# Impact Of Liquidity And Leverage On Profitability Finance Essay

An organization is requisite to sustain a stability between liquidity and profitability while accomplishing its day to day processes. Liquidity is a requirement to make sure that organizations are capable to gather its short-term debt and its constant flow can be guaranteed as of a beneficial venture. The significance of cash as a sign of ongoing financial strength should not be unexpected in view of its critical role within the industry. It needs that business ought to be run both competently and profitably. In the procedure, an asset-liability create variance may take place which may enhance organization’s profitability in the short run but at a risk of its liquidation. On other way, too much focus on liquidity would be at the outflow of profitability and it is ordinary to find finance textbooks (for e.g see Gitman, 1984 and Bhattacharya, 2001) initiate their working capital segments with an argument of the risk and return tradeoffs fundamental in substitute of working capital guiding principle. Consequently, the administrator of a business entity is in a problem of accomplishing desired tradeoff between liquidity and profitability in sort of capitalizing on the value of an organization.

Undersized businesses are analyzed as an important factor of a vigorous and vibrant economy. They are seen as fundamental to the promotion of a venture civilization and to the formation of jobs within the financial system (Bolton Report, 1971). Small Medium-Sized Enterprises (SMEs) supposed to grant an impetus to the financial development of increasing countries and its magnitude is gaining widespread identification. Similarly in Mauritius the SMEs occupy a vital place in the economy, accounting for 90% of industry stock (those employing up to 50 employees) and make use of approximately 25% of private segment employees (Wignaraja and O’Neil, 1999; CSO, 2003; NPF, 2004). Storey (1994) notes that undersized industries, on the other hand, they are distinct, amount to the bulk of venture in all financial system in the world. Though, given their dependence on short-term finances, it has long been documented that the creative administration of working capital is critical for the survival and growth of small firms (Grablowsky, 1984; Pike and Pass, 1987). A large number of business breakdown have been recognized to inability of financial managers to plan and organize properly the current assets and current liabilities of their relevant firms (Smith, 1973).

several research studies have been carry out on the functioning capital management carry out of both large and small firms in India, UK, US and Belgium using any a survey based approach (Burns and Walker, 1991; Peel and Wilson, 1996) to identify the push factors for firms to adopt good working capital practices or econometric analysis to investigate the association between WCM and profitability (Shin and Soenen, 1998; Anand, 2001; Deloof, 2003).

Leverage and profitability

Debt is one of the tools used by many companies to leverage their capital in order to increase profit. However, the affectivity of debt to increase profitability varies between companies. The ability of the company’s management to increase their profit by using debt indicates the quality of the management’s corporate governance. Good corporate governance shows the companies’ performance on their use of debt to increase their profit (Maher and Andersson, 1999).

One method that can be used to measure the effectiveness of debt to maximize the profit is by using Du Pont chart analysis. Du Pont chart analysis can describe the relationship between profitability and the use of debt as reflected by return on equity ratio of a company. The proper use of debt can raise the return on equity ratio. This means that the company’s management can make use of the debt to increase the profit. It also can indicate the ability of company’s management to maximize its operation on assets in making profit (Brigham and Ehrhardt, 2005).

However, profitability might not only be affected by debt. Other factors might affect the profitability of the companies whether they are internal factors or external factors. Internal factors are reflected by operating decisions and companies’ size, while external factors are reflected by the type of industry that the companies run its business and the macro factors that might affect directly to the companies’ performance.

Profitability can be affected by operating decisions when the assets are used effectively to increase profit. Operating decisions can indicate the effectiveness of the companies’ management in making the profit from the assets used. Therefore operational efficiency can be achieved by dividing sales or revenue with total assets (Sari, 2007). However, to increase the assets to generate more profits, companies might use leverage. One type of leverage that companies use is debt. When debt is used to expand the companies by adding more operational assets, then it can generate more cash flows which are expected to increase the value of return on equity ratio (Brigham and Ehrhardt, 2005).

Moreover, return on equity can also be useful in comparing the profitability of the company to the other company in the same industry (www.investopedia.com). This is important because different industry might produce different profitability. As it is explained by Michael Porter that industry presents different pattern of profitability due to different forces that the industry exposed to such as concentration, entry barriers, and growth (Spanos, Zaralis, and Lioukas, 2004).

1.1 Knowledge Gap

There are number of researches focusing on the impact of liquidity and leverage on the profitability. But none of the research till now works on the efficiency of liquidity and leverage to increase the profitability of the organization in the food industry of Pakistan. That shows clearly a gap and this study intended to fill the said gap.

1.2 Problem statement

The study is focusing to investigate the impact of liquidity and leverage on the profitability of the organization.

1.3 Objective of the study

The study has the following specific objectives:

To find out the linkage between liquidity, leverage and profitability.

To assess the influence of liquidity and leverage on profitability.

To ascertain the most prominent liquidity and leverage dimensions that affect profitability.

To study the direct and indirect effect of liquidity and leverage variable on profitability.

1.4 Significance of the study

Topic of leverage liquidity and profitability is today burning issue. This study will help managers of these two companies and other companies to generate profits in favor of their corporation by conducting appropriately the liquidity and leverage and keeping each different component and many others to an optimum level.

Most firms have a great quantity of cash spended in working capital through financing mix. It can for that reason be estimated that the method in which liquidity and leverage are managed will have a significant impact on the profitability of firms.

## 2. REVIEW OF LITERATURE:

The literature review (1) introduces the subject of (study influence of working capital management or liquidity and leverage on profitability). (2) Highlights the problem (that we do not have a good conceptual framework for understanding what working capital leverage and profitability is). (3) Summarizes the work done so far on the topic in a manner that convinces the reader that the researcher has done.

2.1 LIQUIDITY AND PROFITABILITY

The liquidity is express as the “managing current assets and current liabilities, and financing these current assets.” Liquidity is important for creating value for stockholders. Management of Liquidity was found to have a significant impact on profitability in studies in different countries

To find out the relationship between liquidity and profitability, Deloof [5, p. 573] used an analysis of 1,009 immense Belgian non-financial organizations for a period of 1992-1996. By using correlation and regression tests, he found significant negative relationship between earnings before interest and tax and the average collection time, inventories, with accounts payable of Belgian rigids. On the grounds of study results, he proposes that managers can increase the profitability of the corporate firm by reducing average collection period and inventories.

Ghosh and Maji [8, p. 1] challenged to examine the effectiveness of working capital organization of Indian cement companies during 1992 - 93 to 2001 - 2002. They calculated three index values - performance index, utilization index, and on the whole efficiency index to measure the effectiveness of liquidity management, instead of using some common liquidity ratios. By using regression analysis and industry norms as a target efficiency level of individual firms, Ghosh and Maji [8] experienced the momentum of achieving that target level of competence by individual firms during the period of study and found that some of the sample firms successfully improved efficiency during these years.

Eljelly [9] empirically examined the association among profitability and liquidity, as calculated by current ratio plus cash break (cash conversion cycle) on a test of 929 joint stock corporations in Saudi Arabia. by correlation as well as regression analysis, Eljelly [9] found significant negative relationship between the firm's profitability along with its liquidity intensity, as calculated by current proportion. This correlation is more obvious for organizations with high current ratios and long cash conversion cycles. At the industry level, however, he found that the cash alteration cycle or the cash gap is of more significance as a determine of liquidity than current ratio that affects productivity. The firm size variable was also found to have significant effect on profitability at the industry level.

Lazaridis and Tryfonidis [1, p. 26] conducted a cross sectional study by using a sample of 131 firms listed on the Athens Stock Exchange for the period of 2001 - 2004 and found statistically significant relationship between profitability, measured through gross operating profit, and the cash conversion cycle and its components (accounts receivables, accounts payables, and inventory). Based on the results analysis of annual data by using correlation and regression tests, they suggest that managers can create profits for their companies by correctly handling the cash conversion cycle and by keeping each component of the conversion cycle (accounts receivables, accounts payables, and inventory) at an optimal level.

Raheman and Nasr [2, p. 279] studied the effect of different variables of working capital management including average collection period, inventory turnover in days, average payment period, cash conversion cycle, and current ratio on the net operating profitability of Pakistani firms. They selected a sample of 94 Pakistani firms listed on Karachi Stock Exchange for a period of six years from 1999 - 2004 and found a strong negative relationship between variables of working capital management and profitability of the firm. They found that as the cash conversion cycle increases, it leads to decreasing profitability of the firm and managers can create a positive value for the shareholders by reducing the cash conversion cycle to a possible minimum level.

Falope and Ajilore [10, p. 73) used a sample of 50 Nigerian quoted non-financial firms for the period 1996 -2005. Their study utilized panel data econometrics in a pooled regression, where time-series and cross-sectional observations were combined and estimated. They found a significant negative relationship between net operating profitability and the average collection period, inventory turnover in days, average payment period and cash conversion cycle for a sample of fifty Nigerian firms listed on the Nigerian Stock Exchange. Furthermore, they found no significant variations in the effects of working capital management between large and small firms.

Mathuva [11, p. 1] examined the influence of working capital management components on corporate profitability by using a sample of 30 firms listed on the Nairobi Stock Exchange (NSE) for the periods 1993 to 2008. He used Pearson and Spearman’s correlations, the pooled ordinary least square (OLS), and the fixed effects regression models to conduct data analysis. The key findings of his study were that: i) there exists a highly significant negative relationship between the time it takes for firms to collect cash from their customers (accounts collection period) and profitability, ii) there exists a highly significant positive relationship between the period taken to convert inventories into sales (the inventory conversion period) and profitability, and iii) there exists a highly significant positive relationship between the time it takes the firm to pay its creditors (average payment period) and profitability.

In summary, the literature review indicates that working capital management impacts on the profitability of the firm but there still is ambiguity regarding the appropriate variables that might serve as proxies for working capital management. The present study investigates the relationship between a set of such variables and the profitability of a sample of unilever and nestle firms.

2.2 Hypothesis

H0: There is no significant relationship between liquidity and profitability.

H1: There is significant relationship between liquidity and profitability.

2.3 LEVERAGE AND PROFITABILITY

There has been several capital structure studies conducted in the hospitality industry.

Sheel (1994) was one of the pioneers, reporting that collateral value of assets would be the most significant determinant of long-term debt in his research on hotel and manufacturing firms. Kim (1997) investigated the determinants of restaurant capital structure. In the study, seven variables (size, earning volatility, profitability, growth opportunities, non-debt tax-shield, percentage of franchise, and lease expense) were regressed against short-term, long-term and total debt of restaurant firms. The significant determinants for long-term debt were firm size, growth opportunities, and lease expenses. All three predictors were negative. In other words, smaller restaurant firms having fewer growth opportunities and spending less on leases were more likely to use long-term debt (Kim, 1997). Using a pooled regression analysis, Dalber and Upneja (2002) summarized theories related to debt maturity and debt selection (contracting costs of debt, signaling effects, and tax effects). Firms with growth opportunities should need less long-term debt because they make more discretionary investments and they are not willing to pay the relatively high fixed costs of high interest payments.

Long-term debt tends to send the wrong signal about a firm’s market value; low-quality firms may take advantage of mispricing because investors are not able to distinguish them from high-quality firms. In terms of tax effects, a firm with a higher tax rate tends to use more long-term and more risky debt. Tax rates also can be used as a proxy for the firm’s financial stress or distress. In empirically testing these theories, results showed that larger restaurant firms with low growth opportunities and with a higher probability of bankruptcy use more long-term debt because they don’t want benefits to accrue to bondholders, they can afford the higher fixed costs of long-term debt, and they are willing to take advantage of mispricing. Moreover, riskier restaurant firms tend to use more long-term debt (Dalber & Upneja, 2002).

Pinegar and Wilbricht (1989) surveyed Fortune 500 firms, only 31 % of the firms reported that they used target capital structure. Hittle, Haddad, and Gitman (1992) surveyed the 500 largest Over-The-Counter firms and found that only 11 % of the surveyed firms used target capital structure. Furthermore, when both taxes for corporate and equity holders were considered at the same time, financial leverage appeared not to bring significant benefits to the investors at the end (Myers, 2001). Although this is difficult to explain under the agency cost/tax shield trade-off theory, Sunder and Myers (1999) explained that the most profitable firms in many industries often have the lowest debt ratio, which is very different from predictions using the trade-off theory.

Dann (1981) and James (1987) also noted that large positive abnormal returns for a firm’s stockholders are associated with leverage increasing events such as stock repurchases or debt-for-equity exchanges instead of 4 leverage-decreasing events such as issuing stock. Few American companies issue new stock as frequently as once per decade (Megginson, 1997). In contrast to the trade-off theory, the pecking order theory of capital structure states that firms have a preferred hierarchy for financing decisions. The highest preference is to use internal financing such as retained earnings, before resorting to any form of external funding. If a firm uses external funding, the order of preference is debt, convertible securities, preferred stock, and common stock (Myers, 1984). This order reflects the motivation of a financial manager to reduce the agency costs of equity, retain control of the firm, and avoid the

seemingly inevitable negative market reaction to an announcement of a new equity issue (Hawawini & Viallet, 1999).

Titman and Wessels (1988) observed that highly profitable firms have lower levels of leverage than less profitable firms because they first use their earnings before seeking outside capital. In addition, stock prices reflect how the firm performs. Firms tend to issue equity rather than use debt when their stock price increases, so that their leverage levels stay lower than firms using debt. Similar findings were reported more recently in Gu (1993), Sheel (1994), Sunder & Myers (1999) and Wald (1999). According to Wald (1999), profitability, which is the most significant determinant of firms’ financial leverage, negatively affects the debt to asset ratios in the heteroskedastic Tobit regression model. Sheel (1994) also supported the negative relationship between debt-to-asset ratio and non-debt tax shield or/and between firm’s leverage behavior and its past profitability. Specific to the restaurant industry, Gu (1993) found that the fine dining restaurant segment, which uses debt lightly compared to the fast-food restaurant and the economy/family restaurant segments, has the highest percentage of profit margin and of return on assets. The research concluded that medium debt use may not be the optimal capital structure but little or no debt use may be optimal. Because of the characteristics of the food service industry, such as its vulnerability to seasonality and economic adversity, using debt could bring greater risk than for those firms in industries where cost of debt may be lower than restaurant industry (Gu, 1993).

2.4 Hypothesis

H0: There is no significant relationship between leverage and profitability.

H1: There is significant relationship between leverages and profitability.

## 3. METHODOLOGY

3.1 Research Design

This study is causal in nature In this study the researcher examine financial reports of food industry in Pakistan in order to find out the fact that how much liquidity management and leverage affect profitability of food sector, for this purpose liquidity ratios as current ratio and quick ratios are taken while for the computation of leverage debt to equity ratio and debt to total assets ratios are used and check their influence on the profitability ratios {as net profit margin, return on equity, return on asset and total asset turn over ( efficiency ratio )} are used. To conduct appropriate inferences use spss software

3.2 Sources of Data

In this study secondary, descriptive and quantitative data will be used. Data is gathered from annual reports of food industries published for five years. Income statement and Balance sheet specifically have been taken for the computation of ratios for results, findings and inferences.

3.3 Sample Size

The sample size is selected for sampling are the food industry in Pakistan companies.

## Regression

## Variables Entered/Removedb

Model

Variables Entered

Variables Removed

Method

1

DTTA, ATRATIO, DTE, CRATIOa

## .

Enter

a. All requested variables entered.

b. Dependent Variable: NPM

## Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.041a

.002

-.045

109.44131

a. Predictors: (Constant), DTTA, ATRATIO, DTE, CRATIO

Interpretation:

B y .002 % change in independent variable makes the change in the dependent variable

## ANOVAb

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

1733.683

4

433.421

.036

.997a

Residual

1018078.967

85

11977.400

Total

1019812.650

89

a. Predictors: (Constant), DTTA, ATRATIO, DTE, CRATIO

b. Dependent Variable: NPM

interpretation:

Interpretation:

There is no significant relationship between independent variable and dependent variable.

## Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

8.207

19.800

.414

.680

CRATIO

.024

.199

.026

.119

.906

ATRATIO

-.027

.234

-.025

-.117

.907

DTE

-.006

.019

-.037

-.341

.734

DTTA

-.651

8.420

-.009

-.077

.939

a. Dependent Variable: NPM

Interpretation:

There is no significant impact of independent variables on dependent variable.

## Variables Entered/Removedb

Model

Variables Entered

Variables Removed

Method

1

DTTA, ATRATIO, DTE, CRATIOa

## .

Enter

a. All requested variables entered.

b. Dependent Variable: ROA

## Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.162a

.026

-.020

53.48311

a. Predictors: (Constant), DTTA, ATRATIO, DTE, CRATIO

Interpretation:

B y .026 % change in independent variable makes the change in the dependent variable

## ANOVAb

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

6526.565

4

1631.641

.570

.685a

Residual

243137.678

85

2860.443

Total

249664.243

89

a. Predictors: (Constant), DTTA, ATRATIO, DTE, CRATIO

b. Dependent Variable: ROA

Interpretation:

There is no significant relationship between independent variable and dependent variable.

## Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

3.727

9.676

.385

.701

CRATIO

.107

.097

.235

1.100

.274

ATRATIO

-.062

.115

-.114

-.539

.592

DTE

.003

.009

.035

.326

.745

DTTA

-1.614

4.115

-.043

-.392

.696

a. Dependent Variable: ROA

Interpretation:

There is no significant impact of independent variables on dependent variable.

## Variables Entered/Removedb

Model

Variables Entered

Variables Removed

Method

1

DTTA, ATRATIO, DTE, CRATIOa

## .

Enter

a. All requested variables entered.

b. Dependent Variable: ROE

## Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.702a

.493

.470

152.41653

a. Predictors: (Constant), DTTA, ATRATIO, DTE, CRATIO

Interpretation:

B y .493 % change in independent variable makes the change in the dependent variable

## ANOVAb

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

1923467.977

4

480866.994

20.700

.000a

Residual

1974617.887

85

23230.799

Total

3898085.864

89

a. Predictors: (Constant), DTTA, ATRATIO, DTE, CRATIO

b. Dependent Variable: ROE

Interpretation:

There is significant relationship between independent variable and dependent variable.

## Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

-25.736

27.575

-.933

.353

CRATIO

.238

.277

.132

.858

.393

ATRATIO

-.163

.326

-.076

-.500

.619

DTE

.234

.026

.705

9.057

.000

DTTA

-2.134

11.726

-.014

-.182

.856

a. Dependent Variable: ROE

Interpretation:

“There is no significant impact of CRATIO, ATRATIO & DTTA on dependent variable.”

“There is significant impact of DTE on dependent variable”

## Variables Entered/Removedb

Model

Variables Entered

Variables Removed

Method

1

DTTA, ATRATIO, DTE, CRATIOa

## .

Enter

a. All requested variables entered.

b. Dependent Variable: TATO

## Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.666a

.444

.418

2.39025

a. Predictors: (Constant), DTTA, ATRATIO, DTE, CRATIO

Interpretation:

B y .444 % change in independent variable makes the change in the dependent variable

## ANOVAb

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

387.559

4

96.890

16.959

.000a

Residual

485.631

85

5.713

Total

873.190

89

a. Predictors: (Constant), DTTA, ATRATIO, DTE, CRATIO

b. Dependent Variable: TATO

Interpretation:

There is significant relationship between independent variable and dependent variable.

## Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

.131

.432

.304

.762

CRATIO

.035

.004

1.294

8.022

.000

ATRATIO

-.035

.005

-1.108

-6.926

.000

DTE

.001

.000

.107

1.317

.191

DTTA

-.034

.184

-.015

-.185

.854

a. Dependent Variable: TATO

Interpretation:

“There is no significant impact of DTE & DTTA on dependent variable.”

“There is significant impact of CRATIO & ATRATIO on dependent variable”

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