al (2020) examined liquidity management's impact on the financial performance of banks in Nigeria, revealing that the loan-to-deposit ratio and cash reserve ratio negatively and significantly affect financial performance, as measured by return on assets, return on equity, and net interest margin.
Abubakar et al (2023) analyzed liquidity risk among non-financial firms on the Nigerian Stock Exchange by examining the standard deviation of the quick ratio, finding no significant correlation between liquidity risk and firm performance However, they identified a notable negative impact of the current ratio on firm performance Effective management of current assets, such as inventories and cash, alongside short-term debts like loans and payables, is essential for mitigating liquidity risks Ultimately, the study utilized the standard deviation of the current ratio to assess firms' challenges in meeting their short-term financial obligations.
A study by Bambang Sudiyatno and Titiek Suwarti (2022) examined 123 manufacturing companies in Indonesia between 2019 and 2021, revealing that liquidity negatively impacts firm performance Their research suggests that higher liquidity levels may hinder overall performance in these companies.
The Covid-19 pandemic has significantly affected the global economy, yet comprehensive research assessing its overall economic impact remains limited A study by S Devi et al (2020) delves into this issue, providing valuable insights into the economic ramifications of the crisis.
The COVID-19 pandemic significantly affected the financial performance of firms listed on the Indonesia Stock Exchange, revealing a decline in both leverage and activity ratios However, public companies experienced an increase in liquidity and profitability ratios during this period While the liquidity and leverage ratios showed no significant differences overall, a notable disparity emerged between public companies' performance before and during the pandemic regarding these ratios.
A study by Sonal Kumar and Leila Zbib (2022) titled "Firm performance during the Covid-19 crisis: Does managerial ability matter?" reveals that companies led by CEOs with high managerial ability exhibit greater resilience and superior performance during crises, attributed to their enhanced liquidity and larger cash reserves Furthermore, the research indicates that firms under the guidance of skilled CEOs achieved higher raw and cumulative abnormal returns during the Covid-19 pandemic compared to those with less capable leadership.
Shiwei Hu and Yuyao Zhang (2021) conducted research on the impact of the Covid-19 pandemic on firm performance, revealing that a firm's return on assets (ROA) is negatively correlated with the number of cumulative Covid-19 cases Their findings indicate that countries with superior healthcare systems, robust financial infrastructures, and effective governance are better positioned to succeed during the pandemic.
In their 2021 study, "The Impact of Covid-19 on the Financial Performance of PN17 and GN3 Status Firms: Does it Add Salt Into the Wound?", W Shahimi et al explore how liquidity and financial leverage influence the financial performance of distressed firms in Malaysia during the pandemic The findings indicate that liquidity and financial leverage significantly impact these firms' performance, prompting managers to prioritize these factors The study advises managers to carefully assess their liquidity and financial leverage while managing financially distressed firms, suggesting that the pandemic's effects should be of lesser concern.
In Vietnam researches
Recent research in Vietnam has built upon previous foreign studies to examine the impact of liquidity on corporate efficiency A notable study by Ho et al (2020) found a significant positive relationship between liquidity and financial performance, evidenced by a P-value coefficient of 0.001, which is below the 0.05 threshold However, the study also highlights that excessively high liquidity levels, such as holding large amounts of cash or maintaining high inventory, can lead to resource wastage and decreased operational efficiency, as capital remains uninvested.
Truong Hong Trinh et al (2016) identified profitability and capital expenditure as key factors influencing corporate liquidity Their research revealed significant statistical relationships between cash holdings and the cash conversion cycle with both profitability and capital expenditure Specifically, they found that profitability is positively linked to cash holdings, while it negatively impacts the cash conversion cycle, especially within the real estate sector Profitable firms in this industry are inclined to allocate more cash for future opportunities and unexpected events, while simultaneously minimizing investments in working capital due to their ability to negotiate advantageous payment terms.
Nguyen Thi Ngoc Lan and Nguyen Van Cong (2020) in their study "The Determinants of Profitability in Listed Enterprises: A Study from the Vietnamese Stock Exchange" found that firm size significantly positively influences Return on Assets (ROA), while its impact on Return on Sales (ROS) is minimal Liquidity positively affects both ROA and Return on Equity (ROE) but negatively impacts other profitability ratios such as ROS and solvency The research suggests that larger enterprises in Vietnam tend to achieve better profitability, and companies should avoid relying on debt for operational capital, as this can lead to negative consequences for ROE and ROS, potentially increasing the risk of bankruptcy.
Cuong Thanh Nguyen et al (2021) conducted a study titled "Stock Market Returns and Liquidity During the COVID-19 Outbreak: Evidence from the Financial Services Sector in Vietnam," examining the impact of the Vietnamese government's response to the pandemic on listed financial services companies Their research reveals a significant negative correlation between the rise in daily COVID-19 cases and stock returns prior to lockdowns, although the effect on market liquidity during this period is minimal Interestingly, the study highlights a notable positive effect of COVID-19 lockdowns on the stock returns and liquidity of Vietnamese banking, finance, and insurance firms, suggesting that these measures bolstered investor confidence in the government's effective disease control and containment strategies during lockdowns.
A recent study by Thuy Thi Cam Nguyen et al (2024) examined 100 leading financial companies in Vietnam, revealing a significant positive impact of liquidity on profitability The research also found a notable negative relationship between financial leverage and profitability Furthermore, it highlighted that asset growth rate and asset structure positively influence corporate liquidity, while the debt ratio adversely affects it Other independent variables were found to have no correlation with corporate liquidity.
This research focuses on the impact of liquidity on corporate efficiency in Vietnam, addressing a significant gap in existing literature By building on previous findings, it aims to offer valuable insights that will benefit both academics and practitioners in the field.
Introduction to liquidity and firm performance
Liquidity refers to how quickly an asset can be converted into cash, particularly in the context of a company's ability to meet its current liabilities This capability is typically assessed through various financial ratios A company's liquidity is determined by its ability to use current assets—such as cash, inventory, accounts receivable, securities, and short-term assets—to satisfy short-term obligations.
Cash is the most liquid asset, easily convertible into investments like stocks and real estate Unlike other assets that may take time to sell, cash can be quickly spent or exchanged without additional steps, making it the most accessible form of wealth.
In stable markets, short-term asset prices are less affected by market fluctuations, often due to inflation, leading to increased liquidity This heightened liquidity arises from the difficulty in preserving asset value, making it crucial for investors to navigate these conditions effectively.
As the market continues to expand significantly, scholars have developed new interpretations to align with the economic changes of recent decades, leading to the emergence of various asset categories in financial markets Notably, the previously vague concept of capital has been clarified through the term "market liquidity." Evaluating liquidity involves analyzing factors such as stock market activity.
Transaction costs in stock markets are influenced by the difference between buying and selling prices Stocks characterized by high liquidity show small discrepancies between these prices, reflecting their strong market presence In contrast, when the bid price exceeds the selling price, it indicates a lower liquidity ratio for the stock.
The stock market boasts immense breadth, with no restrictions on transaction volume or price, enabling traders to engage in transactions of any desired quantity and value
The market's ability to recover from trading shocks is deemed reliable due to its inherent mechanisms for rebalancing any inflicted damage This resilience significantly influences market liquidity across various global markets, enabling investors and managers to assess asset liquidity effectively By conducting financial asset transactions within legal frameworks and ensuring transparency, fairness among investors is upheld, alongside a commitment to providing verified information about the traded assets and their issuers.
Liquidity plays a significant role in the successful functioning of a business firm (Priya,
After extensive research, it has been determined that the liquidity of a business firm is primarily influenced by two key factors: short-term assets (STAs) and long-term assets (LTAs).
Short-term assets encompass cash, cash equivalents, and assets that can be quickly converted to cash or sold within a year or the normal operating cycle According to the Operate Finance of Banking Academy (2019), these assets collectively represent the value of cash, cash equivalents, short-term financial investments, accounts receivable, inventory, and other assets with short conversion periods at the time of reporting.
Cashes and cash equivalents are vital components of short-term assets, encompassing various forms of monetary funds This includes cash on hand, such as Vietnamese currency, checks, receipts, treasury bills, and precious metals, as well as cash held in bank accounts These assets are listed under current assets on the balance sheet Additionally, cash in transit refers to money being securely transported between accounts Cash equivalents are short-term investment securities with maturities of three months or less, characterized by high liquidity, allowing for easy conversion to cash, although they carry risks due to potential price fluctuations linked to interest rate changes.
Account receivables encompass various accounting transactions related to amounts owed to a business, including customer receivables, internal receivables, and pledged deposits These receivables provide valuable insights into the payment status of obligations resulting from the business's production and commercial activities.
Inventories consist of tangible assets owned by a business, essential for manufacturing, commercial activities, or service delivery These assets include raw materials, primary and auxiliary materials, labor tools, packaging, finished goods, and work-in-progress items intended for sale or operational use Evaluating inventory is crucial, as it significantly influences the assessment of a business's revenue generation and overall financial performance.
Short-term financial investments refer to business investments with a maturity period of no more than 12 months from the reporting date These investments include securities held for business purposes, held-to-maturity investments, loans, leases, joint venture capital contributions, and trading of various securities Their overall value is adjusted for depreciation provisions, highlighting their significance in a company's financial strategy.
Other short-term assets refer to those that do not fit into the previously defined categories, encompassing items with a recovery or utilization period of 12 months or less from the reporting date This includes short-term prepaid expenses, deductible VAT, tax receivables, repurchase transactions of government bonds, and various other short-term assets recorded at the reporting date.
Long-term assets, defined by their recovery or utilization period exceeding 12 months, include various types such as long-term receivables, fixed assets, investment real estate, and long-term financial accounts These assets are vital for enhancing an organization's financial health and stability, acting as lasting investments with extended timelines for return or use.
Long-term receivables include amounts owed by customers and prepayments made to sellers and suppliers, representing the business's rights to collect debts over a period exceeding 12 months.
Firm performance
Firm performance is influenced not only by a company's internal efficiency but also by the market environment in which it operates Key financial metrics for evaluating a company's performance include revenue, return on equity, return on assets, profit margin, sales growth, capital adequacy, liquidity ratio, and stock prices In the manufacturing sector, critical ratios to monitor include total unit sales, return on assets, and inventory turnover.
1.4.2 Criteria for valuating the performance of a firm
Return on assets (ROA) is a key metric that indicates a company's profitability in relation to its total assets, highlighting the efficiency with which the company utilizes its assets to generate earnings.
The formulas of ROA is:
A consistently high and stable Return on Assets (ROA) ratio indicates a company's efficient utilization of its assets and resource maximization Different industries have distinct asset structure requirements; for example, heavy industries such as metal and cement often necessitate substantial fixed assets, resulting in a lower ROA ratio In contrast, technology and consumer goods companies typically operate with fewer fixed assets, which can lead to a higher ROA ratio.
Return on Equity (ROE) is a financial ratio that measures of the profitability of a business in relation to its equity The ROE formula is calculated as:
The Return on Equity (ROE) ratio measures a company's efficiency in utilizing its capital, reflecting the profit generated for each unit of invested capital A higher ROE indicates better capital utilization; however, its interpretation varies by industry, as some sectors necessitate greater equity capital for effective operations.
Gross Profit Margin or Gross Margin (GPM) is a metric used to assess a firm’s financial heath GPM is calculated as the formula:
A high Gross Profit Margin (GPM) indicates a company's strong ability to generate profits from its operations, while a low GPM may signal potential challenges in profitability, prompting the need for strategies such as increasing prices or cutting costs However, GPM should be viewed in the context of broader economic conditions and industry trends, as these factors also play a crucial role in a company's overall performance.
Debt-to-equity ratio (D/E) indicated how much debt a company is using to finance its assets relative to the value of shareholders equity
It is found by the below formula:
A Debt-to-Equity (D/E) ratio greater than 1 indicates that a company's assets are primarily financed by external capital, while a ratio less than 1 shows that assets are mainly funded by the owners' equity Persistent high D/E ratios can signal potential difficulties in debt repayment and increased pressure from bank interest rates In contrast, a low D/E ratio reflects strong financial health, indicating ample equity capital and efficient business operations.
The debt-to-equity (D/E) ratio fluctuates across various industries and is affected by economic cycles and market conditions For instance, companies in the service industry typically exhibit lower D/E ratios compared to those in manufacturing sectors.
The D/E ratio is affected by various market factors, and its significance varies across different economic periods and industries.
Criteria for evaluating the operations of a company in the manufacturing sectors
Liquidity is crucial for manufacturing companies as it allows them to manage customer orders effectively and maintain optimal inventory levels Consequently, it is vital for these companies to assess various metrics beyond just financial indicators to ensure comprehensive performance evaluation.
Inventory Turnover ratio is a financial ratio showing how many times a company turned over its inventory relative to its cost of goods sold (COGS) in each period
It is found by the below formula:
A high inventory turnover ratio indicates that a business is selling its products quickly and effectively managing its inventory However, if the ratio is excessively high, the company may miss opportunities for product promotion and risk not having enough stock to meet customer demand.
A low inventory turnover ratio indicates that businesses are purchasing more inventory than actual demand, which can lead to excess stock This is particularly critical for products that are perishable or prone to becoming outdated, such as food, beverages, fashion items, and automobiles To avoid the risk of dead or obsolete inventory, companies dealing with these products must maintain a high inventory turnover ratio to ensure timely sales and minimize losses.
In summary, low-margin industries typically experience higher inventory turnover compared to high-margin industries, as they need to offset lower per-unit profits by achieving greater sales volumes.
Return on sales (ROS) is a key financial metric that evaluates a company's efficiency in generating profits relative to its revenue It reflects the percentage of profit earned from a company's operational activities compared to its total revenue, essentially measuring the profit generated for each unit of revenue.
The formulas of the return on sales equal to:
Net sales are influenced by the selling price and production costs of a business A high ratio indicates effective management of production costs or high selling prices, while a decrease may signal a loss of cost control or discounting strategies Managers can leverage Return on Sales (ROS) to assess the effectiveness of sales strategies, particularly in manufacturing, where it plays a crucial role in guiding marketing initiatives and optimizing profitability ROS is essential for aligning sales strategies with the organization's financial goals.
Large-scale enterprises, characterized by advanced technology and substantial production capacity, are positioned to achieve high profitability These companies benefit from increased opportunities to adopt innovative production technologies, diversify their business operations, and expand into various industries Their significant market presence allows them to produce a wide range of products and adapt to changes both domestically and internationally The size of a company can often be measured by the capitalized value of its shares in the capital market.
While enterprises with small-scales will still maintain with traditional production method
Due to a limited workforce and a focus on specific products, many companies struggle with extensive market research This is particularly evident in manufacturing, where larger enterprises tend to demonstrate significantly better performance outcomes.
Small-scale companies benefit from easier control over capital and human resource management, allowing for more effective operations In contrast, large-scale enterprises may struggle with management inefficiencies, potentially hindering their production and business effectiveness Therefore, manufacturing companies should aim for a balanced firm size to enhance product quality and better serve their customers.
Building a brand and reputation is a demanding process for businesses, requiring long-term effort and dedication Established companies in the manufacturing sector benefit from extensive operational histories, which enhance their reputation and streamline costs In contrast, information technology firms prioritize staying ahead in technology rather than the length of their operations Ultimately, manufacturers with a strong industry tradition leverage their experience to achieve higher profits and maintain a competitive edge.
Firm age can restrict the product portfolio diversity of large companies, particularly those with family traditions, leading them to prioritize process innovation These firms often hesitate to enhance their environments or diversify their offerings, as they assume that customers favor established products over new innovations.
In summary, the age of a firm plays a crucial role in its financial performance, affecting profitability both directly and indirectly The decisions made by managers regarding whether to maintain traditional practices or diversify in response to market trends significantly influence the company's financial outcomes.
Tangible assets are physical assets with a definite monetary value that can be easily transacted, unlike intangible assets, which possess theoretical value They are categorized into current and fixed assets, playing a crucial role in daily business operations and serving as collateral for loans In the manufacturing sector, tangible assets are particularly vital, as they help lower agency costs in capital structure decisions by providing easily collateralizable resources that enhance financial performance (Rajan & Zingales, 1995).
Manufacturing companies typically possess a higher proportion of tangible assets, primarily consisting of fixed assets and inventory According to the theory and the Vietnam Accounting Standard (VAS), the author employs specific formulas to calculate these tangible assets effectively.
Tangible assets play a crucial role in securing funding for students by facilitating access to external finances They offer a dependable method for valuing a company amidst asymmetric information Additionally, these assets enable the company to decide if any loss associated with goodwill should be reflected in the income statement.
The impact of liquidity and other factors on firm performance of
The connection between liquidity and operational efficiency in companies has garnered considerable attention from researchers, particularly following the economic impact of the Covid-19 pandemic This study evaluates corporate liquidity using four key indicators: current ratio, quick ratio, inventory turnover ratio, and operating cash ratio Notably, different research studies utilize various methodologies to analyze the relationship between these two crucial factors.
A significant study by Hong Thi Xuan Nguyen (2022) examined the impact of the Covid-19 pandemic on the financial performance of Vietnamese logistics enterprises, revealing that the pandemic has adversely affected operational efficiency, as indicated by a decline in the average Return on Assets (ROA).
Research on the impact of Covid-19 on liquidity reveals mixed results Shaharuddin et al (2021) analyzed the first two quarters of 2020 and found no significant effects of the pandemic on company operational efficiency, as measured by ROA and ROE In contrast, studies by Amnim et al (2021) and Rashata, H (2021) indicated that Covid-19 significantly affected various industry sectors, leading many companies to struggle with liquidity and, ultimately, exit the market.
A study by Xu et al (2022) investigated the effects of the COVID-19 pandemic on the financial performance and cash holdings of companies in China's agri-food sector The findings revealed that the pandemic did not significantly impact the operational efficiency of these firms.
Research indicates that liquidity significantly influences the operational efficiency of companies, particularly during major disasters In times of crisis, the role of liquidity becomes even more critical, as it directly affects how businesses navigate challenges and maintain their operations.
CHAPTER II: RESEARCH METHODS AND RESEARCH DATA
Reality of manufacturing industries in Viet Nam from 2019-2023
Chart 1: The Index Industrial for Production 2019-2023
(Source: General Statistic Office of Viet Nam)
In recent years, the manufacturing sector has emerged as a key driver of Vietnam's economy, with notable growth in areas such as electricity, electronics, information technology, textiles, and construction Prior to the impact of the Covid-19 pandemic, the Index of Industrial Production (IIP) in Vietnam demonstrated stable development from 2016 to 2019.
In 2020, the industrial sector faced significant challenges despite a more favorable growth rate in the Industrial Production Index (IIP) compared to previous years Key manufacturing sectors experienced notable declines, with sugar production down by 22.9%, beer by 13.9%, liquefied petroleum gas by 13%, crude oil extraction by 12.6%, natural gas by 11.5%, synthetic fiber textiles by 8.9%, motorcycles by 7.7%, regular clothing by 4.9%, and leather footwear and automobiles by 2.9%, alongside a decrease in animal feed production.
The Index Industrial For Production 2019-2023 by 2% Consequently, this had a considerable impact on the overall supply for the country's economy
Under the guidance of the Party and the state, Vietnam has successfully revitalized its development momentum, evidenced by the consistent growth of the Industrial Production Index (IIP) from 2021 to 2023, with milestones of 4.82 in 2021, 7.89 in 2022, and 13.17 in 2023 This progress highlights the Vietnamese government's favorable policies for businesses, particularly foreign direct investment (FDI) enterprises, and underscores the robust recovery of the domestic manufacturing sector in 2023, despite a global decline in total demand The growth reflects the government's dedicated efforts to eliminate obstacles and support businesses in restoring and enhancing their production activities.
Between 2020 and 2023, the manufacturing sector encountered significant challenges, but with robust support and coordination from the Party, Government, and local organizations, its health has notably improved This collaborative effort has allowed the sector to exceed initial targets To continue promoting manufacturing activities, it is essential to implement targeted solutions that support industrial production and address existing challenges.
Research data description
As of July 28, 2023, the Ho Chi Minh Stock Exchange (HOSE) featured 409 listed businesses, totaling over 141 billion outstanding shares (CafeF) This article analyzes secondary data, focusing on financial reports from 133 out of 137 manufacturing companies listed on HOSE, covering a four-year period from 2019 to 2023.
The research model included 133 continuously operating and listed corporations from 2020 to 2023 Although analyzing all 137 companies would enhance the data quality, four companies, including Lam Son Sugar Joint Stock Corporation and Thanh, did not meet the necessary criteria for data processing and research objectives.
As of the data collection date, Thanh Cong – Bien Hoa Joint Stock Corporation and Siba High-Tech Mechanical Group Joint Stock Company have not released their publicly audited financial reports for 2023 Additionally, Sai Gon Vien Dong Technology Joint Stock Corporation has exhausted its inventory since 2020, resulting in insufficient data for the required variables.
The authors gathered data for their research model from reliable sources, including current assets, current liabilities, inventories, total assets, shareholders' equity, cost of goods sold, net income, net fixed assets, and cash and cash equivalents This information was sourced from the websites of issuing organizations, ensuring it is continuously updated and pertains to companies that have provided comprehensive and accurate data for over four years, from 2020 to 2023, based on audited financial reports.
Research method and research model
The research method is based on comparing and combining econometric models from previous studies to identify the relationship between liquidity indicators of enterprises and their operational efficiency
The research process follows these steps:
Step 1: Research and model development:
The research process is based on previous studies and individual understanding, combined with analysis and synthesis to compare and examine factors in Vietnam and other countries to identify differences
This method entails analyzing existing literature and economic models to explore the impact of various factors on liquidity and operational efficiency Researchers customize these models for Vietnamese enterprises, taking into account their distinct characteristics compared to other nations The process includes statistical analysis, econometric modeling, and theoretical frameworks to thoroughly understand the connection between liquidity indicators and operational efficiency.
Step 2: Analyze and validation research:
This article synthesizes key findings from several research studies, including Hong Nguyen Thi Xuan's (2022) investigation into the impact of the COVID-19 pandemic on the financial performance of Vietnamese logistics enterprises, which analyzed effects at various stages It also incorporates insights from K Li et al (2020), who explored the relationship between liquidity and financial performance among non-financial firms listed on the Ghana Stock Exchange using the Hausman test alongside Fixed Effects Model (FEM) and Random Effects Model (REM) Additionally, it references the work of R Ismail, contributing to a comprehensive understanding of these financial dynamics during the pandemic.
In 2016, a study titled "Impact of Liquidity Management on Profitability of Pakistani Firms: A Case of KSE-100 Index" examined the relationship between liquidity management and profitability using the cash flow ratio The research proposed a model and methodology for calculating key financial variables to analyze this impact effectively.
The author selects specific financial metrics to analyze the performance of manufacturing corporations in Vietnam, identifying Return on Assets and Return on Equity as the dependent variables The independent variables include the Current Ratio, Quick Ratio, and Inventory Ratio, while firm size, inflation rate, and GDP growth rate are designated as control variables.
Table 1: Variables in the model
Classify variables Variables Index Formula
Firm size FS Ln (total assets)
Inflation rate IR World Bank
In the data processing phase, incorporating the variables CR, QR, ITO, and CFR into the regression model produced imprecise outcomes due to the varying scales of businesses To enhance the accuracy of the data for research purposes, the author decided to utilize the logarithmic transformation of these variables.
Additionally, the other variables, including Return on Assets and Return on Equity, are being collected and processed into percentage form
The study employs an OLS regression model to analyze the effect of corporate liquidity on firm performance, utilizing both Fixed Effects Model (FEM) and Random Effects Model (REM) alongside the Hausman test to assess the appropriateness of the variables within the model The author concludes the analysis with a comprehensive summary of the findings.
𝛽 0 : Blocking coefficient of the model
Research methodology
Based on the theoretical framework of the variables presented in Chapter 1, the study will conduct evaluations based on the following hypotheses:
H1: The liquidity has affected to the Return on Assets
H2: The liquidity has affected to the Return on Equity.
MODEL OF TESTING THE IMPACT OF LIQUIDITY ON FIRM
The multicollinearity models
The author using Stata17 to conclude the multicollinearity between variables in this summary table:
ROE ROA CR QR ITO CFR TA FZ IR GDP
The correlation matrix reveals that most variables exhibit high correlations, with the exception of the 82.55% correlation between ROA and ROE This strong correlation is acceptable as both are profitability indicators sharing the same after-tax profit numerator Consequently, the author concludes that the majority of variables are not correlated, enhancing the model's significance and suitability for research and observation.
The multicollinearity between the Quick Ratio (QR) and the Current Ratio (CR) is notably high at 85.26%, primarily due to their similar formulas; while QR excludes inventory from the denominator, CR incorporates both inventory on hand and inventory turnover Despite this high correlation, the model remains valid as it does not adversely affect the overall analysis The model also considers after-tax profit as the numerator, and other variables demonstrate low correlations with each other, as shown in the accompanying table Consequently, most variables maintain correlation coefficients below 80%, affirming the model's appropriateness and relevance for observation and research.
The Ordinary Least Square (OLS) regression model
3.3.1 The Ordinary Least Square (OLS) regression model with ROE is dependent variable
The result from the running the regression model through Stata 17 being stated below:
Table 6: The OLS regression model with ROE dependent variable
ROE Coefficient Std err P-value Beta
The model demonstrates a strong regression significance with an R-squared value of 77.23%, indicating that it effectively explains 71.83% of the variance in the dependent variable Additionally, 22.77% of the model's variance is attributed to random factors Furthermore, the model exhibits statistical significance, as evidenced by a Significant F value of 0.
The analysis indicates that all variables, except for the control variable QR (P-value > 5%), show a significant P-value of 0, demonstrating that both independent and control variables significantly affect the dependent variable (P-value < 5%) Notably, hypothesis H2 is excluded from this conclusion, as it presents a negative coefficient of -43.78% for CR, suggesting a detrimental impact.
The study reveals that current ratios (CR) negatively affect return on equity (ROE) In contrast, the independent and control variables—inventory turnover (ITO), cash flow ratio (CFR), total assets (TA), financial leverage (FZ), and gross domestic product (GDP)—demonstrate a positive influence on ROE.
The OLS model test reveals a negative relationship between the current ratio (CR) and return on equity (ROE) in manufacturing businesses, indicating that as ROE increases, the CR tends to decrease, and vice versa This trend has been consistent during the period from 2020 to 2023, particularly influenced by the effects of Covid-19.
CR decrease due to the descent of the current assets of the corporates as they might continuously paying their short-term and long-term debt payments
The macroeconomic control variables, interest rate (IR) and gross domestic product (GDP), demonstrate a positive relationship with the return on equity (ROE) model, as indicated by a p-value of less than 5% This suggests that an increase in a company's ROE ratio correlates with rising IR and GDP rates, and conversely, a decrease in ROE is associated with lower IR and GDP.
We accept a part of hypothesis H2, as the dataset found a relationship between changes in the indicators CR, QR, ITO, CFR, TA, FZ, which influence ROE
3.3.2 The OLS regression model with ROA is dependent variables
Through using Stata17, the author completes the result data of the regression process in this below table:
Table 7: The OLS regression model with ROA dependent variable
ROA Coefficient Std err P-value Beta
The model demonstrates significance with an R-squared value of 79.10%, indicating that it explains a substantial portion of the variance in the dependent variables, surpassing the 50% threshold This suggests a stronger explanatory power compared to the ROA regression model Specifically, the independent variables account for 79.10% of the variance, while 20.9% is attributed to random factors Additionally, the model's significant F-statistic of 0 reinforces its statistical validity Notably, all dependent variables, except for the current ratio (CR), have a negative impact on the model, indicating their influence on the independent variables.
The regression analysis reveals that the QR ratio, with a P-value exceeding 5% and a positive coefficient, does not positively influence the ROA model Consequently, this indicator fails to demonstrate a significant impact on the ROA model.
While the independent variable ITO and CFR with positive coef and their P-value |t| [95% conf interval]
_cons -0.60158 0.209355 -2.87 0.004 -1.01318 -0.18998 sigma_u 03257375 sigma_e 02330813 rho 66137085 (fraction of variance due to u_i)
The FEM model demonstrates statistical significance with a Prob>F value of 0.0000 for the independent variable ROE, well below the 5% significance threshold In contrast, the QR and ITO variables exhibit p-values exceeding 5%, indicating no effect on ROE Notably, CFR is the sole independent variable with a p-value below 5% Additionally, the CR variable, characterized by a negative coefficient and p-value under 5%, negatively influences the model Thus, we conclude that both CFR and CR significantly impact the ROE model.
The model's key control variables, FZ, GDP, and IR, show a P-value less than 5%, indicating their positive impact on the model's outcomes.
The analysis reveals that TA variables have a positive influence on the ROE model, indicated by a positive coefficient and a p-value of less than 5% Conversely, the coefficient for IR is relatively low at 0.006225, suggesting it has a minimal impact on the ROA results model.
In summary, the analysis reveals that, aside from the independent variables CR and CFR, there is no significant relationship between the independent variables QR and CR and their effect on Return on Equity (ROE) Conversely, other control variables such as FZ, TA, GDP, and IR demonstrate a positive impact on the model.
Table 9: REM model with the dependence ROE
Random-effects GLS regression Number of obs = 532
Group variable: CK Number of groups = 133
Wald chi2(8) = 1691.95 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000
ROE Coef Std err z P>|z| [95% conf interval]
_cons -0.01847 0.027353 -0.68 0.5 -0.07208 0.035145 sigma_u 00755705 sigma_e 01816177 rho 14758425 (fraction of variance due to u_i)
The random effects model demonstrates statistical significance for the dependent variable ROE, with a Prob>chi2 value of 0.0000, indicating a strong relationship Regression analysis reveals that the independent variables Current Ratio (CR), Inventory Turnover (ITO), and Cash Flow Ratio (CFR) have P-values below 5%, suggesting a notable impact on ROE.
The negative coefficient of the CR adversely affects the ROE model, while the ITO and CFR variables, with their positive coefficients, contribute positively to the model's performance.
The analysis reveals that the QR variable has a P-value exceeding 5%, suggesting that it does not significantly affect Return on Equity (ROE) In contrast, all control variables demonstrate P-values below 5%, indicating a significant impact, particularly highlighting the influence of Total Assets (TA) and Financial Leverage (FZ).
IR, GDP impact positively to the ROE model
In summary, all variables except for the Current Ratio (CR) positively influence the Return on Equity (ROE) model, including ITO, CFR, TA, FZ, IR, and GDP, while the CR variable has a negative impact.
3.4.1.2: FEM and REM model with ROA
Table 10 FEM model with the dependence ROA
Fixed-effects (within) regression Number of obs = 532
Group variable: CK Number of groups = 133
ROA Coef Std err z P>|z| [95% conf interval]
_cons -0.49413 0.252532 -1.96 0.051 -0.99062 0.002358 sigma_u 02382016 sigma_e 02811515 rho 4178628 (fraction of variance due to u_i)
The Fixed Effects Model (FEM) analysis reveals a statistically significant relationship between the independent variables and Return on Assets (ROA), with a Prob>F value of 0.0000 Notably, Inventory Turnover (ITO) and Cash Flow Ratio (CFR) exhibit positive coefficients and p-values below 5%, indicating their significant impact on ROA Conversely, the Current Ratio (CR) and Quick Ratio (QR) show p-values exceeding 5%, suggesting no influence on the ROA model Therefore, an increase in ITO and CFR is associated with a rise in the ROA ratio within the manufacturing sector.
The results indicate that all control variables significantly impact the Return on Assets (ROA) model, exhibiting positive coefficients and p-values less than 5% Consequently, we can conclude that Total Assets (TA) and Firm Size (FZ) positively influence ROA.
IR and GDP impact positively to the ROA model
In summary, the FEM model demonstrates that ITO and CFR are independent variables influencing the results, while CR and QR have no effect on the ROA model Additionally, all control variables positively impact the ROA model.
Table 11 REM model with the dependence ROA
Random-effects GLS regression Number of obs = 532
Group variable: CK Number of groups = 133
Wald chi2(8) = 1979.62 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000
ROA Coefficient Std err z P>z [95% conf interval]
IR 0.006075 0.002344 2.59 0.010 0.001482 0.010669 GDP 0.018466 0.000666 27.75 0.000 0.017161 0.01977 _cons -0.11138 0.028901 -3.85 0.000 -0.16802 -0.05474 sigma_u 0 sigma_e 02811515 rho 0 (fraction of variance due to u_i)
The Random Effects Model (REM) reveals a statistically significant relationship with the dependent variable ROA, evidenced by a Prob>chi2 value of 0.000, which is below the 5% significance threshold However, among the independent variables, only the QR variable shows a P-value exceeding 5%, suggesting it lacks statistical significance.
The variable CR exhibits a negative coefficient, indicating a detrimental impact on the ROA model In contrast, both ITO and CFR demonstrate positive coefficients with P-values below 5%, signifying their beneficial influence on ROA Additionally, the control variables, including TA and FZ, play a role in this analysis.
IR and GDP also have positive coefficients and P-values below 5%, indicating that they also positively influence ROA
In conclusion, except for the variable QR, all the other variables show its own relationship with the ROA model.
The Hausman test
Table 12 The Hausman test FEM, REM for ROE
(b) (B) (b-B) sqrt(diag(V_b-V_B)) fe re Difference Std err
IR 0.006225 0.00538 0.000845 0.000328 b = Consistent under H0 and Ha; obtained from xtreg
B = Inconsistent under Ha, efficient under H0; obtained from xtreg
Test of H0: Difference in coefficients not systematic chi2(7) = (b-B)'[(V_b-V_B)^(-1)](b-B)
H0: The model factors influencing the REM
H1: The model factors influencing the FEM
The Hausman test with 2 model FEM and REM for the dependence variable ROE giving the result Prob > chi2 = 0.0161 which is larger than 0.05 (5%) so we except H1, decline H0
The study identifies the FEM model as the most suitable, revealing that the independent variable CFR positively influences the dependent variable ROE In contrast, the CR variables negatively affect ROE, while QR and ITO demonstrate no significant impact on the ROE model Additionally, control variables such as TA and FZ are considered in the analysis.
IR and GDP influence positively to the model
Table 13 The Hausman test FEM, REM for ROA
V_B)) fe1 re1 Difference Std err
IR 0.006755 0.006075 0.00068 0.000648 b = Consistent under H0 and Ha; obtained from xtreg
B = Inconsistent under Ha, efficient under H0; obtained from xtreg
Test of H0: Difference in coefficients not systematic chi2(7) = (b-B)'[(V_b-V_B)^(-1)](b-B)
H0: The model factors influencing the REM
H1: The model factors influencing the FEM
The Hausman test with 2 model FEM and REM for the dependence variable ROE giving the result Prob > chi2 = 0.0283 which is smaller than 0.05 (5%) so we except H1, decline H0
The findings indicate that the FEM model reveals no influence of CR and QR on ROA, while ITO and CFR positively impact ROA Additionally, all control variables were accounted for in the analysis.
TA, FZ, IR and GDP show its positively impact to ROA model.
Summaries the research results
We have the summary table of research results after testing with the following models:
The dataset reveals a significant relationship between variations in key financial indicators, specifically the Current Ratio (CR), Inventory Turnover (ITO), Cash Flow Ratio (CFR), and Total Assets (TA), which collectively influence Return on Equity (ROE) Changes in these indicators demonstrate a clear correlation with ROE, highlighting their importance in financial analysis.
The analysis reveals that, aside from Inventory Turnover (ITO) and Cash Flow Ratio (CFR), there is no significant relationship between the independent variables, Quick Ratio (QR) and Current Ratio (CR), and their effect on Return on Equity (ROE) in the FEM model However, all control variables demonstrate a measurable impact on the ROE model, highlighting the importance of examining the relationships between variations in CR, ITO, CFR, and ROE within the FEM framework.
The dataset finds the relationship between CR, CFR, TA,
FZ, IR and GDP variables impact to the ROE model of REM.The dataset finds, impact of FZ on ROE in REM model
The Hausman indicates that variable CFR, ITO, TA, FZ,
IR and GDP positive influence on the dependent variable ROE
2 ROA The data find relationship between the all-independence indicator and its impact on ROA except for QR ratio
The dataset finds relationship between the independent variables ITO, CFR, TA and their impact on ROA in FEM model
The dataset finds, impact of CR, ITO, CFR, TA, FZ, IR and GDP on ROA in REM model
The Hausman indicates that variable ITO, CFR, TA, FZ,
IR and GDP have influence on the dependent variable ROA.
CONCLUSION AND SOME RECOMMENDATIONS OF
Conclusion
The research data is based on audited financial statements of 137 listed companies on the
From 2020 to 2023, the Ho Chi Minh Stock Exchange exhibited significant trends across various industrial sectors An analysis of 133 samples, incorporating 2 dependent variables, 4 independent variables, and 4 control variables, was conducted using regression models The author's objective was to validate hypotheses and derive meaningful conclusions regarding market dynamics during this period.
Multicollinearity is not present in the model, as evidenced by correlation matrix values consistently below 0.8, indicating that the variables are independent and their influence on the model is minimal.
The OLS, FEM, REM models are suitable for the dataset The conclusions drawn are based on the research findings:
Firstly, the negative impact of the pandemic on Return on Assets (ROA) and Return on
Equity, measured by Return on Equity (ROE), remains consistent across both models, corroborating the findings of Shiwei Hu and Yuyao Zhang (2021) as well as W Shahimi et al (2021) Additionally, the regression model yields results that align with the research conducted by Kariuki, Muturi, and Njeru (2021) regarding ROE, and with the insights of Yameen, Farhan, and Tabash.
The pandemic has had a significant impact on the Return on Assets (ROA), with post-pandemic conditions showing only a slight improvement in the negative coefficient, resulting in a continued negative Cash Ratio (CR) This negative CR indicates that the economy remains in a recession In response, manufacturing companies are adapting by lowering revenue and increasing expenses.
This could also be a sign that the company is facing difficulties in paying short-term debts
The integration of investment decision stages remains unresolved, highlighting the urgent need for managers to enhance their current CR to prevent potential bankruptcy While the results do not quantify the influence of QR on ROA and ROE, the coefficient outcomes suggest that QR does have an impact on the overall model.
Both the Cash Flow Ratio (CFR) and Inventory Turnover (ITO) show positive values in both periods, indicating potential issues in manufacturing companies related to excessive inventory due to a lack of suitable buyers This aligns with findings from K Li et al (2020), Y Kong et al (2019), and RNA Dodoo (2020), suggesting that large inventory reserves may stem from companies accumulating raw materials to meet production demands However, the prevailing interpretation is that these companies struggle to find buyers, leading to an increased CFR A higher CFR signifies that companies have ample cash available to address short-term debts, enhancing their flexibility in meeting obligations and ultimately improving firm performance.
Total Assets (TA) positively influence both dependent variables, indicating that companies are equipped to endure market fluctuations and bolster their stock prices This finding aligns with Ao Dada's (2016) research and suggests that higher TA correlates with lower liquidity risk Originating from net fixed assets, TA offers flexibility and versatility, which can be leveraged for multiple purposes Effectively optimizing these indicators can enhance a company's credibility and improve overall firm performance.
Financial leverage (FZ) consistently demonstrates a positive influence on the performance of manufacturing firms listed on the HOSE stock exchange, aligning with the findings of Nguyen Thi Ngoc Lan and Nguyen Van Cong (2020) A higher FZ indicates greater control over total assets, which is crucial for establishing competitive advantages and stimulating consumer spending, ultimately leading to improved business efficiency.
The QR variables demonstrate a positive impact on both Return on Assets (ROA) and Return on Equity (ROE) according to the model results This finding aligns with the previous research by Dsouza et al (2023), which explored the effects of liquidity and leverage on performance within the hotels and entertainment services industry in the MENA region.
The QR ratio reflects a company's ability to meet short-term debts through government policies, highlighting ongoing challenges faced during the pandemic Maintaining a stable QR ratio is crucial for ensuring liquidity and appealing performance for investors Companies should focus on leveraging this ratio, but it's important to avoid keeping it low, as this may indicate financial difficulties and hinder the prompt settlement of short-term obligations.
Limitations of the research model and proposed future research directions
The research sample size has limitations as it does not encompass all companies listed on the Hanoi Stock Exchange (HNX) and the Unlisted Public Company Market (UPCOM) Given that the manufacturing sector significantly contributes to the country's GDP, future research should focus on analyzing the impact of manufacturing companies on these exchange markets Additionally, researchers should categorize each manufacturing sector, recognizing that companies operate in specialized roles across various industries, such as plastic manufacturing and timber production.
The research findings will provide a clearer understanding of each manufacturing sector, enabling investors to make well-informed investment choices Additionally, business owners will be better equipped to optimize their use of equity and assets, thereby improving operational efficiency.
In addition to the variables already considered, there are numerous external factors not measured in the current model that could significantly influence firm performance The authors recommend that future research incorporate additional variables such as company age, corporate governance, cash conversion cycle (CCC), capital structure, and earnings per share (EPS) to enhance the understanding of what drives firm performance.
Thirdly, the study recommends that further study should research on the impact of working capital on the firm performance of manufacturing sectors with both firms listed on HOSE and
UPCOM in Vietnamese stock market to bring a brand-new look for the whole manufacturing companies
Due to time constraints and incomplete data sources, many companies have not updated their audited financial reports from 2019 to 2023 The Covid-19 pandemic has adversely affected the socio-economic landscape, causing supply chain disruptions, business closures, and increased bankruptcies The manufacturing sector, crucial for supplying essential goods, has faced significant fluctuations during this time By strategically selecting the timeframe, the omissions in the income reports and GDP can be more accurately assessed.
The research focused on the liquidity impact on manufacturing companies in Vietnam, but it was limited by the short duration of data collected The author suggests that future studies could benefit from utilizing quarterly data to gain a clearer understanding of sector changes over time Additionally, incorporating Covid-19 case variables into the analysis is recommended to more accurately assess the pandemic's effects on the industry.
Some recommendation to improve liquidity of Vietnam manufacturing sectors
Based on a review of the model's results, insights from prior research, and identified shortcomings, the author proposes several solutions to enhance the model's effectiveness.
To enhance Return on Equity (ROE) and Return on Assets (ROA), companies must focus on improving their Current Ratio (CR) and Quick Ratio (QR) Achieving a balance between short-term and long-term borrowing is essential for financial stability For manufacturing firms, borrowing is crucial for investing in production lines, workforce, and vital assets when internal finances fall short, thereby optimizing available capital resources.
Effective capital borrowing is essential for companies to manage both long-term and short-term debts, as mismanagement can lead to bankruptcy Establishing a balanced debt structure between these two types of debts enhances operational efficiency for manufacturing companies Maintaining optimal ratios ensures that assets are utilized effectively without compromising equity, which in turn improves product quality, elevates production standards, and enhances customer service, ultimately optimizing overall company operations.
To enhance Inventory Turnover (ITO), the company must focus on reducing its ITO numbers through effective inventory management This involves a comprehensive understanding of operations and close monitoring of market fluctuations, as ITO significantly impacts current assets Managers should coordinate inventory levels, implement suitable storage policies, and promote strategies to minimize risks related to inventory storage To further decrease ITO, increasing inventory levels can ensure availability, but it is crucial to balance this with cost considerations Poor inventory management can lead to shortages or excess stock, disrupting production and negatively impacting operational efficiency if storage costs become excessive.
To improve their Cash Flow Ratio (CFR), companies can reduce purchasing costs to lower the cash required for inventory acquisition Regularly reviewing cash flow conditions and minimizing operating expenses are effective strategies for enhancing cash flow while maintaining liquidity Additionally, optimizing sales processes, managing inventory efficiently, and promptly recovering receivables can relieve financial pressure, especially when short-term debts are paid early While a high CFR isn't always beneficial, balancing credit terms and purchasing policies can serve as a valuable indicator for optimizing overall business operations.
Research shows that the size of an enterprise positively impacts all efficiency evaluation indicators in business operations To increase total assets, companies can focus on enhancing Fixed Assets or Current Assets For Fixed Assets, investing in additional production lines and expanding factory scale is crucial for improving production efficiency In terms of Current Assets, businesses should aim to boost cash reserves, maintain adequate raw material inventory to meet production demands, and recover short-term debts effectively.
12 months) However, companies also need to balance between short-term and long-term assets to suit their business needs
To build trust and credibility with customers, investors, and the community, companies must prioritize corporate social responsibility (CSR) and ethical business practices As stakeholders increasingly favor businesses that focus on community engagement, implementing effective CSR strategies can significantly enhance a company's credibility, brand value, and market reputation A strong CSR commitment not only attracts investment capital but also helps reduce costs, boost long-term revenue, and maintain stable liquidity Ultimately, this leads to improved operational efficiency and financial stability, equipping businesses to navigate economic challenges effectively.
Some recommendation to improve liquidity of Vietnam manufacturing sectors
Based on the results of the researches, the author recommendation suggests the following solutions:
The government has effectively implemented various support programs for businesses and sectors significantly impacted by the Covid-19 pandemic, including extensions on value-added tax, corporate income tax, personal income tax, and land rent payments To further aid recovery, it is essential for the government to introduce additional targeted policies that address the ongoing challenges posed by both the pandemic and the current global economic recession Small and medium-sized enterprises, in particular, require substantial government support to navigate these difficulties.
The government can support businesses by offering funding packages with low interest rates and extended repayment terms Additionally, it should encourage innovation and the adoption of new technologies through support initiatives, helping businesses enhance operational efficiency and navigate the challenges posed by outdated business models as the market shifts towards e-commerce platforms and faces rising market risks.
The State Bank of Vietnam should adopt a more flexible approach to managing exchange rates to shield the domestic market from external influences, particularly by stabilizing the USD/VND exchange rate and domestic gold prices to enhance the competitiveness of local businesses Furthermore, the bank must effectively coordinate monetary and fiscal policies to ensure liquidity in the money market, enabling credit institutions to work alongside the central bank to lower loan interest rates.
The State Bank of Vietnam must tighten monetary policy and manage state budget revenues and expenditures effectively at both central and local levels by implementing support packages for businesses impacted by Covid-19 This approach will help control inflation, instill confidence in manufacturing and other businesses, and attract foreign investment A stable inflation rate is crucial for promoting production and export activities, fostering overall economic development.
To prevent future pandemic outbreaks similar to those experienced in other countries, it is essential to sustain the Covid-19 vaccination campaign and regularly update infection statistics and the health status of affected individuals Should another outbreak occur, the financial repercussions for businesses and the overall economy could lead to a significant crisis.
To maximize the advantages of trade agreements, it is crucial to enhance the effective utilization of specific provisions Strengthening economic cooperation with regional and global partners, as well as engaging in international organizations, can significantly elevate Vietnam's standing in the global market Businesses must continuously innovate, adopt new technologies, and improve their supply chains and production quality to meet evolving market demands.
The government must enhance public investment efforts by restructuring sustainable investments tied to economic growth models, including improvements to transportation systems and infrastructure By effectively leveraging public investment, unemployment rates can be reduced, leading to increased incomes and heightened consumer spending, which will ultimately contribute to national economic growth and aid in the recovery from the pandemic's effects.
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Appendix: Companies in this study
AAA DAG HPG SCD SRC VHC
AAM DAT HRC SFG STK VNM
AAT DBC HSG SHA SVD VPS
ABS DBD HT1 SHI SVI VTB
ABT DBT HTG SJF TCM YBM
ACC DCL HVX SMB TCR
ACG DCM IDI SPM TDP
ACL DGC IMP SRC TEG
ADP DHC KDC STK THG
ADS DHG KMR SVD TLD
AGM DLG LAF SVI TLG
ANV DMC LBM TCM TMT
APC DPM LIX TCR TNC
APH DPR MCP TDP TPC
ASM DQC MSH TEG TRA
BAF DRC MSN THG TRC
BBC DTL NAF TLD TTF
BFC DTT NAV TLG TVT
BHN DXV NHH TMT TYA
BKG EVE NHT TNC VAF
BMP FCM NKG TPC TLG
BRC FIT OPC TRA TMT
CAV FMC PAC TRC TNC
CLC GDT PAN TTF TPC
CMX GEX PHR TVT TRA
CRC GIL PLP TYA TRC
CSM GMC POM VAF TTF
CSV GMH PTB SCD TVT
CVT GTA RAL SFG TYA
DAG GVR RDP SHA VAF
DAT HAP SAB SHI VCA
DBC HCD SAM SJF VCF
DBD HHP SAV SMB VDP
DBT HII SBV SPM VGC
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