Effect of Liquidity Management on Financial Performance of Nigerian Consumer Goods Manufacturing Firms

Effect of Liquidity Management on Financial Performance of Nigerian Consumer Goods Manufacturing Firms

Talatu K. Bagana^*, Dr. Saheed A. Lateef, Prof. Emeka E. Ene

Accounting and Finance Department, Faculty of Management and Social Sciences, Baze University Abuja, Nigeria

*Corresponding Author

DOI: https://doi.org/10.51244/IJRSI.2024.1106018

Received: 17 May 2024; Accepted: 04 June 2024; Published: 05 July 2024

ABSTRACT

The study examined the effect of liquidity management on the financial performance of Nigerian consumer goods manufacturing firms. The study adopted an ex-post facto research design. The study depends wholly on secondary data collected from the annual report of 10 listed consumer goods manufacturing firms on the Nigerian Exchange Group (NEG) from 2018 to 2022. The dependent variable for the study is financial performance which was proxies as return on asset while the independent variables are cash and cash equivalent, cash conversion cycle, quick ratio, current ratio and working capital were as the dependent variable. Panel analysis was used in the study. E-views 12 statistical package was used to analyze the data. The study discovered that cash and cash equivalents exert positive and significant impact on financial performance of consumer goods firms, that there is negative and significant relationship between cash conversion cycle and financial performance of consumer goods in Nigeria, that there is negative and no significant relationship between quick ratio and financial performance of consumer goods firms in Nigeria, that there is positive and significant impact of current ratio on financial performance of consumer goods firms in Nigeria, that there is positive and significant impact of working capital on financial performance of consumer goods firms in Nigeria. The study concluded that liquidity management significantly affects the financial performance of Nigerian consumer goods manufacturing firms. The study recommended that management reduce the magnitude at which they use up cash and its equivalent in settling their short-term obligations to improve their profitability.

Keywords: Liquidity Management, Financial Performance, Cash Equivalent, Working Capital, Consumer Goods, Return on Asset

INTRODUCTION

A firm’s financial performance refers to the capacity of an organization to produce long-term profits (Kayani et al., 2023). It functions as an essential gauge, offering perceptions of the company’s capacity to make money, add value for stakeholders, and negotiate the difficult terrain of financial operations (Derun & Mysaka, 2018). These definitions emphasize the pivotal role a firm’s performance plays in business survival and provide the justification for the consistent focus on the interactions between microeconomic variables, industrial attributes, and macroeconomic factors and firm performance. More specifically, many studies investigated and validated the effect of some firm-specific attributes. The attributes include firm age, size, capital structure, and even dividend policy. However, despite the plethora of discourse on firm performance, the trade-off relationship between performance and liquidity management has remained an enigma.

Highlighting the existence of a complex connection between liquidity and profitability, Panigrahi et al (2018) stated that the liquidity in the operation of a firm might impact its profitability. More explicitly on the interaction between liquidity and profitability, Azeez (2020) explained that firms with abundant liquidity may have minimal liquidity risk, but maintaining a considerable amount of assets in liquid form may limit investment in profitable initiatives owing to a lack of capital, thus resulting in reduced profitability. Contrariwise, according to the author, firms that focus entirely on profitable initiatives may struggle with day-to-day operations due to the paucity of idle funds. Similarly, Effiong and Ejabu (2020) cautioned on maintaining excess liquidity since idle money generates no income or profits for the organization. These statements suggest a trade-off link between liquidity and profitability, requiring that a well-managed company seek a precise balance between insufficient and excessive liquidity.

Liquidity is a key financial criterion for determining a company’s capacity to meet its short-term creditors without suffering undesired losses (Alhassan & Islam, 2021). Subsequently, liquidity management, according to Okpala et al (2019), is the strategic planning and control necessary to guarantee that an organisation has enough liquid assets. Simply put, liquidity management aids companies to avoid liquidity shortages to satisfy short-term demands. Highlighting the importance of effective liquidity management to a company’s success and financial survival, Jepkorir et al (2019) posited the prioritizing of strong liquidity management for businesses to avoid insolvency and the possibility of bankruptcy. Wuave et al., (2020) also observed that mismatching current assets and obligations can cause liquidity issues, culminating in a severe liquidity crisis. These authors pinpoint the critical role effective liquidity management plays in ensuring the smooth operation of a commercial enterprise.

Effective liquidity management requires an understanding of vital liquidity ratios such as the current ratio, quick ratio, and cash ratio (Sathyamoorthi et al., 2020). These liquidity ratios provide valuable information about an organization’s capacity to fulfill immediate financial responsibilities.

The current ratio is an estimate of the relative value between current assets and total liability. This reflects the extent current assets can readily be deployed to meet current obligations as they fall due. The exclusion of inventory from current assets to compute the relative value will compute the quick ratio. The rationale behind the exclusion of inventory can be justified by the presence of slow-moving inventory and seasonal sales questioning the ease of conversion of inventory to cash. Cash ratio, a common measure of liquidity, compares the cash balance to current liabilities while working capital is the difference between current assets and current liabilities. The extent to which these ratios are practicable greatly depends on the industrial context.

Since firm performance is a key signal for shareholders’ wealth maximization, it is paramount that any proximate factor that seemingly interacts with it receive adequate empirical attention, therefore, this study studied the liquidity management/profitability nexus in Nigeria by examining the effect of liquidity management on the financial performance of quoted consumer goods firms.

1.1 Statement of Problem 

The increasingly turbulent and competitive market setting within which organisations are expected to strive requires management to consistently focus on long-term sustainability. Long-term sustainability has been empirically validated as synonymous with maximizing shareholders’ wealth. To achieve the maximization of shareholders’ wealth, firms would have to pursue performance typically measured as profitability. This focus on firm performance has elicited many empirical investigations to expose factors that have a considerable effect on firm performance. Some of these studies revealed significant factors such as cash and cash equivalent, cash conversion cycle, quick ratio, current ratio and working capital (Azam, 2017; Laminfoday, 2018; Nabeel & Hussain, 2017; Otieno, 2016; Otekunrin et.al, 2019)These factors were found to exert time-varying effects across different contexts in terms of economies and sectors.

Despite the myriad of studies on the determinants of firm performance, the acclaimed trade-off between liquidity and firm performance (Effiong & Ejabu, 2020; Panigrahi et al. 2018) evokes an in-depth focus on the relationship between liquidity management and firm performance.  Emphasizing the significant effect of liquidity management, Mohanty and Mehrotra (2018) stated that achieving a sustainable operating model required a careful mix of effective liquidity management. Nevertheless, there exist opposing perspectives as notable studies conducted by Shanthirathna (2019) and Panigrahi (2019) have shown no significant correlation between liquidity and profitability hence questioning the notion of a trade-off between performance and liquidity. This adds complexity to the continuing discussion on the interaction between these factors in different business settings.

Furthermore, as emphasised by Dalci (2018) and Imhanzenobe (2020), understanding how cash equivalents and other current assets affect organizations’ financial health and stability is critical for stakeholders ranging from investors to managers and policymakers.

Despite the importance of firms’ liquidity management strategies, there is a lucidity of empirical research on their specific impact on key financial performance metrics such as profitability, return on equity, and risk management practices in the consumer goods manufacturing sector. This issue of liquidity requires more examination due to the sector’s vulnerability to a variety of external variables, such as changes in customer tastes, raw material pricing, and global economic circumstances, which can interact with cash equivalent holdings in complicated ways (Dalci, 2018).

As a result, filling this gap in the research is critical for improving the understanding of the financial dynamics of consumer products manufacturing businesses and guiding strategic decision-making processes in both academic and practical settings. This study therefore examined the effect of liquidity management on the financial performance of quoted consumer goods companies in Nigeria.

LITERATURE REVIEW

2.1 Theoretical Review

The Trade-off Theory and Agency Theory provide support for this study as it navigates the challenges of managing liquidity within a subset of Nigerian manufacturing enterprises. The Trade-off Theory highlights the complex choices businesses make when managing liquidity by shedding light on the balance they must find between the advantages and disadvantages of financial strategies. Concurrently, the Agency Theory examines the interactions between managers and shareholders, exposing possible conflicts of interest in managerial choices, particularly liquidity-related ones. By including these theoretical frameworks, the research expands its analytical scope and offers a thorough perspective for understanding the complex dynamics of liquidity management in the manufacturing industry. These theories are discussed in this section as follows:

a. Agency Theory

An essential element of Agency Theory that reinforces its investigation is the concept of information asymmetry (Shaikh & O’Connor, 2020). Management frequently holds superior knowledge compared to shareholders regarding the internal operations and financial well-being of the company (Urbanek, 2020). Within the domain of liquidity management, this disparity in information becomes essential. Nigerian manufacturing companies, confronted with various economic difficulties and possibilities, must make choices regarding cash that are in line with both immediate stability and long-term profitability. Agency Theory enables researchers to examine the impact of management’s access to confidential information on their decision-making on liquidity, and the subsequent consequences on financial performance (Mitnick, 2019).

The principal-agent relationship is defined by the principal’s requirement to devise mechanisms to synchronize the agent’s interests with their own (Vitolla et al., 2020). Within the study’s framework, shareholders need confirmation that management’s choices regarding liquidity are in line with the ultimate objective of maximizing shareholder wealth (Shaikh & O’Connor, 2020). Consequently, researchers have the opportunity to investigate the methods and structures implemented by manufacturing enterprises in Nigeria to oversee and regulate management’s decisions about liquidity. This may entail examining the function of board supervision, internal regulations, or incentive systems that connect managerial remuneration to the financial success of the company (Vitolla et al., 2020).

Furthermore, Agency Theory emphasizes the use of contracts as instruments to alleviate agency issues (Jensen & Meckling, 2019). Contracts in the field of liquidity management might take on several manifestations. Examples of procedures that may be used to align the interests of management and shareholders in terms of liquidity choices include covenants in loan agreements and performance criteria in management contracts (Urbanek, 2020). Researchers can examine the contractual features of liquidity management in Nigerian manufacturing enterprises and evaluate the influence of these mechanisms on financial performance.

In addition, the study might be enhanced by examining the notion of goal incongruence, which refers to situations where the goals of management and shareholders do not completely coincide. The inquiry focuses on how manufacturing organizations manage the discrepancy in liquidity choices. Gaining insight into management’s perception of their role in attaining organizational objectives and assessing the alignment of these objectives with shareholder interests, enhances the analysis of the research (Dong et al., 2021).

Ultimately, Agency Theory is an essential conceptual framework for examining the impact of Liquidity Management on the Financial Performance of Manufacturing Firms in Nigeria. Through adopting the perspective of agency connections, researchers have a deeper understanding of the complex interplay between decision-making, information dissemination, and goal congruence within these organizations (Jensen & Meckling, 2019; Voorn et al., 2019). This theory offers a comprehensive framework for comprehending the process of making, monitoring, and assessing liquidity management choices, which eventually impact the financial performance of manufacturing companies in Nigeria.

b. Trade-off Theory

The Trade-off Theory is a notable financial theory that provides useful insights into the correlation between managing liquidity and achieving financial performance (Khoa & Thai, 2021). This theory suggests that organizations must choose between preserving their financial stability by keeping enough cash on hand and allocating it toward ventures that generate profits (Agyei et al., 2021). Nevertheless, according to the notion, having too much liquidity might be unproductive because unused funds do not create profits and may impede the firm’s potential to make money. The Trade-off Theory is particularly applicable in the Nigerian manufacturing sector, which is characterized by dynamic and uncertain economic situations. Companies must skillfully manage the delicate equilibrium between maintaining adequate liquidity to mitigate uncertainty and allocating resources to exploit lucrative possibilities (Rahman, 2019). The theory acknowledges that the ideal degree of liquidity might differ among companies and sectors, highlighting the importance of a customized strategy.

The Trade-off Theory assists in this study by directing the investigation into the strategic decision-making process of manufacturing enterprises in Nigeria concerning their liquidity circumstances. Chatzina and Papadopoulos, (2018) examine how these companies assess the expenses and advantages linked to maintaining liquidity compared to investing in ventures that generate profits. This study encompasses factors such as the foregone benefits of unused cash, the influence of liquidity on immediate and future profitability, and the possible compromises associated with handling liquidity risk. Moreover, the Trade-off Theory promotes an analysis of external factors that impact decisions related to liquidity (Stevanovic et al., 2019). The economic conditions, market dynamics, and regulatory frameworks significantly influence a company’s strategy towards liquidity. According to the hypothesis, manufacturing companies can modify their levels of available cash in reaction to shifts in the economic climate (Dierker et al., 2019). Researchers may investigate how these modifications affect the company’s overall financial performance.

The study utilizes the Trade-off Theory to establish a theoretical framework that enables a detailed comprehension of the complex correlation between liquidity management and financial performance in the specific setting of Nigerian manufacturing enterprises. It offers a perspective for scholars to examine the strategic decisions made by these companies to reconcile immediate liquidity requirements with the goal of long-term profitability and expansion (Yakubu et al., 2021). The Trade-off Theory enhances the theoretical basis of the study and adds to a thorough investigation of decision-making linked to liquidity in the Nigerian manufacturing sector (Martinez et al., 2019).

2.3 Conceptual Review

Liquidity Management

Liquidity management is the preservation of an adequate cash position and its corresponding balances to meet a firm’s financial commitments at any given time (Ajose & Solape, 2021). According to Ware (2015), liquidity management includes all managerial choices and activities that affect the volume and efficiency of liquidity. Corporate liquidity is a company’s ability to weather a storm when it has to have the cash and close-by cash equivalents to handle its issues (Onyekwelu et.al, 2019). It emphasizes the importance of managing current assets, current obligations, and the connections between them. Planning and managing current assets and current liabilities in a way that completely removes the danger of being unable to fulfill short-term obligations is the impact of liquidity management.

According to Eze and Agu (2020), a company’s liquidity management is said to be at its best if it is founded on the premise of collecting cash from debtors as soon as feasible and minimizing the cash payments of current liabilities or short-term obligations. To compensate for its short-term debts, a company may be obliged to turn to external financing if it is unable to maintain a strong liquidity position. Liquidity management refers to the planning and control necessary to ensure that the organization maintains enough liquid assets either as an obligation to the customers of the organization to meet some obligations incidental to the survival of the business or as a measure to adhere to the monetary policies of the central bank. Central banks define Liquidity management as the framework, set of instruments, and regulations that the monetary authority adheres to in managing systemic liquidity, consistent with the ultimate goals of monetary policy (Bhattacharyya & Sahoo, 2011).

Panigrahi (2013) asserted that liquidity management is a set of strategies and processes that ensure businesses can access cash as needed to pay for goods and services, make payrolls, and invest in innovative new projects. Liquidity management is a very vital aspect of all organizations these days. It has become so imperative that even profitable corporations can fail if they do not have the cash available to meet their recurrent cash demand and their short-term cash commitments as and when due.

2.3.1 Measures of Liquidity Management

The measures of liquidity management adopted in this study are cash and cash equivalents, cash conversion cycle, quick ratio, current ratio, and working capital. The measurements are reviewed as follows:

Cash and Cash Equivalent

Cash and cash equivalents (CCE) are the most liquid current assets found on a business’s statement of balance sheet. Cash equivalents are short-term commitments in the interim idle that are easily convertible into a known cash amount. Dittmar and Duchin (2011), an investment generally counts to be a cash equivalent when it has a short maturity period of 90 days or even less (if the maturity period is more than 90 days (for example, 100 days), then it will not be seen as cash and cash equivalents) from date of acquisition and when it carries an insignificant risk of changes in value. Equity investments typically are excluded from cash equivalents, except they are essentially cash equivalents, for instance, if the preferred shares are acquired within a short maturity period and with a specified recovery date (Denis, 2013).

Cash and cash equivalent consist of cash on hand, bank account, marketable securities, deposits, and other. Ogundipe et al (2012) viewed. Cash holding is also known as cash or cash equivalent that can be easily converted into cash. According to them, cash holding will include cash in hand and bank, short-term investment in money market instruments such as treasury bills. Cash holdings are simply defined as cash and marketable securities or cash equivalents (Opler, et.al, 1999). According to them, cash equivalents are current assets, which can be converted in a very short term and are thus characterized by a high degree of liquidity. They include, for instance, US treasury bills, certificates of deposits, bankers’ acceptances, and further money market instruments.

Cash equivalents are liquid asset substitutes (high credit quality and degree of liquidity) that can easily be transformed into cash in the short term and comprise of government treasury bills, bankers’ acceptances, certificates of deposit, savings accounts, and other money market instruments. Cash holdings that cover all maturing obligations of a firm are archetypes of sound financial strength. However, credit crunch/recession, information asymmetry, and market imperfections has necessitated that firms hoard cash as optimal cash levels are vague and unpredictable (Drobetz & Grüninger, 2007). The amount of cash set aside by an organization or corporation to satisfy its financial needs is referred to as cash holdings. It is beneficial to businesses when external borrowing is more expensive than internal financing. Cash holdings are described by Zhang (2016) as cash and short-term investments in assets.

Cash Conversion Cycle

According to Besley and Brigham (2005), Cash Conversion Cycle (CCC) refers to the length of time from the payment for the purchase of raw materials to manufacture a product until the collection of account receivable associated with the sale of the product. Keown, et.al (2003) defined CCC as the sum of days of sales outstanding (average collection period) and days of sales in inventory less days of payables outstanding. According to Jordan (2003), the cash cycle refers to, “the number of days that pass before we collect the cash from sale, measured from when we pay for the inventory”. CCC is “the length of time a company’s cash is tied up in working capital before that money is finally returned when customers pay for the products sold or services rendered” (Churchill & Mullins, 2001).

Bodie and Merton (2000) defined the cash cycle time as, “the number of days between the date the firm must start to pay cash to its suppliers and the date it begins to receive cash from its customers”. Tan and Author, (2019) opine that the cash conversion cycle is derived from three components: inventory conversion period, receivable conversion period, and payable deferral period. The cash conversion cycle is a tool used by management to measure the company’s ability to perform working capital management (Hanafi, 2004). It takes into account of the contribution of inventory in influencing the level of corporate profitability in the retail company which supplies a component of the company’s investment is quite large. The cash conversion cycle is the accumulated sum of the period of receivables and inventories less the period of the debt repayment period.

According to Gitman (2010), cash conversion cycle (CCC) means the whole yardstick of assessing the level of utilization of working capital in an organization. It can be described as the total number of days of sales outstanding (which is also termed the average collection period) and the period of sales in days of inventory (also called inventory less period of payable in days outstanding (termed average payable period).

Angahar and Alematu (2014) consider CCC as the calculation of the period it will take between payment and collection of cash. They maintained that CCC is the period, in days, that it takes for the cash to be collected after sales, determined from the time the firm finally made payment for goods. It is simply the number of days that passes before the collection of cash from sales, measured from when organizations pay for inventories. It can be expressed as the accounts receivable period plus inventory period less accounts payable, multiplied by 365 then divided by the cost of sales (Mohamed, 2013). The cash conversion cycle is determined from the time taken to purchase raw materials, through manufacturing until collecting money from the sale of goods on account (Besly, 2000).

Quick Ratio

According to Wardiyah (2017), quick ratio or acid test ratio is a ratio to measure a company’s ability to pay short-term financial liabilities by using liquid assets that are more liquid (Liquid Assets). According to Fahmi (2018), the Quick Ratio (acid test ratio) is often referred to as the fast ratio. The quick ratio is a more rigorous measure of short-term solvency test than the current ratio because the numerator eliminates inventories that are deemed to be slightly illiquid and a possible source of loss. According to Sukamulja (2019), the quick ratio is almost the same as the current ratio, the difference is that the quick ratio does not include inventory in the calculation.

Quick ratio is the ratio used to measure the company’s ability to meet its short-term debt obligations with current assets without taking into account inventory compared to current debt in a company. This ratio also evaluates a company’s liquidity condition. It is also referred to as the “acid test ratio” and is used to determine whether a company has sufficient liquid assets that can be quickly turned into cash to pay its short-term obligations. Only the most liquid current assets and liabilities are included in this ratio (Durrah, 2016). The quick ratio, according to Warrad (2014), is a more stringent indicator of liquidity since it excludes inventory and other assets, such as prepaid expenses, which may not be very liquid.

The quick ratio is one of the most widely used profitability ratios to analyze a company’s financial position. This ratio still uses short-term or current assets and short-term liabilities in its calculation (Dahiyat, 2016). This means that only short-term assets are most quickly used, sold, or converted into cash, to pay off short-term obligations. In contrast, current assets that require more time to be converted into cash are excluded (i.e., inventories). In short, it can be said that this ratio is safer in assessing company liquidity.

Current Ratio

The current ratio is the quotient of current assets and current liabilities. It is used to measure the short-term liquidity of a firm. It depicts the ability of the firm’s management to utilize assets efficiently and effectively. The current ratio is a measure of an entity’s liquidity on the balance sheet. It reflects a company’s ability to meet short-term obligations. The current ratio assesses whether a corporation has sufficient resources to pay its debts over the next 12 months (Idowu & Babatunde, 2022). The current ratio is defined as the ratio of current assets to current liabilities. The current ratio, commonly known as the working capital ratio, assesses a company’s capacity to satisfy its short-term obligations within a year. The ratio takes into account the weight of total current assets versus total current liabilities; it reflects an entity’s financial health by measuring its capacity to pay down short-term commitments with current assets (Naceur, 2017).

The current ratio is thought to be among the simplest and easiest ways to gauge a firm’s liquidity condition, additionally called the working capital ratio. It is a liquidity ratio that assesses a company’s capacity to settle short-term debt or debt with a one-year maturity. According to Lalithchandra et al. (2021), it is a gauge of how effectively a business pays off its short-term obligations. The ratio offers the strongest single signal of whether or not short-term creditors’ claims are guaranteed by assets that are anticipated to be turned into cash within a time frame roughly matching the claims’ maturity (Raveesh, 2011). The weight of total current assets compared to total current liabilities is taken into account in the ratio. It shows how a company’s finances are doing and how it can use its current assets’ maximum liquidity to pay off debt and other obligations. It can be calculated by dividing current assets by current liabilities.

Current Ratio is the ratio used to measure a company’s ability to pay its short-term liabilities by using current assets. The low ratio means the company is unable to pay its liabilities as soon as possible and is unable to take advantage of the cash cut or other expected matters. Whereas a high ratio means that the used money in running a company is held in a government’s securities, savings, cash, or other funds (Gill & Chatton, 2003). The current Ratio is an indication of a company to meet market liquidity and ability to meet the demands of the creditors (Sawir, 2005). The acceptable current ratio varies from industry to industry. If the current ratio of the company is within this range, then it is generally considered to have good short-term financial strength.

The current ratio is defined as the relationship between current assets and current liabilities. It is a measure of general liquidity, and it is the most widely used to analyze short term financial position or liquidity of a firm (Fabozzi & Peterson, 2003). The current ratio can be calculated by dividing the total current assets by the total current liability.

Current ratio can be measured by the company’s working capital position from the profits earned in a certain period where the capital can show the level of security of short-term obligations. The company’s short-term obligations are often met by using current assets such as cash, receivables, securities, or inventories (Sumardewi, 2019). According to Brigham and Houston (2016) current ratio is the ratio calculated by dividing current assets by current liabilities. It indicates the extent to which current liabilities are covered by those assets expected to be converted to cash in the near future. Subramanya (2014) said that the current ratio is a relevant and useful measure of liquidity and short-term solvency, it is subject to certain limitations we must be aware of.

Working Capital

Muniraju and Kumar (2018) defined working capital management (MWC) as all management actions and decisions that usually influence the size and efficacy of working capital. Working capital can be simply defined as the residual value when current assets are subtracted from the short-term liabilities. ICAN (2014) defines working capital as the fund that an entity requires to support its daily operating assets of a business. In Ani, et.al (2012), working capital is the inventory with the ability to be converted or resale so as to make profit.

Ismail, et.al (2015) define working capital in terms of “the portion of a firm’s current assets which are financed from long-term funds”. In the view of Napompech (2012), working capital is described as an amount invested on the entity’s current assets in relation to current liabilities that are used to finance the investment. Working capital management, therefore, is the panacea by which the firm can accomplish the short terms liabilities that are due for payment, and it is a condition that should be fulfilled for the operations

of the entity to be sustained (Mohammed and Nasr, 2010).

Ebenezer and Asiedu (2013) observe that working capital management entails the management and financing of the short-term assets and liabilities of the enterprise. Working capital management (WCM) in the words of Mohammed, et al. (2015) is a process of planning for the acquisition and usage of short-term assets and it is the process of determining the organization’s policy in planning for its current assets and liabilities holdings in financing its routine operations.

In Pandey (2007), working capital management means the administration of the constituents of working capital like payables, cash, receivables, inventories and others. Its focus is on the issues arising from trying to organize the short-term assets between them (Soyemi and Olawale, 2014).

Working capital management comprises the management of current assets and current liabilities, and good working capital management ensures a satisfactory level of working capital at all times (Kumari & Anthuvan, 2017). Working capital is the flow of available funds necessary for the working of a business. It consists of funds invested in current assets, which in the ordinary course of business can be turned into cash within a short period without diminishing in value and without disrupting the organization (Mohanty, 2013). According to Yasdanfar and Ohman (2014) working capital management related to cash management, inventory, and accounts receivable could affect the company’s short-term funding.

2.3.2 Financial Performance

According to Kabethi (2013), financial performance is the process of measuring the results of a Firm’s policies and operations in monetary terms. According to Machiuka (2010) financial performance reflects the financial position of the company, the level of competitiveness in the same sector, and a thorough knowledge about the cost and profit centers within the firm. Financial performance may be defined as a general measure of a company’s overall financial health over a given period of time, and can be used to compare similar companies across the same industry or to compare industries or sectors in aggregation (Maymand, 2014).

Financial performance provides a subjective measure of how well a company can use assets from its primary mode of business and generate revenues. Financial performance is measured by revenues from operations, operating income or cash flow from operations or total unit sales. The analyst or investor may wish to look deeper into financial statements and seek out margin growth rates or any declining debt (Leah, 2008). Financial performance indicators such as ratios include profitability, liquidity, utilization, financial structure, and investment – shareholder ratio (Philip, 2004).

Financial performance is a measure of an organization’s financial condition or financial outcomes resulting from management decisions conducted by organization members (Okobo, et.al 2022).In broader sense, financial performance means the degree to which financial goals are been attained. It is the practice of measuring the results of a firm’s policies and operations in monetary terms. It is used to measure the firm’s financial health over a period of time and can also be used to compare firms across the same industry or to compare industries or units in general.

Measure of Financial Performance

The measure of financial performance adopted in this study is the return on assets. This measure is reviewed as follows:

Return on Assets (ROA)

Return on Assets indicates a company’s net income as a proportion of its total assets available for use (Puspitasari et al., 2021). ROA assesses management’s capacity to generate a return on the firm’s assets; this computation uses income before deducting interest expenses, as interest is a return to creditors for resources provided to the enterprise. The resultant adjusted income sum represents the income before any distributions to individuals who contributed funds to the firm (Supriyadi, 2021).

The ROA is calculated by dividing net income + interest expenditure by the company’s average asset investment for the year.

RESEARCH METHODOLOGY

3.1       Research Design

The study employed Ex-post facto research design. The justification for adopting this design is that requisite data were not manipulated but sourced from secondary materials with a view of gaining deeper information and obtaining good knowledge about the study. The dependent variable for the study is financial performance which was proxied as return on asset while the independent variables are cash and cash equivalent, cash conversion cycle, quick ratio, current ratio, and working capital.

3.2       Population of the Study

There are twenty-six (26) listed consumer goods manufacturing firms listed on the Nigerian Stock Exchange as of 31^st December 2022. This constitutes the population of the study.

3.3       Sample Size and Sampling Techniques

Since the population size is not within the manageable limit, the entire population will not be used in the study. The sample size for the study is 10 listed consumer goods manufacturing firms listed for the period of 5 years i.e. 2018 to 2022. However, 10 listed consumer goods manufacturing firms for a period of 5 years yielded 50 observations.

3.4       Source of Data

The study employed the use of secondary data. Data were generated from the audited annual reports of listed consumer goods manufacturing firms for the years 2018 to 2022. The company’s financial statements were obtained from the Nigerian Exchange Group and sampled companies’ websites. The reason for the period is to get recent data from the sampled listed consumer goods manufacturing firms

3.5       Study Variables and Measurement 

S/N	Variable	Sign	Measurement
1	Return on asset	ROA	Net profit / total assets
2	Cash and cash equivalent	CCE	Cash + Short term deposits
3	Cash conversion cycle	CCC	Days Inventory Outstanding + Days Sales Outstanding – Days Payables Outstanding period
4	Quick ratio	QR	Cash in hand + cash at bank + account receivable + short term investment / current liabilities.
5	Current ratio	CR	Current asset/current liabilities
6	Working capital	WC	Current asset – current liabilities

3.6       Method of Data Analysis

The techniques used in data analysis involved the use of descriptive tests, diagnostic tests, and panel regression tests.

Descriptive statistics test was performed to determine the characteristics of the dependent and independent variables. Descriptive statistics tests are used to understand the nature of the data. Gujarati, (2010) notes that descriptive statistics helps to know the normality distribution of our data through their averages and Jarque-Bera values Correlation test was used to determine the sign and strength of the relationship between the dependent and independent variables.

Diagnostic test carried out include normality test and heterogeneity test. Baseline panel regression analysis was performed using pooled ordinary least square (OLS), random and fixed model effects estimation. These estimations were performed to determine the statistical significance of the hypothetical relationship between the dependent and independent variables.

The Hausman test was employed to select the best fit model (pooled, random or fixed effects). Panel regression results were evaluated using the probability values of the t-statistic and the level/direction of the coefficients. The E-view statistical software 10 version was used in data analysis. The decision rule on the statistical significance of the results obtained was based on the probability values of the t-statistic

Model Specification

In analysing the effect of liquidity management on the financial performance of Nigerian consumer goods manufacturing firms, the model for the study is given below:

ROA = f (LIQ)

ROA = f (CCE, CCC, QR, CR, WC)

Using the ordinary least square structure, the function could be expressed as:

ROA_it= β0 + β1CCE_it+ β2CCC_it+ β3QR_it+ β4CR_it + β5WC_it + µ_it

Where;

ROA = Return on asset

CCE = Cash and cash equivalent

CCC = Cash conversion cycle

QR = Quick ratio

CR = Current ratio

WC = Working capital

β0 = Constant

β1-5 = Parameter Estimate

µ = Error term.

RESEARCH ANALYSIS AND FINDING

4.1 Descriptive analysis

The analytical procedure for this study began by providing the descriptive statistics of each variable included in our model as follows:

Table 4.1 Descriptive Statistics

	ROA	CCE	CCC	QR	CR	WC
Mean	0.198131	9.980868	9.615321	1.053141	4.495665	10.01859
Median	0.109107	9.980376	9.615004	1.076908	4.174343	10.02231
Maximum	1.356435	10.13351	9.866566	1.524623	8.825557	10.17237
Minimum	0.002492	9.705073	9.303906	0.611326	2.219385	9.861692
Std. Dev.	0.269225	0.107948	0.143136	0.225062	1.329125	0.077503
Skewness	2.804300	-0.662734	-0.271095	0.074857	1.110531	-0.105947
Kurtosis	10.87949	2.905092	2.491605	2.120848	4.712242	2.496009
Jarque-Bera	194.8806	3.678901	1.150905	1.656921	16.38519	0.622719
Probability	0.000000	0.158905	0.562450	0.436721	0.000277	0.732450
Sum	9.906534	499.0434	480.7660	52.65704	224.7832	500.9293
Sum Sq. Dev.	3.551625	0.570988	1.003906	2.481990	86.56205	0.294328
Observations	50	50	50	50	50	50

 Source: E-Views 12 Output, Author’s Computation (2024).

Table 4.1 presents the descriptive statistics of the effect of liquidity management on the financial performance of Nigerian consumer goods manufacturing firms during the period of 2018-2022. The table shows that Return on Asset (ROA) has a mean of 0.198 with a standard deviation of 0.269; this implies that the average return on assets of Nigerian consumer goods manufacturing firms from 2018- 2022 was 0.19%.  However, the maximum and minimum value of ROA were 1.356 and 0.0002 respectively. The distribution was positively skewed with the value of 2.80 and the Kurtosis was 10.87 which is greater than 3, indicating that the distribution is leptokurtic.

The mean value of cash and cash equivalent (CCE) was 9.980 with a standard deviation of 0.107, this implies that the average cash and cash equivalent of Nigerian consumer goods manufacturing firms from 2018- 2022 was 0.10%. However, the maximum and minimum of cash and cash equivalent were 10.1 and 9.70 respectively. The distribution was negatively skewed with a value of -0.662 and the Kurtosis was 2.90 which is less than 3, indicating that the distribution is platykurtic.

The mean value of cash conversion cycle (CCC) was 9.615 with a standard deviation of 0.143, this implies that the average cash conversion cycle of Nigerian consumer goods manufacturing firms from 2018- 2022 was 9.61%. However, the maximum and minimum of cash conversion cycle were 9.86 and 9.30 respectively. The distribution was negatively skewed with a value of -0.271 and the Kurtosis was 2.49 which is less than 3, indicating that the distribution is platykurtic.

The mean value of Quick Ratio (QR) was 1.053 with a standard deviation of 0.225, this implies that the average quick ratio of Nigerian consumer goods manufacturing firms from 2018- 2022 was 1.05%. However, the maximum and minimum quick ratio were 1.524 and 0.611 respectively. The distribution was positively skewed with the value of 0.074 and the Kurtosis was 2.120 which is less than 3, indicating that the distribution is platykurtic.

The mean value of current ratio (CR) was of 4.49 with a standard deviation of 1.329, this implies that the average current ratio of Nigerian consumer goods manufacturing firms from 2018- 2022 was 4.4%. However, the maximum and minimum of current ratio was 2.000 and 1.0000 respectively. The distribution was positively skewed with the value of 1.110 and the Kurtosis was 4.712 which are greater than 3, indicating that the distribution is leptokurtic.

The mean value of working capital (WC) was 10.01 with a standard deviation of 10.02, this implies that the average working capital of Nigerian consumer goods manufacturing firms from 2018- 2022 was 10.01%. However, the maximum and minimum working capital were 10.17 and 9.86 respectively. The distribution was negatively skewed with the value of -0.105 and the Kurtosis was 2.49 which is less than 3, indicating that the distribution is platykurtic.

The normality test is important to find out whether the error term follows normal distribution. The test shows that residuals are normally distributed. The normality of residuals is also confirmed by the Jargue-Bera Probability which shows that the p-values in the respective cases are greater than 0.05, indicating normality of the data at a 5% level of significance.

4.2       Correlation Test Result 

Table 4.2 Correlation Matrix

Covariance Analysis: Ordinary
Date: 05/08/24   Time: 10:31
Sample: 2018 2022
Included observations: 50
Correlation
Probability	ROA	CCE	CCC	QR	CR	WC
ROA	1.000000
	—–
CCE	-0.780317	1.000000
	0.0000	—–
CCC	0.615092	0.400605	1.000000
	0.0001	0.0039	—–
QR	-0.210626	0.076444	-0.041687	1.000000
	0.1420	0.5977	0.7738	—–
CR	-0.079027	-0.058565	-0.230502	0.112942	1.000000
	0.5854	0.6862	0.1073	0.4348	—–
WC	-0.453893	-0.382531	-0.441998	0.154593	-0.091189	1.000000
	0.0012	0.0061	0.0013	0.2837	0.5288	—–

Source: E-views 12 Output, 2024

Table 4.2 is the correlation result which indicates that ROA and CCE show that there is a significant positive high correlation in Nigerian consumer goods manufacturing firms. It was also found that ROA and CCC have a significant positive high correlation in Nigerian consumer goods manufacturing firms. The result further shows that ROA and QR have an insignificant negative low correlation in Nigerian consumer goods manufacturing firms, ROA and CR have an insignificant negative low correlation in Nigerian consumer goods manufacturing firms and ROA and WC have a significant negative high correlation in Nigerian consumer goods manufacturing firms.

4.3       Fixed Effect Likelihood Ratio Test

The test checked if the error terms were correlated with the regressors. Thus, the decision rule for the fixed effect likelihood ratio specification is stated; thus, at 5% Level of significance:

H₀: Pooled effect is most appropriate for the Panel Regression analysis

H₁: Fixed effect is most appropriate for the Panel Regression analysis

As encapsulated above, if the p-value is less than 0.05 the decision rule is to reject the null hypothesis which states that pooled effect is most appropriate for the panel regression analysis (meaning that the fixed effect model is the most appropriate model). Similarly, if the p-value is greater than 0.05 the decision rule is to accept the null hypothesis which states that pooled effect is most appropriate for the panel regression analysis (meaning that the fixed effect model is to be rejected).

Table 4.3: Fixed Effect Likelihood Ratio Table

Redundant Fixed Effects Tests
Equation: Untitled
Test cross-section fixed effects
Effects Test	Statistic	d.f.	Prob.
Cross-section F	7.305787	(9,35)	0.0000
Cross-section Chi-square	52.865740	9	0.0000

Source: E-View 12 Output (2024)

The Result of fixed effect likelihood ratio test shows that chi-square statistics value is 7.305 while the probability values of is 0.0000. This signifies that there is enough evidence to reject the null hypothesis which states that pooled effect is most appropriate for the panel regression analysis. It thus stands that error component model (pooled effect) estimator is not appropriate because the pooled effects are probably correlated with one or more regressors. Thus, the most consistent, appropriate and efficient estimation for the study, given the options of a pooled effect analysis and a fixed effect analysis, is the fixed effect model of regression analysis. Consequently, the result suggests that the fixed effect regression model is most appropriate for the sampled data, this is because the likelihood ratio test statistics as represented by the corresponding probability value is less than 5%. It is now logical then to proceed to another test which is the Hausman test, which will show the appropriateness of otherwise of using the fixed effect model or the random effect model.

4.1.4    Hausman Test

The Hausman test is a test for specification of model in the panel data analysis and this is a test that is used to choose between two panel regression analysis which are fixed effects model and random effects model. Because of the panel nature of the data set utilized in this study, both fixed effect and random effect regressions were run. Hausman specification test were then conducted to choose the most appropriate and preferred model between the fixed effect and the random effect regression models. The test checked if the error terms were correlated with the regressors. Thus, the decision rule for the Hausman specification test which is similar to that of the likelihood ratio is stated thus; at a 5% Level of significance:

H₀: Random effect is most appropriate for the Panel Regression analysis

H₁: Fixed effect is most appropriate for the Panel Regression analysis

As mentioned, and listed above, if the p-value is less than 0.05 the decision rule is to reject the null hypothesis which states that random effects model is most appropriate for the Panel Regression analysis (meaning that the preferred model is fixed effects model). Similarly, if the p-value is greater than 0.05 the decision rule is to accept the null hypothesis which states that random effect model is most appropriate for the Panel Regression analysis (meaning that the fixed effect model is to be rejected). Based on the Hausman specifications test result, the study however used fixed effect model which is more appropriate for the study.

Table 4.4 Hausman Test Result

Correlated Random Effects – Hausman Test
Equation: Untitled
Test cross-section random effects
Test Summary		Chi-Sq. Statistic	Chi-Sq. d.f.	Prob.
Cross-section random		13.30741	5	0.0007
Cross-section random effects test comparisons:
Variable	Fixed	Random	Var(Diff.)	Prob.
CCE	0.981346	-0.476223	0.004417	0.9385
CCC	-0.654050	0.080042	0.002929	0.6310
QR	-0.083700	-0.094874	0.000218	0.4487
CR	0.124085	0.016700	0.000132	0.5203
WC	-0.467736	-0.451912	0.008145	0.8608
Cross-section random effects test equation:
Dependent Variable: ROA
Method: Panel Least Squares
Date: 05/08/24   Time: 10:38
Sample: 2018 2022
Periods included: 5
Cross-sections included: 10
Total panel (balanced) observations: 50
Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	9.148601	7.761969	1.178644	0.2465
CCE	0.981346	0.331820	2.957464	0.0008
CCC	-0.654050	0.261601	-2.500181	0.0012
QR	-0.083700	0.126143	-0.663530	0.5113
CR	0.124085	0.032994	3.760835	0.0003
WC	0.467736	0.068761	6.802344	0.0000
	Effects Specification
Cross-section fixed (dummy variables)
R-squared	0.683090	Mean dependent var		0.198131
Adjusted R-squared	0.556326	S.D. dependent var		0.269225
S.E. of regression	0.179328	Akaike info criterion		-0.355879
Sum squared resid	1.125545	Schwarz criterion		0.217728
Log likelihood	23.89697	Hannan-Quinn criter.		-0.137446
F-statistic	5.388679	Durbin-Watson stat		1.662307
Prob(F-statistic)	0.000026

Source: E-views 12 Output, 2024

The Result of Hausman test shows that chi-square statistics value is 13.307 while the probability values of is 0.0000. This signifies that the null hypothesis is to be rejected which states that random effect is most appropriate for the Panel Regression analysis. It thus stands that error component model (fixed effects model) estimator is the most appropriate because the fixed effects model are well correlated with the regressors. Thus, the most consistent and efficient estimation for the study is the fixed effect model. Consequently, the result suggests that the fixed effect regression model is most appropriate for the sampled data because the Hausman test statistics as represented by corresponding probability value is less than 5%.

The coefficient of multiple determinations (R²) is 0.683 and in line with the panel nature of the data used in this study, the regression model shows that the range of values between adjusted R² and R² falls between 55.6%, and 68.3% respectively. This indicates that about 68.3% of the total variations in return on asset (ROA) is explained by the variations in the independent variables (cash and cash equivalent, cash conversion cycle, quick ratio, current ratio and working capital), while the remaining 31.7% of the variation in the model is captured by the error term, which further indicates that the line of best fit is highly fitted.

The F-stat value is used to test for the goodness of fit of the model and it serves as a test of the joint statistical significance of all the variables examined together and also tests the existence of a significant linear relationship between the dependent and independent variables. The decision rule is to accept the F-stat as significant if the probability value is less than 0.05 otherwise it is rejected. The F-stat (5.3888) and p-value (0.000) indicate joint significance at 5% level of significance.

The Durbin Watson statistics is used to test for the existence of first order serial correlation between successive units of the error term. As a rule of thumb, if the Durbin Watson statistics is less than 1.5, there is presence of first order serial correlations. The Durbin Watson statistics of 1.6 indicates the absence of serial correlation of the residuals in the model.

4.2       Test of Hypotheses

4.2.1: Hypothesis One

H₀₁: Cash and cash equivalents exert no significant impact on the financial performance of consumer goods firms.

The study showed that cash and cash equivalents have positive and significant effect on return on asset (ROA). It can be observed that 1 unit increase in cash and cash equivalents will increase ROA by 0.98. The positive relationship between CCE and ROA shows that cash and cash equivalents have significant effect on ROA this can be seen from the probability value which is less than 0.05% level of significance. Based on the above the null hypothesis was rejected and therefore concluded that cash and cash equivalents exert significant impact on financial performance of consumer goods firms.

4.2.2: Hypothesis Two

H₀₂: There is no significant relationship between cash conversion cycle and financial performance of consumer goods in Nigeria

The study showed that cash conversion cycle (CCC) has negative and significant effect on return on asset (ROA). It can be observed that 1 unit increase in CCC will decrease ROA by 0.65. The negative relationship between CCC and ROA shows that cash conversion cycle has significant effect on ROA this can be seen from the probability value which is less than 0.05% level of significance. Based on the above the null hypothesis was rejected and therefore concluded that there is significant relationship between cash conversion cycle and financial performance of consumer goods in Nigeria.

4.2.3: Hypothesis Three

H₀₃: There is no significant relationship between the quick ratio and financial performance in consumer goods firms in Nigeria.

The study showed that quick ratio (QR) has negative and insignificant effect on return on asset (ROA). It can be observed that 1 unit increase in QR will decrease ROA by 0.083. The negative relationship between QR and ROA shows that quick ratio has no significant effect on ROA this can be seen from the probability value which is less than 0.05% level of significance. Based on the above the null hypothesis was accepted and therefore concluded that there is no significant relationship between quick ratio and financial performance of consumer goods firms in Nigeria.

4.2.4: Hypothesis Four

H₀₄: There is no significant impact of current ratio on the financial performance of consumer goods firms in Nigeria

The study showed that current ratio (CR) has positive and significant effect on return on asset (ROA). It can be observed that 1 unit increase in CR will increase ROA by 0.12. The positive relationship between CR and ROA shows that quick ratio has significant effect on ROA this can be seen from the probability value which is less than 0.05% level of significance. Based on the above the null hypothesis was rejected and therefore concluded that there is significant impact of current ratio on financial performance of consumer goods firms in Nigeria.

4.2.5: Hypothesis Five

H₀₅: There is a no significant impact of working capital on the financial performance of consumer goods firms in Nigeria

The study showed that working capital (WC) has positive and significant effect on return on asset (ROA). It can be observed that 1 unit increase in WC will increase ROA by 0.46. The positive relationship between WC and ROA shows that working capital has significant effect on ROA this can be seen from the probability value which is greater than 0.05% level of significance. Based on the above the null hypothesis was rejected and therefore concluded that there is significant impact of working capital on financial performance of consumer goods firms in Nigeria.

4.3       Discussion of Findings

The study discovered that cash and cash equivalents exert positive and significant impact on financial performance of consumer goods firms. This is supported by the findings of Naveed et.al (2020) that cash holding has a significant impact on firm performance. Odo and Ohazuluike (2020) that cash from operating activities significantly affect profit for the year of food and beverage firms in Nigeria. Tonye et.al (2020) that operating and investing cash flow has a significant positive relationship with the performance of companies in the Consumer Goods Sector of Nigeria. Amahalu and Ezechukwu (2017) that direct and significant influence of the operating cash flow on the Return on Equity (ROE). Ndubuisi and Ezechukwu (2017) that cash holding (proxy by cash to total book value of assets and cash) has a positive and statistical significant effect on financial performance

It was noted that there is negative and significant relationship between cash conversion cycle and financial performance of consumer goods in Nigeria. This is inline with the findings of Ibrahim and Dengel (2022) that cash conversion cycle has a negative and significant impact on the value of listed oil and gas companies in Nigeria. Obalemo (2020) that Cash Conversion Cycle (CCC) has a significant negative relationship with ROA. Ade Rizky (2018) that CCC has negative effect to firm profitability. Yasiret al. (2014) that negative relationship between firms’ cash conversion cycle and profitability. Chuke, et.al (2018) that CCC had negative and significant effect on profitability. Nguyen and Sundaresan (2018) that cash conversion cycle (CCC) has a significant inverse relationship with profitability in the agriculture and food companies in Thailand. Takon (2013) that cash conversion cycle had a significant negative relationship with profitability

Furthermore, that there is negative and no significant relationship between quick ratio and financial performance of consumer goods firms in Nigeria. The findings of the study concur with the findings of Aniyah et.al (2020) that there is a positive and insignificant effect between quick ratio and return on equity.

The study confirmed that there is positive and significant impact of current ratio on financial performance of consumer goods firms in Nigeria. This is supported by the findings of Odendo (2024) that current ratio has significant effect on financial performance of the Agricultural firms listed at the Nairobi Securities Exchange, Kenya. Deti et.al (2022) that current Ratio have a significant positive effect on Profitability (ROA) in Pharmaceutical Sub-Sector Manufacturing Companies Listed on the Indonesia. Ariesa, et.al (2020) that current ratio, among others, had a significant effect on the variable stock prices of manufacturing companies. Hermanto (2018) that current ratio and debt-to-equity ratio had a significant effect on return on equity and return on asset

More so, it was noted that there is positive and significant impact of working capital on financial performance of consumer goods firms in Nigeria. The findings of the study is in agreement with the findings of Anene et.al (2023) that working capital management have statistical significant effect on the financial performance (Return on Assets) of firms listed on the natural recourses sector of Nigeria exchange limited. Aduwo and Aduwo (2023) that working capital management has significance effect on the measures of corporate performance. Adekanbi and Oluwadare (2019) that working capital management affected firms’ profitability in Nigeria. Osuma (2018) that working capital management has a significant effect on the profitability of the selected banks. Dauda, (2015) that significant and positive relationship exist between the working capital management and the profitability of the DMBs in Nigeria.

CONCLUSION AND RECOMMENDATIONS

5.1 Conclusion

Two vital and ever-changing characteristics of a corporation are liquidity and financial success. While a non-liquid business will unintentionally close, a loss-making firm will be labeled as unwell. Thus, liquidity has evolved into a fundamental and all-encompassing factor when assessing a business entity’s success. As a result, it’s important to keep a company’s liquidity levels in check; they should be sensible enough to support operations without building up idle capital.

Therefore, effective liquidity has always been essential for corporate firms to operate smoothly. Liquidity requirements of a firm depend on the uniqueness of the firm with no specifics in the determination of the appropriate levels of liquidity to be maintained by such firms. This study therefore concludes that liquidity influences the financial performance of quoted consumer goods companies in Nigeria.

5.2 Recommendations

Based on the findings of this study, the following recommendations are made:

First, Understanding that liquidity management is sector-specific, managers of consumer products companies are urged to conduct market research. Comprehending distinct industry characteristics helps direct the creation of customized liquidity management tactics that correspond with the demands and obstacles of Nigeria’s consumer goods manufacturing business.

Secondly, It’s critical to combine effective risk management techniques with liquidity methods, particularly in times of financial crisis. To improve overall financial resilience, the management of consumer products companies in Nigeria should think about implementing a comprehensive strategy that synchronizes risk reduction measures with liquidity forecasts.

Thirdly, Companies that sell consumer products need to make significant investments in cash flow monitoring systems to guarantee precise forecasting and prompt decision-making. Improved financial performance may be attained by proactive short-term investments and regular evaluations of liquidity levels.

Lastly, Management of consumer products companies should think about expanding their collection of liquidity measurements beyond the conventional current and acid test ratios. A more thorough understanding of liquidity dynamics and their effect on profitability may be obtained by investigating other ratios and liquidity indicators.

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