Impact of Working Capital Management on Performance of Listed Non

Impact of Working Capital Management on Performance 1

Proceedings of 2nd

International Conference on Business Management (ISBN: 978-969-9368-06-6)

IMPACT OF WORKING CAPITAL MANAGEMENT ON PERFORMANCE

Impact of Working Capital Management on Performance of Listed Non Financial Companies of

Pakistan: Application of OLS and LOGIT Models

Irfan Ahmed

University of Sargodha, Sargodha, Pakistan


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Abstract

Present study investigates the impact of working capital management on the performance of the

firm using a sample of 253 non financial listed companies of Karachi Stock Exchange (KSE),

Pakistan. The study used secondary data taken from Balance Sheet Analysis of Stock Listed

Companies on KSE published by State Bank of Pakistan. Results were analyzed by using the

Logistic Regression, OLS Regression and Pearson Correlation techniques. The result suggests

that out of the five selected components of working capital management only current asset over

total sales showed significant negative relationship with both the proxies of performance i.e.

return on equity and return on assets. While current asset over total asset (CATA), inventory

turnover, debtor’s turnover and current ratio showed significant positive relationship with

performance. Logistic regression results suggested that probability of firm being in profit is

highly determined by CATA, CATS and CR.

KEYWORDS: Working capital management, Profitability, OLS regression, Logistic regression.

1. Introduction


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WCM is among the most important decisions taken by the financial manger. It directly affects

the profitability and is considered one of the most important parts of financial decision making

(Haq et al 2011). Net working capital is the excess of current assets over current liabilities of a

firm. It determines the strength of the business and its liquidity position means more the working

capital more the liquidity of the firm. WCM could be permanent or temporary; former is the

amount of current assets company must possess for longer period of time to offset its current

liabilities while later is the excess of current assets to meet seasonal current liabilities (Van Horn

2005).

According to Raheman and Nasr, (2007) Management of current assets to meet short term

obligations of the company is WCM. Objective of the WCM is to make sure that firm meets the

operating requirements and also remain in a position to pay short term debt when they fall due

(Mohamad & Noriza 2010). Mismanagement of working capital will lead a firm to liquidity

crisis by reducing its profitability and creditability, so managing working capital effectively is

necessary for going concern of the business and also for its profitability (Siddique & Khan

2009). Earlier we have classified WCM as temporary and permanent, now we are classifying it

as aggressive and conservative. Aggressive WCM refers to the firm’s strategy of having fewer

current assets in proportion of total assets or having high proportion of current liabilities as

compared with the total liabilities of the company. It leads a company to low liquidity or higher

profitability (Van horne & Machowicz 2004). Conservative WCM technique appears with the

philosophy of using long term source should be used for the entire investment in the current

assets and short term should be used only for urgent situations. Distinct features of conservative

WCM are increased liquidity and less risk but more interest has to be paid on the seasonal

requirement for the entire period. Larger firm focus on higher sales with fewer on cash basis


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which leads to greater cash flow problems and seasonality while smaller firms major focus is

stock management and credit management policies with low profitability. When a firms credit

sales increases their account receivables increases which leads to less inventory in the stock and

accounts payables also increases. These three components (accounts receivable, accounts

payables and inventory) of cash conversion cycle have different ways to be managed in order to

maximize firm’s profitability. In order to make CCC more effective, equilibrium should be

maintained in accounts receivables, accounts payables and inventory that have positive

significant impact on profitability of a firm.

WCM is important for the growth and stability of any country’s economy. Pakistan is rich in

having natural resources and human skills and different industries are playing their vital role in

the growth of Pakistan economy. Textile, Sugar, Chemical and Cement sectors are major

contributors of country’s economy. Pakistan textile industry is the largest industry of the country

and is ranked 8th largest exporter of textile products around the world. Investment in the sector is

US$ 7 Billion and its total exports are US$ 9.6 billion in year 2008-09. It contributes 46% to the

total manufacturing and 8.5 % of the country’s GDP. 38 % of the total labor force is employed in

this sector and its market capitalization is 5% of the total market capitalization (Pakistan

Economic Survey SBP, TDAP). Sugar Industry is ranked 2nd largest industry of Pakistan and is

5th largest sugarcane producing area in the world. It’s also the 15th biggest sugar producer of the

world. In 2009-10 Pakistan’s sugarcane production is approximately 47.8 MMT (million metric

tons), which is 2.2 MT (million tons) less from the previous year. In 2009-10 sugar imports are

forecasted at 1.03 MT and 50000 tons decrease is estimated in consumption. Chemical sector is

ranked in top 5 highly growing and globally traded sectors. Chemicals are divided into 2 main

categories commodity and specialty chemicals. According to IAR (Industrial Advisory Report


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2009) Imports of chemical increased but not significantly as compared to the growth of industry.

In 2007-08 Pakistan imported Rs. 329 billion worth of different chemicals. Demand of chemical

is increasing and the sector can grow in the long term but absence of clear policies and

frameworks are a big hurdle in it. Pakistan is enriched with the cement resources and is ranked as

the 5th biggest exporter in the world. Many plants are working in the private sector and there is a

prediction that India will import cement from Pakistan and till 2008 Pakistan has imported

130000 tons of cement (according to Growth of Pakistan Cement Industry – Overview Friday, 26

September 2008). Demand of cement has been increased significantly in the last decades.

Cement Industry has captured African countries as well for its exports. Industry growth is 32%

but exports increased by 111.86% (Pakistan Cement Industry Report 2009).

It is evident from above discussion that textile, sugar, chemical and cement sectors are the major

contributors of economic growth of Pakistan and WCM in these sectors plays a vital role in

managing the affairs of their respective businesses. However, it is important to investigate the

nature and processes of WCM strategies adopted by these industrial sectors of economy.

Therefore, the present study is intended to investigate the empirical results of 4 major industries

(Textile, sugar, chemical and cement) of Pakistan listed at KSE during period 2004 to 2009. This

study will help us to analyze effect of accounting ratios on WCM and on profitability

collectively. We will also derive a conclusion about the relation between WCM and profitability.

Rest of the article is arranged in the following order: Section 2 provides Literature Review,

Methodology is given in section 3, section 4 provides Results while Conclusion is given in the

last section.

2. Literature Review


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Various studies have been done in the past to check the relationship between profitability and

WCM. Mostly, researchers used the proxies for both WCM and profitability and it remained

difficult to state one exact relationship between them as different studies contain different

determinants of WCM and those variables have shown different relationship with the proxies of

profitability. Lazaridis & Tryfonidis (2006) worked on 131 listed companies of Athens stock

exchange for four years from 2001 to 2004. Aim behind the study was to determine statistically

significant relation between CCC and profitability which is measured by gross operating profit.

Accounts receivable turnover, accounts payable turnover and inventory management are the

three components of CCC. Pearson correlation and regression results showed that there is a

negative relationship between accounts receivable turnover, accounts payable turnover &

inventory management and profitability which is in line with the study of Deloof (2003).

Raheman & Nasr (2007) analyzed the effect of several variables on Net Operating Profitability

which includes average collection period, average payment period, ITO in days, CCC, ITO in

days and CR in Pakistan. Control variables including debt ratio, size of the firm and financial

asset over total asset ratio are used and they applied Pearson Correlation and Regression for

purpose of analysis. Sample of 94 Pakistani listed companies for 6 years from 1999-2004 had

been taken and concluded that managers can maximize shareholder value by efficiently

managing components of CCC. It shows that there exists a strong negative relation between

firm’s profitability and measures of WCM.

From emerging economy of Pakistan, Afza & Sajid (2008) investigated the relationship between

aggressive/conservative working capital policies and Firm’s return. They took a sample of 263

non financial companies from 17 industrial sectors after removing firms with negative equity

listed on KSE which constitute the whole population for analysis. They reported a negative


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relationship between measures of the profitability and degree of aggressiveness of financing and

investment policies.

According to Gill et al (2010) who extended Lazaridis & Tryfonidis (2006) study on relationship

between WCM and profitability, there exists a significant relationship between CCC and

profitability. They analyzed a sample of 88 firms listed on NYSE (New York Stock Exchange)

for three years from 2005 to 2007 using correlation and regression analysis to conclude that their

study was in line with the study of Lazaridis and Tryfonidis study and said that if a manager

efficiently manages accounts receivables, accounts payables and inventory he can increase the

profits of the firm.

H. Jamal Zubairi (2010) studied Pakistan automobile sector and checked the impact of WCM

and capital structure on profitability of the firm. To measure the profitability they used earnings

before interest and taxes. Panel data set was analyzed using regression. The results showed that

profitability variations due to the above mentioned four variables give three quarters of total

variation. They also reported positive relation between profitability and size of the firm which is

in accordance with the results of Raheman & Nasr (2007) study.

In Malaysia, Mohamad & Noriza (2010) did their study by taking secondary data from

Bloomberg’s 72 listed companies for 5 years from 2003-2007 to derive the relationship

empirically between WCM and profitability. Study was done to check effects of working capital

components (such as CCC, CATA ratio, debt to asset ratio, CR and current liabilities over total

asset ratio) on firm’s performance and profitability measured by Tobin’s Q ratio, return on

invested capital and ROA. Correlation and Multiple Regression results showed a significant

negative relation between working capital components and company’s performance.


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Haq et al (2011) conducted a study in Pakistan on WCM of cement industry by taking a sample

of 14 cement firms listed on KSE (Karachi Stock Exchange) from year 2004-2009 in panel data

set. They used eight accounting ratios (CR, QR, CATA ratio, CATS, cash turnover ratio, ITO

ratio, DTO, creditor turnover ratio) as independent variables and ROI as the dependent variable.

Estimated results based on Pearson correlation and Pooled Ordinary Least Square Regression

shows moderate relationship between WCM and profitability of firm.

Karaduman et al (2011) studied this relationship of WCM and profitability by taking data of five

years of non financial companies listed at Istanbul Stock Exchange. A balanced panel sample of

127 companies was analyzed which gives total of 635 observations. CCC was used as a measure

of WCM and for profitability ROA acted as a measure. The result showed that efficient

management of CCC will give greater returns.

From the previous studies it is evident that researchers used the accounting ratios as a proxy to

check the relationship between WCM and profitability. Most frequently ROA, ROE, ROIC and

Tobin’s Q are the proxies used for profitability and CCC, CATA, ITO, DTO and CR are the

variables used for WCM. The methodology adopted by the majority of researchers to examine

the relationship is correlation analysis, OLS regression and multiple regression analysis. The

results indicate that different contents of WCM show different relationship with profitability

proxies and it is difficult to conclude the exact relationship of WCM with the profitability.

3. Methodology

3.1 Sample and Data

The study is based on checking the relationship between WCM and profitability in the all listed

non financial sectors of Pakistan industry. For the analysis of the population the sample of 263


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companies from 4 major non financial sectors has been taken from the Balance Sheet Analysis of

Joint Stock companies listed on Karachi Stock Exchange from 2004-2009 published by State

Bank of Pakistan. Initially, the whole population census was taken for study and the numbers of

observations were 1578 but the descriptive analysis showed presence of various outliers in the

data which were removed by the procedure of 1% trimming. Finally, those firm year

observations have been deleted where value of any variable found missing which reduces the

sample size equal to 984 firm year observations.

3.2 VARIABLES OF STUDY & HYPOTHESIS:

The following ratios representing WCM have been analyzed in the study.

1- Current Asset over Total Assets Ratio

2- Current Assets over Total Sales Ratio

3- Inventory turnover

4- Debtors Turnover

5- Current Ratio

6- Quick Ratio

ROE and ROA have been used as a proxy of firm’s profitability. The table 1 given below

presents the definitions and expected signs of the variables.

The relation between the variables had been examined by making the use of Binary Logistic

Regression analysis on the unbalanced panel sample. The equation representing the relationship

is as follows:


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Where:

ROEit represents ROE for firm i at time t

ROAit represents ROA for firm i at time t

CATAit represents CATAR for firm i at time t

CATSit represents CATSR for firm i at time t

ITOit represents ITO for firm i at time t

DTOit represents DTO for firm i at time t

CRit represents CR for firm i at time t

QRit represents quick ratio QR for firm i at time t

uit represents error term of the model

The binary logistic regression has been used on the panel sample data. The advantage of using

the binary logistic regression is that t gives the results in binary form say YES or NO which

cannot be explained by ordinary least square regression. In our study it helps to determine that a

firm is in profit or loss in a particular year.

3.3 ESTIMATION TECHNIQUES:

Present study has used OLS as the primary estimation technique to investigate the impact of

WCM on firm’s performance. However, the chances of firm earning profit or suffer loss as a

result of its WCM policy is yet to be investigated in detail. Therefore, present study has also

incorporated binary logistic regression to determine the probability of firm being profitable or in

loss due to its WCM. Logistic regression is a method for determining the relationship between

predictor variables and a dichotomously coded dependent variable. It’s a linear model used for

binomial regression. Just like other forms of the regression analysis, it makes use of the predictor


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variables that can be either numerical or categorical. For estimation purposes LOGIT model can

be explained by the following equation:

The LOGIT specification of dependent variable is:

Where Y, representing dependent variable, is equal to 1 if the company is profitable and 0 if the

company is in loss. The probability that the company is profitable is given by:

If we assume that error follows a logistic distribution to be standard than it will be represented:

On the other hand if we say that the company will not be profitable or in loss than the equation

will be:

The odd ratio i.e. P(Y=1)/1-P(Y=1) which states the ratio of probability that company will be

profitable to the probability that company will not be profitable would be equal to:

Now if we take the log of the odd ratios it will be:


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Exponent raised to the power beta Exp (β) explains that if the value of Exp (β) is less than 1 than

increase in independent variable will correspond to the decrease in the odd ratio and if the value

is greater than 1 than increase in independent variable will result in an increase in the odd ratio.

4. Result and Analysis

4.1 Descriptive Analysis

Table 2 of descriptive analysis reports the mean, median and standard deviation of the all

dependant and independent variables.

4.2 Pearson correlation

Pearson correlation is used to check the linear association among variables of the study. We

found that ROE is having positive association with ROA, CATA, CR and QA at the significance

level of 1%. It also has positive association with ITO and DTO but that is not significant. ROE

have the negative association with CATS but association is not significant .In case of ROA there

is positive association with CATA, ITO, DTO, CR and QR at the significance level of 1% and

negative association with CATS at the significance level 5% shown at the end in table 3.

Collinearity statistics shows that there is no multicollinearity factor in the study.

4.3 Regression Analysis:

Regression Analysis is basically used to measure the relationship between dependent and

independent variables. In this study Regression analysis contains two dependent variables. In


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table 4 regression results are shown by taking ROE as dependant variable. In table 4 ROE is

replaced by ROA.

Table 4 shows that there is positive relationship with CATA, ITO, DTO and CR at the

significance level of 1% shows that increase in these variables will significantly increase ROE of

the firm. While there is negative relation with CATS at the significance level of 1%. This

relation shows that increase in CATS will significantly decrease ROE of the firm. The value of

R-square 0.122 shows that only 12.2% of the change in ROE is explained by the independent

variables. The F-statistic value is 27.08 and is significant at the level of 1%.The adjusted R-

square value is 11.7%.

Table 5 shows ROA has positive relationship with CATA, ITO, DTO and CR at the significance

level of 1%. While there is negative relation of ROA with CATS at the significance level of 1%.

The value of R-square .310 shows that only 31% of the change in ROA is explained by the

independent variables. The F-statistic value is 88.08 and is significant at the level of 1%.The

value of Durban Watson test is 1.45.

To check the relationship between WCM and profitability we have seen regression results for

each sector individually. The results show that CATA and CATS strongly determines ROE and

ROA for the textile sector. Similarly DTO is most significant in Chemical sector and ITO shows

most significant results with both ROE and ROA in cement sector analysis. An important finding

is that no variable shows significant results in sugar sector analysis. So, Regression was run on

the three sectors (textile, chemical and cement) excluding sugar sectors and that improves the

value of R-square.

4.4 Logistic Regression


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Logistic regression is used when the dependent variable is binary. In our study it has been used

to see that either the firm is profitable in a year or not. According to the results shown in table 6,

1 unit increase in CATA will cause an increase of 20.863 in the probability that the ROE will

increase. CATS value gives an indication that 1 unit increase in CATS will decrease the

probability of an increase in ROE by 0.362. The value for exp (β) of ITO shows that 1 unit

increase in ITO will increase the probability of increase in ROE by 1.001. DTO value is giving

an indication that an increase of 1 unit in DTO will decrease the chances of increase in ROE by

0.998 units. Similar to ITO, CR exp (β) shows that 1 unit increase in CR will cause an increase

in the probability of increase in ROE by 1.010 units. Result reveals CATA, CATS and CR are

the strong determinants of the WCM and highest value of CATA shows it the most significant

determinant of WCM.

Conclusion

Present study investigates the relationship between WCM and profitability of a firm of 263 non

financial stock listed companies at KSE (Karachi Stock Exchange). For analysis purpose

financial ratios of WCM are used to check their affect on the performance of the firm in the

context of 4 major non financial sectors of Pakistan. The result suggests that out of the five

selected components in regression analysis CATS ratio is only variable that shows significant

negative relationship with both ROE and ROA. CATA is having significant positive relationship

with ROA and ROE which is in line with the studies of Noriza & Azhar (2010). ITO shows

positive relationship with ROE but the results are not significant while shows significant positive

relationship with ROA. DTO is also having significant positive relationship and CR shows

significant positive relationship with both the proxies of profitability and these results are

parallel to the study of Haq et al (2011). We hope that our results would be a contribution in the


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study of WCM and profitability as logistic regression is first time used for analysis to the best of

our knowledge. It is recommended that future research should be done specifically on the sugar

sector to identify the strong determinants of profitability in this sector and to use other variables

of WCM that better predicts the relationship with profitability.

References

Afza, Talat & Mian Sajid Nazir, 2008. “Working Capital Approaches and Firm’s Returns in

Pakistan”, Pakistan Journal of Commerce and Social Sciences, Vol.1, pp.25-36.

Binti Mohammad nor Edi Azhar & Noriza Binti Mohd Saad (2010). “Working Capital

Management: The Effect of Market Valuation and Profitability in Malaysia”.

International Journal of Business and Management, Vol. 5, No. 11.

Eljelly, A. (2004). “Liquidity-profitability tradeoff: an empirical investigation in an emerging

market”. International Journal of Commerce and Management, Vol.14 (2), pp. 48- 61.

Gill Amarjit, Nahum Biger & Neil Mathur (2010). “The Relationship Between Working Capital

Management And Profitability: Evidence From The United States”. Business and

Economics Journal, Volume 2010: BEJ-10.


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Haq Ikram ul, Muhammad Sohail, Khalid Zaman &Zaheer Ala (2011). “The Relationship

between Working Capital Management and Profitability: A Case Study of Cement

Industry in Pakistan”. Mediterranean Journal of Social Sciences, Vol.2, No.2.

Karaduman Hasan Agan, Halil Emre Akbas, Arzu Ozsozgun Caliskan and Salih Durer (2011).

“The Relationship between Working Capital Management and Profitability: Evidence

from an Emerging Market”. International Research Journal of Finance and Economics,

ISSN 1450-2887, Issue 62.

Lazaridis I & Tryfonidis, D. (2006). “Relationship between working capital management and

profitability of listed companies in the Athens stock exchange”. Journal of Financial

Management and Analysis, Vol.19 (1), pp 26 – 35.

Narware P. C. (2004). “Working capital and profitability- an empirical analysis”. The

Management Accountant, Vol, 39 (6), pp 120-127.

Raheman, Abdul & Mohamed Nasr. (2007). “Working capital management and profitability-

case of Pakistani firms”. International Review of Business Research Paper, Vol 3, No1,

pp.279-300.

State Bank of Pakistan (2010). Balance Sheet Analysis Joint Stock Companies Listed on the

Karachi Stock Exchange, online available at:www.sbp.org.pk/departments/stats/bsa.pdf

Zubairi H. Jamal (2010). “Impact of Working Capital Management And Capital Structure on

Profitability of Automobile Firms in Pakistan”. Social Science Research Network. July

31, 2010Finance and Corporate Governance Conference 2011 Paper


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Tables:

Table 1: Definitions, symbols and expected signs of the proxy variables

Dependent Variables Definition Symbols Expected

Signs

Return on Equity Measures how much the shareholders

earned for their investment in the company.

ROE

Return on Assets Measures the ability of firm to utilize its

assets to create profits

ROA

Independent Variables

Current Asset over

Total Assets Ratio

Describes the proportion of current assets in

comparison with total assets

CATAR Negative

Current Assets over Describes the ratio of current assets to the CATSR Negative


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Total Sales Ratio total revenues of firm

Inventory Turnover

Ratio

Indicates the liquidity of the inventory ITO Positive

Debtors Turnover

Ratio

Indicates the liquidity of the receivables DTO Positive

Current Ratio Determines the short term debt paying

ability of the firm

CR Negative

Quick Ratio Determines the short term debt paying

ability of the firm excluding inventories

QR Negative

Table 2: Descriptive

No MEAN MEDIAN S.D

ROE 984 3.25 5.40 31.21

ROA 984 2.88 1.70 9.21

CATA 984 .46 0.46 .17

CATS 984 .56 0.46 .38

ITO 984 7.80 4.63 11.20

DTO 984 26.20 13.16 21.80

CR 984 108.05 92.50 70.92

QR 984 64.54 46.15 58.79


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Table 3: Pearson correlation

ROE

ROA

CATA

CATS

ITO

DT

CR

QR

ROE

1

ROA

.741**

1

CATA

.247** .302** 1

CATS

-.017 -.080* .343** 1

ITO

.057 .107** -.244** -.202** 1

DTO

.055 .097** -.263** -.144** .191** 1

CR

.271** .463** .531** .322** -.055 -.023 1

QR .256** .431** .431** .320** .140** .029 .925** 1


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** Significant at level 1%; *Significant at level 5%

Table 4: OLS Regression Table for ROE

Model

Unstandardized Coefficients

T

B Std. Error Sig.

CATA 42.392 7.074 5.993 .000

CATS -10.687 2.694 -3.967 .000

ITO .227 .088 2.579 .010

DTO .084 .031 2.701 .007

CR .087 .016 5.388 .000

(Constant) -23.699 3.412 -6.945 .000

Adjusted R square .117 Sig. F .000

Std. Error 29.32 Durbin Watson 1.45

Table 5: OLS Regression for ROA

Model

Unstandardised Coefficients

T

B Std. Error Sig.

CATA 11.405 1.849 6.169 .000


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CATS -6.210 .704 -8.819 .000

ITO .091 .023 3.964 .010

DTO .030 .008 3.694 .007

CR .057 .004 13.669 .000

(Constant) -6.624 .892 -7.427 .000

R-Square .310 F 88.07

Adjusted R square .307 Sig. F .000

Std. Error 7.66 Durbin Watson 1.16

Table 6: Logistic Regression

Model

Sig.

B Df Exp(B) Wald S.E

CATA 3.038 1 .000 20.86 21.35 .657

CATS -1.017 1 .000 .362 16.23 .235

ITO .001 1 .859 1.00 .032 .007

DTO -.002 1 .318 .998 .997 .002

CR .010 1 .000 1.01 19.16 .002

(Constant) -.774 1 .007 .461 7.36 .285


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Cox & Snell R-Square

Nagelkerke R-Square

.114

.163

-2 log likelihood 1011.08

Chi square Df Sig

Step 114.21 5 .000

Block 114.21 5 .000

Model 114.21 5 .000

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Impact of Working Capital Management on Performance of Listed Non