efr-21069-september-2014-3(11)-a

Economics and Finance Review Vol. 3(11) pp. 01 – 14, September, 2014 ISSN: 2047 - 0401

Available online at http://www.businessjournalz.org/efr

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Effect of Working Capital Management on performance of Firms Listed at the Nairobi

Securities Exchange

Cyprian Nyarige Nyamweno

Department of Economics, Finance and Accounts,

School of Business,

Jomo Kenyatta University of Agriculture and Technology,

Nairobi Kenya

E-mail: [email protected]

Tobias Olweny

Lecturer, School of Business,

Jomo Kenyatta University of Agriculture and Technology

E-mail: [email protected]

ABSTRACT

This study sought to determine the effect of working capital management on performance of firms listed at the

Nairobi Securities Exchange in Kenya. A sample of 27 listed firms was used for the period 2003 to 2012. The

study employed a Robust GMM applied to Arellano-Bover/Blundell-Bond linear dynamic panel-data estimation

analysis. The results revealed that days of accounts receivables and cash conversion cycle have an indirect

effect on performance measured by gross operating profit. Days of accounts payables and days in inventory

have a significant and direct effect on performance. Inflation and size were found to have indirect and direct

effect on performance respectively. Although not significant, they cannot be ignored by finance managers who

wish to boost performance. ANOVA results confirm that various sectors have varying and somewhat same

averages of working capital. Therefore industry averages should not ignored when setting working capital

management policies in Kenya.

Keywords: Working Capital Management (WCM), Gross Operating Profit (GOP), days in accounts receivables

(AR), days in accounts payables (AP), days in inventory (INV), Nairobi Securities Exchange (NSE),

1. INTRODUCTION

Working capital performance provides critical insight into the state of a company’s financial position. As an

important indicator of financial fitness, the availability of a company’s working capital is one of the first items a

lender or investor will examine on a balance sheet (Financial Executives International Canada, 2013).

Globally 1000 companies lose about $2 billion per year due to poor working capital management. The recent

financial and economic crisis has shown how important it is for firms to maintain a healthy cash position. The

risk of becoming illiquid always increases in times of credit constraints and economic downturn. However,

companies are still unable to properly assess their cash needs (Frankfurt Business Media, 2012).

Long term financing and short term are two ways of financing a business enterprise. Long term financing is

requirement means a firm needs for capital expenditure while short term financing is requirement for certain

expenditure like procurement of raw materials, payment of wages, day-to-day expenditures. Working capital

management is the short-term finance of the business which is a closely related to trade between profitability

and liquidity. Efficient working capital management seeks to improve the operating performance of a business

concern and it helps to meet the short term liquidity. Hence, the study of working capital management is not

only an important part of financial management but also an overall management of a business concern

(Paramasivan C; Subramanian T, 2009).

The stock market and financial sector also plays an important role in the development of any economy. The

depth of the financial sector has generally been found to promote economic growth. It is confirmed that a well-

functioning capital market increase economic efficiency, investment and growth (Ngugi, Amanja, & Maana,

2010). This means that the performance of the listed firms in any economy is vital to the performance of the

stock market and of that economy at large.

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Inflation on the other hand affects working capital management and policy. It plays out on both the balance

sheet and the income statement of all businesses and households. Anticipating the future effects of inflation can

work to the advantage of the savvy financial manager. The fundamental principle to be followed in inflationary

times is that cash is guaranteed to lose value over time while the physical assets will gain in value. Incorporating

this principle into all financial transactions becomes critical for success (Carl S; Dan L; Ellisabeth D, 2011).

Shareholders, economies and lenders have invested heavily in the listed firms financially and providing a

healthy environment for conducting business. Therefore these stakeholders expect such companies to perform to

the expected standards. Some companies have so far performed well while others have suffered declined

performance. Some companies have been delisted from the NSE due to financial reasons (Chebii, Kipchumba,

& Wasike, 2011).

Working capital presents a huge opportunity for companies to release cash from their balance sheets and operate

more effectively. Actually well-managed cash provides firms with growth without the need for additional

funding (Frankfurt Business Media, 2012).

From companies’ annual reports from Nairobi Securities Exchange (2013), it is evident that many companies

quoted at NSE do not pay dividends consistently, and when they pay, the level of payout is very low contrary to

shareholders’ expectations. Further with corporate failures witnessed in Kenya like Uchumi Supermarkets and

Kenya Cooperative Creameries, with some undergoing through receivership Maina & Sakwa (2010), there was

need and motivation to undertake this study.

1.2 Research Objectives

1.2.1 General Objective

The main objective of this is study was to determine the effect of working capital management on performance

of firms listed at the Nairobi Securities Exchange.

1.2.2 Specific objectives

1. To determine the effect of cash conversion cycle on profitability of firms listed at the NSE.

2. To determine the effect of days of accounts payables on profitability of firms listed at the NSE.

3. To determine the effect of days of accounts receivables on profitability of firms listed at the NSE.

4. To determine the effect of days in inventories on profitability of firms listed at the NSE.

1.3 Significance of the study

This study is useful in the following ways; first, it identifies how the components of working capital affect

performance of listed firms. Thus, a firm management can strike a balance between those components to

maximize the shareholders’ wealth as well meet the shareholders’ expectations. Its findings will also validate

some of the outcomes of previous studies.

1.4 Scope of the study

The study considers the listed non-financial firms at the NSE. Such firms define their working capital differently

as compared to financial, insurance and investment firms. In its nature, Nairobi Securities Exchange is an

international market open to foreign investors and hence the firms listed have an international image. Any

dismal performance will make investors lose interest in investing in them and the market. This study utilized the

working capital components as reported in the financial statements and annual reports of the listed firms. The

findings of this study are generalized for all companies making them very useful not only to the firms listed but

also for the firms in Kenya, the stock market and economy at large.

1.5 Limitations of the study

While care is taken to collect the accurate data, there is chance that the financial statements may lack accurate

information as they are subject to manipulation by firms so as to meet industry specific requirements. There is

risk that the financial information displayed might have been modified to omit or include some information as

part of working capital to meet company requirements or other regulatory requirements for listed companies.

This may pose a challenge for generalization especially for the private and family owned companies.

1.6 Delimitations of the study

Financial Institutions have been excluded from the study sample because their working capital includes deposits

and loans which make part of their main business as well as the financial regulatory requirements as opposed to

other firms. Therefore working capital may affect their performance differently from non-financial firms.




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The exclusion of financial firms may cast doubt on the generalizations of the results of the study. The firms

listed have well developed financial structures with the oversight of the Capital Market Authority hence they

have the ability to comply with stringent financial regulatory requirements. The analysis of such firms therefore

provides an important insight into the management of working capital policies and their effects in performance

of all firms in Kenya.

2. LITERATURE REVIEW

The main theme of the theory of working capital management is the interaction between current assets and

current liabilities and it involves managing the balance between a firm’s short-term assets and short-term

liabilities with an aim of ensuring to continuity of operations (Pandey, 2011).

2.1 Trade Credit theories

Trade credit can either be given by a supplier in the form of accounts receivables, or can be received by a

customer in the form of accounts payables. The authors of this body of literature have studied why firms decide

to receive or to grant trade credit based on the advantages of either the supplier or customer, from the

operational, commercial and financial perspective;

2.1.1 Financing advantage theory

According to Joana, Vitorino, and Moreira(2011), a supplier may have an advantage over traditional lenders in

investigating the credit worthiness of his clients, as well as a better ability to monitor and force repayment of the

credit. This may give a number of cost advantages over financial institutions in offering credit to a buyer as

elaborated below: (a) information acquisition - a supplier may visit the buyer’s premises more often than

financial institutions would. The size and timing of the buyer’s orders also give an idea of the condition of the

buyer’s business. The buyer’s inability to take advantage of early payment discounts may serve as a tripwire to

alert the supplier of deterioration in the buyer’s creditworthiness (Biais, Bruno, Gollier, & Viala, 1993).While

financial institutions may also collect similar information, the supplier may be able to get it faster and at lower

cost because it is obtained in the normal course of business, (b) controlling the buyer - the supplier can threaten

to cut off future supplies in the event default or delayed repayment. This threat may especially be credible if the

buyer accounts for a small portion of the supplier’s sales. By contrast, a financial institution may have more

limited powers; the threat to withdraw future finance may have little immediate effect on the borrower’s

operations (Garcia-Teruel & Martinez-Solano, 2007), and (c) salvaging value from existing assets - if the buyer

defaults, the supplier can seize the goods supplied. The more durable the goods supplied the better collateral

they provide and the greater the credit the supplier can provide. Financial institutions can also reclaim the firm’s

assets to pay off the firm’s loan but if the supplier already has a network for selling its goods, its costs of

repossessing and resale will be lower than that of an institution (Mian & Smith, 1992).

2.1.2 Price discrimination theory

Market power of firms can be enhanced considerably by practicing price discrimination through trade credit as

buyers are heterogonous. Mostly, firms enjoying high price-cost margin are found to resort to price

discrimination (The National Bureau of Economic Research, 1996).

Trade credit follows industry practice hence the application of this strategy is limited and can be used selectively.

Customers who have low default risk and can obtain institutional finance at better terms may not be willing to

accept trade credit because its implicit cost is higher than that of institutional finance. This makes the offer only

attractive to high-risk marginal customers whose access to institutional finance is prohibitively costly raising the

incidence of bad debts (Bhattacharya, 2009).

2.1.3 Transactions costs theory

Trade credit may reduce the transactions costs of paying bills. Rather than paying bills every time goods are

delivered, a buyer might want to cumulate obligations and pay them only monthly or quarterly. This will enables

an organization to separate the payment cycle from the delivery schedule (Ferris, 1981).

2.2 Risk-return trade-off theory

The management of working capital involves risk and return trade-off. It is not possible to accurately estimate

the working capital needs and so a firm must decide about levels of current production to be carried out. Given a

firm’s technology and production policy, sales and demand conditions and operating efficiency, its current

assets holdings will depend upon its working capital policy which may follow conservative or aggressive policy

and these policies involve risk and return trade-offs (Pandey, 2011).




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2.3 The Cost trade-off theory

Cost of liquidity and illiquidity are involved in maintaining a particular level of current assets. Very high level

of current assets means excessive liquidity hence return on assets will be low as funds are tied up in idle cash

and stocks earn nothing while high levels of debtors reduce profitability. Therefore cost of liquidity through low

rates of return increases with the level of current assets. Conversely, cost of illiquidity means holding

insufficient current assets whereby a firm will be unable to honor its obligations forcing it to borrow on short-

term at high interest rates. This adversely affects a firm’s creditworthiness and may limit future access to funds

and possible insolvency. A firm should balance the cost of liquidity and cost of illiquidity at equilibrium as

shown in figure 1(Pandey, 2011).

Figure 1: Cost Trade-off

Source:(Pandey, 2011)

2.4 Empirical Review

Firms’ management can create value by reducing the number of days accounts receivables (AR), increasing

their inventories (INV) to a reasonable level, take long to pay their creditors in as far as they do not strain their

relationships with these creditors and careful reduction of the cash conversion cycle to its minimum. In a study

by Mogaka & Jagongo (2013) on the effects of working capital management on firm’s profitability (measured

by ROA) using panel data, they found out a significant negative relationship between profitability and number

of AR and CCC, but an insignificant positive relationship with INV and AP(significant). Financial leverage,

sales growth, current ratio and firm size were used as control variables and were found to have significant effect

on the firm’s profitability.

A study by Kulkanya (2012) on the relationship between working capital and profitability (measured by

GOP)using a panel regression revealed a significant negative relationship between profits and inventory

conversion period, receivables conversion period and cash conversion cycle. Therefore managers can improve

profitability by reducing the cash conversion cycle, inventory conversion period, and receivables conversion

period. Accounts payables have an insignificant negative relationship with profitability and managers cannot

increase profitability by lengthening the payables deferral period. However he noted that industry characteristics

have an impact on the GOP.

A study on working capital management in Turkish clothing Industry by Karabay and Gülseren(2013),

concluded that clothing companies should reduce debt collection period, cash conversion cycle and establish a

balance between liquidity and profitability in order to survive and to increase their profits. Size was found to

have a significant positive relationship with days of accounts payables and current ratio. Big size companies do

not have liquidity problems as they have capacity to increase their liquidity without borrowing.

Muchina and Kiano (2011), while studying the influence of working capital management on firms’ profitability,

a case study of SMEs in Kenya, found out that average debtors days, stock turnover period and cash conversion

cycle significantly affect firm profitability. Financial leverage, ratio of current ratio and firm size had a

significant impact on the profitability. This study however did not find out the direction of the relationship

between cash conversion cycle and profitability.

Anandasayanan (2014), who studied the effects of working capital management on profitability of Sri Lankan

listed firms for the period 2003-2009, found a significant negative relationship between net operating

profitability and the average collection period, inventory turnover in days and average cash conversion cycle.

This was interpreted to mean that managers can create value by reducing the number of days of accounts

receivable and inventories to a reasonable minimum. The control variable natural logarithm of sales was




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significantly positively related with profitability. Debt ratio was insignificantly positively related to profitability

meaning that managers do not have to bother very much on the debt ratio when determining the strategy to

increase their profits.

A study by Vural, Sokmen, and Çetenak (2012) on the effects of working capital management (measured by

GOP) on firm’s performance: evidence from 75 manufacturing firms listed on Istanbul Stock Exchange using

dynamic panel data analysis, revealed that firms can increase profitability by shortening accounts receivables

collection period and cash conversion cycle. The control variable leverage revealed a significant negative

relationship with firm value measured by Tobin Q and profitability.

A consistent result was further obtained from a study by J. Garcia (2011) on the ‘impact of working capital

management upon companies’ profitability: evidence from European Companies’ that used cash conversion

cycle to represent working capital and GOP to measure of profitability, revealed a significant negative

relationship between RCP, ICP, Payables Deferral Period, CCC, and profitability. These findings suggest that

firms can improve their profitability by reducing the time span during which working capital is tied within the

company.

2.5 Conceptual Framework

The conceptual framework shows the effect of working capital on firm performance.

Figure 2: Schematic Conceptual Framework

Source: Author (2014)

To remain consistent with previous studies, measures pertaining to working capital management and

profitability were taken from Vural, Sokmen, and Çetenak (2012). Variables have been measured as shown in

Table 1: Variable Abbreviation and Measurement

Abbreviation Variable in full Measurement

GOP Gross Operating Profit sales-cost of goods sold / total assets-financial assets

GOPt-1 Previous year Gross Operating

Profit

salest-1-cost of goods soldt-1/ total assetst-1-financial

assetst-1

AR Days in accounts receivables (average of accounts receivable / sales* 365)

AP Days in accounts payables (average of accounts payable/cost of goods sold *365)

INV Days in inventory (Inventory / cost of goods sold)*365

CCC Cash Conversion Cycle AR+ INV- AP

LNSALES Size natural logarithm of total sales

INFL Inflation Annual inflation rates

ε error term of the model

α intercept

2.6 Research Gaps

The study used a Robust Generalized Method of Moment (GMM) System Estimation applied to Arellano-

Bover/Blundell-Bond linear dynamic panel-data which is a system estimator that uses additional moment

conditions based on the work of (Manuel Arellano and Stephen Bond, 1991). Studies so far undertaken in Kenya

in this area on thought, have not employed this method of analysis, a gap that this study bridges therefore adding

to the existing literature. Use of lags of the dependent variable is crucial to control for dynamics of the process

Cash conversion cycle

Days in Inventory

Days of accounts payables

Days of accounts receivables

Inflation

Size

Performance (GOP)




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therefore enabling a researcher to discover new or different relationships between dependent and independent

variables especially over a long period Brañas-Garza, Bucheli, and García-Muñoz (2011), like in this study 10

years. The firms listed at the securities exchange are assumed to be financially prudent and healthy which

potential attracts investors. If such companies do not maximize the shareholder returns, no matter the financial

and operating environment, the public including international investors will lose interest in investing in NSE and

which will negatively affect the economy.

3. RESEARCH METHODOLOGY

To remain consistent with other studies (Vural, Sokmen, & Çetenak, 2012), the study used a system generalized

method of moments employed to dynamic panel data for the analysis. A purposive sample of 27 firms was

studied from a population of 57 listed firms, Nairobi Securities Exchange (2013) andexposed to 270 total

observations for the year 2003 to 2012.The following sectors considered (Nairobi Securities Exchange, 2013);

Agriculture (6), Automobile and Accessories (4), Construction and allied (5), Commercial and Services (4),

Energy and Petroleum (2), Manufacturing and Allied (6).

3.1 Data Analysis and Presentation

Data of accounts payables, accounts receivables, inventories, sales turnover, total assets, and cost of goods sold

was extracted from individual company published annual reports and financial statements was collected. The

country annual inflation rates were obtained from published reports by Kenya National Bureau of Statistics for

the study period.

The study used a Robust GMM applied to dynamic panel data by employing Roodman, D (2006) xtabond2

command which is not an inbuilt Stata to analyze data. This estimation technique was used because, among

difference GMM, ordinary least squares and within group estimators, GMM estimators exhibit the smallest bias

and variance for dynamic panel data (Brañas-Garza, Bucheli, and García-Muñoz (2011).Once the system

estimators were obtained, the validity of the model was checked. Manuel Arellano and Stephen Bond (1991),

propose a test to detect serial correlation in the disturbances in the first-differenced errors for second order

autocorrelation (AR (2)). To find out the difference between the effects of the working capital management on

performance of firms in different sectors, a one-way ANOVA was carried for each variable.

The following regression equations were estimated;

Model I: GOP = β0+ β1GOPit-1+ β2 ARit + β3LNSALESit + β4 INFLit + εit

Model II: GOP = β0 + β1GOPit-1+ β2INVit + β3LNSALESit + β4INFLit + εit

Model III: GOP = β0 + β1GOPit-1+ β2APit + β3LNSALESit + β4INFLit + εit

Model IV: GOP = β0 + β1GOPit-1+ β2CCCit + β3LNSALESit+ β4INFLit + εit

Model V: GOP = β0 + β1GOPit-1+ β2ARit+ β2APit + β2INVit+ β2CCCit+β3 LNSALESit+ β4INFLit + εit

Equation 5 was estimated as a control model to establish the relationship and significance of the individual

working capital components to the overall model as done by (Mathuva, 2010).

4. RESEARCH RESULTS AND DISCUSSION

4.1 Descriptive Statistics

Table2: Descriptive Data Analysis

Variables Obs Mean Std. Dev. Min Max

GOP 270 0.603778 0.901789 0 11.72

AR 270 73.83917 45.68519 9.596 336.681

AP 270 123.9285 98.27132 15.45 495.364

INV 270 102.7382 71.05691 0 355.252

CCC 270 52.89482 95.80095 -271.28 311.488

LNSALES 270 8.057499 1.525012 3.61092 11.8095

INFL 270 9.488737 3.570342 4.08 15.1

Source: Survey of 2003-2012, Stata output




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Table 2 shows that the credit period granted by firms to their clients ranged averagely at 73.84 days while

creditors were paid after 123.93 days. Inventory took an average of 103.02 days to be sold while the average

CCC ranged at 52.65 days. Therefore NSE listed firms issue a shorter credit period while they hold cash due to

creditors longer as a delay tactic to reinvest. The firms exhibit a conservative working capital policy to cushion

them from unexpected fluctuations in the market. These findings somewhat match those obtained by; Mogaka

and Jagongo (2013) with ACP of 56.54 days, ICP of 93.85 days, APP of 96.50 days and CCC of 53.88 days and

Safda and Chaudhry(2012) who used ROA to proxy profitability found out average accounts receivable of 89.36

days, days in inventory of 94.65 days, days in accounts payable of 154.79 days, with CCC of 30.35 days.

Also a study in Istanbul in Turkey by Vural, Sokmen, and Çetenak (2012) found days of accounts receivables

was 94.3 days, days in inventory was 94.56 days, but days of accounts payables of 64.48 days and cash

collection cycle of 128 were different. This means that the firms paid their debts as soon as possible faster that

they could receive the debt owed to them by their customers. This may be attributed to the fact that firms’ main

intention was to grow sales by issuing longer credit period while the market may suppliers may be sensitive to

debt owed to them by such firms a fact that firms may not want to spoil their credibility. The difference in these

results to the ones of this study can be attributed to the nature of the market and economy in which they have

been carried out. In advanced economies, the suppliers may be more sensitive than those in developing

economies hence the reaction by firms.

4.2 Correlation Results

Table 3: Pearson Correlation matrix between variables

Variables GOP AR AP INV CCC LNSALES INFL

GOP 1.000

AR -0.159*

1.000

AP 0.309*

0.059 1.000

INV 0.079 -0.055 0.486*

1.000

CCC -0.334*

0.383*

-0.639*

0.213*

1.000

LNSALES 0.218*

-0.503*

0.134*

0.199*

-0.236*

1.000

INFL 0.014 0.056 0.032 0.055 0.0340 -0.011 1.000

*Significant at 0.05 and 0.01significance level. Source: Stata output

The results as shown in Table 3 reveal that satisfactory performance of managers would increase profitability

by reducing CCC. AR and CCC are negatively correlated with GOP indicating that if the both duration of both

increase, it will have a negative impact on the profitability. AP and INV are both positively related with GOP

meaning that an increase in AP and INV leads to increase in GOP. Also the results reveal that LNSALES and

INFL are directly correlated with GOP indicating that profitability increase with increase in both size of the

firms and inflation. Consistent results have been obtained by Joana, Vitorino, & Moreira (2011), Vural, Sokmen,

& Çetenak (2012), Baveld (2012) and Nzioki, Kimeli, Abudho, and Nthiwa (2013). Mogaka & Jagongo (2013)

further found out that ACP and CCC are indirectly related to ROA and the trio of ICP, LNSALES and APP

being directly related. The direct relation between profitability and APP means that lagging payments to

suppliers ensures that firms have enough to purchase more inventories for resale thus increasing its sales levels

and boosting their profits. Firm size was also found to be positively related to ROA meaning that larger firms

report higher profits compared to smaller firms. Similar result for inflation was obtained by Awan (2014) where

the study obtained a direct but insignificant relationship with ROE but a significant one with ROA.

However, shortcoming of Pearson correlations is that they are not able to identify the causes from consequences

hence the regression analyses will be held in this study Baveld (2012).

4.3 Regression results

The validity of instruments was done using Sargan test which is based on the observation that residuals should

be uncorrelated with instruments as a null hypothesis. The results in Table 4, Table 5, Table 6, Table 7, and

Table 8, reveal that the null hypothesis that the over-identifying restrictions are valid cannot be rejected

(p>0.05). To test serial correlation of order 1 in levels, the study checked for correlation of order 2 in differences.

The instruments are uncorrelated with the errors or that they are not omitted variables in the model (p>0.05).

The wald test for joint significance of coefficients revealed that the coefficients in the model are statistically

jointly significant in determining the variations in GOP despite some of them individually not significant

(p=0.0000, wald chi2 (4) = 28.03 in Table 4, (p=0.0000) and wald chi2(4) = 21.78 in Table 5, (p=0.0000) and




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wald chi2(4) = 24.10 in Table 6, (p=0.0000) and Wald chi2(4) = 37.10 in Table 7 and (p=0.000) and Wald

chi2(4) = 368.53 in Table 8 respectively).

4.3.1 Effect of accounts receivables on firm profitability

The results on Table 4 reveal that AR is not an important determinant of GOP (p=0.618>0.05), but has a

negative effect on GOP meaning an increase in the AR leads to a decline in GOP. A one day increase in AR is

associated with a 0.0913281% decrease in profitability. This negative relationship is consistent with the cost

trade-off theory. Similar findings were reported by Baveld (2012), Joana, Vitorino, & Moreira (2011), Mogaka

& Jagongo (2013), and Nzioki, Kimeli, Abudho, & Nthiwa (2013). A study by Safda & Chaudhry(2012)

reported an indirect but significant relationship of profitability. This therefore means that a more restrictive

credit policy will improve performance of a firm. The results further reveal the control variable LNSALES has a

direct relationship with a firm’s performance meaning that a 1% increase in sales leads to 7.941621% increase

in GOP. This is true because firms with more sales turnover are more likely to get more credit from banks for

expansion and increase in profits because of the effects of sales revenue on profits. Such firms also enjoy

economies of scale which adds to their profits. Also there is a positive effect of inflation on performance, a

result that also obtained by Rasheed(2014).Last year’s GOP was found to directly determine this year’s GOP

which mean that a 1% increase in last year’s GOP leads to a 15.582% increase in this year’s GOP. Although not

significant, it has a high effect on GOP as compared to other variables. The intercept is negative at -0.0919019

meaning that when, last year’s GOP, AR, LNSALES and inflation are held at zero, firms will make losses at

9.190%. This implies that working capital has a big effect firms’ profitability.

Model I: GOP=-0.09190+0.15582GOPt-1- 0.000913ARit+0.079416LNSALESit+0.0002122INFLit+ε it

4.3.2 Effect of accounts payables on firm profitability

AP is an important determinant GOP (p=0.068<0.10)at 10% confidence interval as revealed in Table 5with a

direct effect on GOP meaning that a one days increase in the days of accounts payables is associated with

increase in GOP by 0.1672%. This is consistent with findings by (Mathuva, 2010). Last year’s GOP is found to

directly determine this year’s GOP as revealed by the positive coefficient (0.1124015) which is interpreted to

mean that a 1% increase in last year’s GOP leads to an 11.24015% increase in this year’s GOP. Despite the fact

that the effect is not significant, it has a high effect on GOP as compared to other variables. LNSALES has a

direct but insignificant effect on GOP with a coefficient of 0.0874989 meaning that a 1% increase in sales leads

to an8.7499% increase in GOP. Further it means that under high sales level, a firm will make more profits by

delaying in paying off their creditors. Firms will utilize the cash due to the creditors so to increase their

production thereby influencing their profitability. Inflation (-0.002491) is indirectly related to the GOP meaning

that a 1% increase in inflation, leads to a 0.249% decrease in profitability. During high inflation, firms will

delay their payments to creditors which they reinvest to increase their profitability. This is because firms find it

hard to access credit from the banks as interest for such loans will increase in tandem with inflation. Therefore

the control variables affect the choice of working capital policy by firms listed at the NSE.

Model II: GOP=-0.3780+0.1124GOPt-1+0.001672APit+0.087499LNSALESit- 0.002491NFLit+ε it

4.3.3 Effect of days in inventory on firm profitability

The results in Table 6reveal that, the days in inventory is found to have a direct and significant (0.0044289)

effect on GOP (p= 0.019<0.05). This is means that one day stay in inventory leads to a 0.4429% increase in

GOP. Therefore an adequate and timely flow of inventory is imperative for the success and growth of any

company.

This is consistent with a conservative working capital management policy. This means maintaining high levels

of inventory will in turn reduce the cost of possible interruptions in the production process and possible loss of

business due to shortages. This result is consistent with that of Mathuva (2010). However, this result is

somewhat different from that of Baveld (2012), Stephanou (2010), Kulkanya (2012), Panigrahi(2013), and

Garcia-Teruel & Martinez-Solano, (2007), who found an indirect relationship of days in inventory and

profitability. The differences may be attributed to the regression analysis methodology that was employed by

this study. Last year’s GOP is found to directly determine this year’s GOP as revealed by the positive coefficient

(0.1714255) which is interpreted as; a 1% increase in last year’s GOP leads to a 17.14255% increase in this

year’s GOP. Despite the fact that the effect is not significant, it has a high effect on GOP as compared to other

variables. LNSALES (0.0573022) and inflation (-0.0050861) are both not significantly affecting GOP.

LNSALES is interpreted to mean that a 1% increase in sales leads to a 5.73% increase in GOP. This is also from

the theoretical relationship between sales revenue and GOP. Also a 1% increase in inflation leads to a 0.05086%




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decrease in GOP. This happens because during inflationary times disposable income of consumers is eroded by

inflation hence they cannot afford to buy more thereby reducing firms’ sales revenue.

Model III: GOP=-0.39374+0.17143GOPt-1+0.004429NVit+0.05730LNSALESit-0.005086INFLit+ε it

4.3.4 Effect of cash conversion cycle on firm profitability

Table 7 shows that CCC has an indirect but not a significant (p=0.135>0.10) effect on GOP. That is interpreted

to mean that a one day increase in cash conversion period, leads to decreases in profitability of 0.1772% hence

for managers to increase profitability, they should reduce the CCC. This is also consistent with cost trade-off

theory and the findings of Mathuva(2010), Baveld(2012), Stephanou(2010), and Amarjit, Nahum, and

Neil(2010). Last year’s GOP is found to directly determine this year’s GOP as revealed by the positive

coefficient (0.1674274) which is interpreted to mean that a 1% increase in last year’s GOP leads to a 16.74274%

increase in this year’s GOP. Despite the fact that the effect is not significant, it has a high effect on GOP as

compared to other variables. LNSALES (0.067031) and inflation (0.00063) are both have direct but not

significant effect on gross operating profit. The results further mean that at high inflationary times, firms will try

as much as possible to shorten the CCC to protect the sales gains from being eroded by inflation and take

advantage of delaying the cash to creditors and making the most out of it. The findings of LNSALES were

consistent with those of consistent with Mathuva(2010).

Model IV: GOP=2.552+0.1674GOPt-1-0.1772CCC it+6.703LNSALES it+0.0630INFLit+ε it

4.3.5 Effect of working capital management on firm profitability

The model V as reported in Table 8 serve a control model for the variables under study to provide an indicator

as to the most significant variable affecting the study. All the variables are significant (p<0.05) with the

exception of control variables LNSALES, INFL and lagged form of the GOP. The results confirm that AR (-

0.1263628) and INV (-0.1228277) have an indirect effect on GOP but CCC has a wrong sign (0.1235656) that is

positively related to GOP with the AP also directly related to GOP (0.125545). LNSALES (0.0834587) has a

direct effect on GOP while inflation (-0.000577) has a negative effect on GOP. Last year’s GOP is found to

directly (0.1531341) determine this year’s GOP.

Model V: GOP=-0.32832+0.1531GOPt-1-0.126363ARit+0.125545APit-

0.1235656CCCit+0.08345868LNSALESit-0.000577INFLit+ε it

4.4 Analysis of Variance (ANOVA)

The study carried a one-way ANOVA on all the four components of working capital management namely AR,

AP, INV and CCC to find if their means for the selected sectors are significantly different.

For all the variables under consideration, the F-values were significant (p<0.10) as shown by results in Table 9,

Table 11, Table 13 and Table 15, meaning that least one of the means differ from zero in all the six sectors, but

it does not tell us where the differences or similarities are in the averages of those variables. This necessitated

application of a bonferroni correction on each of the variables as reported in Table 10, Table 12, Table 14 and

Table 16. The results that show significant p values (P=0.0000<0.10) and are said to have different averages of

AR. While those that reveal are insignificant (P>0.10) have same averages of AR meaning that the sectors have

somewhat same policy towards treatment of days of accounts receivable. It indicates that the application of

working capital policies on the four components (AR, AP, INV and CCC) do not differ significantly across

those sectors. While those with unequal means imply that approaches used by firms in such sectors do

significantly vary.

5. SUMMARY OF THE FINDINGS, CONCLUSION AND RECOMMENDATIONS

The results show that the AR and CCC have indirect effect on firm’s profitability but which is not significant

hence by shortening collection period and CCC firms may increase their profitability. Also AP and INV have a

significant and direct effect on firms’ GOP. Control variables LNSALES and INFL were found to have different

effects on the choice firms’ working capital policy. In overall measure, the control variables affect how firms

choose their working capital policies. Working capital management is viewed as an effective lever to increase

cash flow and preserve, or even to enhance company value. But more importantly, for many companies in

Kenya today, it may be the necessary key to survival.

Based on the key findings, the following conclusions have been reached: The management of a firm can create

more value for their shareholders by reducing the CCC and AR. AP and INV were found to have a significant

direct effect on firm performance. Firms’ management can therefore create value for their shareholders by




10

lagging payments to their creditors taking care not to ruin their credibility which may prevent future credit

facilities and possible litigation from creditors. By reducing the days in accounts receivable, firms will have

ready cash to reinvest and can prevent the cash from getting eroded by effects of inflation as well as benefit

from cheap source of financing owing to the fact that the cost of borrowing in Kenya is on the upward trend.

Also firms can create value by increasing their inventory levels but only to an optimum level that can maximize

returns and minimize the costs of keeping it considering the effects of inflation and the size of their firms. By

doing so the finance managers should keep the cash conversion cycle short while watching their various

industry averages and practices as shown from the ANOVA test results.

5.1 Recommended areas for further research

Further study should be conducted using quarterly data as this way the various components of working capital

like will be subjected to a more robust regression with increased number of observations.

Also future study should consider economic cycles for the same sample. This will give a better insight into

working capital management in tandem with changing economic cycles for example the global financial crisis of

2007 to 2008.

Further research can also focus on carrying out an analysis at different business cycle. This can be done as a

case study of selected companies because at different stages of business growth, businesses are expected to

manage working capital differently while trying to maximize profits. It is expected that a business that is starting

might want to allow more days in accounts receivable than normal so as to attract more sales while at the same

time it may want to reduce the same at it studies the customers. All these actions can be verified by conducting a

study in that area.

Finally further study should be also consider using a different measure of firm performance other than GOP but

employing robust GMM applied to dynamic panel data. This may include market share, ROA, EPS, DPS among

others. The listed companies have to worry about the reaction of the shareholders whose main interest may be in

DPS and EPS.

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12

APPENDIX 1: REGRESSION RESULTS

Table 4: Effect of days of accounts receivables on firm profitability

Prob> chi2 = 0.000, Wald chi2(4) = 28.03

GOP Coef. Std. Err. (Robust) z P>|z| [95% Conf. Interval]

GOPt-1 0.15582 0.1466553 1.06 0.288 -0.1316192 0.4432592

AR -0.0009133 0.0018294 -0.50 0.618 -0.0044988 0.0026722

LNSALES 0.0794162 0.0676618 1.17 0.241 -0.0531986 0.212031

INFL 0.0002122 0.0035277 0.06 0.952 -0.0067019 0.0071264

INTERCEPT -0.0919019 0.5736794 -0.16 0.873 -1.216293 1.032489

Arellano-Bond test for AR(1) in first differences: z = -1.99 Pr> z = 0.046


Sargan test of overriding restrictions: chi2(26) = 22.22 Probability > chi2 = 0.676

Table 5: Effect of days of account payables on firm profitability

Prob> chi2 = 0.000 Wald chi2(4) = 21.78

GOP Coef. Std. Err. (Robust) z p>|z| [95% Conf. Interval]

GOPt-1 0.1124015 0.1070196 1.05 0.294 -0.0973531 0.3221561

AP 0.0016722 0.000916 1.83 0.068 -0.0001232 0.0034675

LNSALES 0.0874989 0.0576461 1.52 0.129 -0.0254855 0.2004833

INFL -0.002491 0.0044302 -0.56 0.574 -0.011174 0.0061919

INTERCEPT -0.377997 0.4149732 -0.91 0.362 -1.191329 0.4353355


Arellano-Bond test for AR(2) in first differences: z = 0.05 Pr> z = 0.960

Sargan test of overriding restrictions: chi2(26) = 21.07 Prob> chi2 = 0.739

Table 6: Effect of days in inventory on firm profitability

Prob> chi2 = 0.000, Wald chi2(4) = 24.10


GOPt-1 0.1714255 0.1372014 1.25 0.212 -0.0974843 0.4403352

INV 0.0044289 0.001891 2.34 0.019 0.0007226 0.0081351

LNSALES 0.0573022 0.0747448 0.77 0.443 -0.0891949 0.2037992

INFL -0.005086 0.0051625 -0.99 0.325 -0.0152044 0.0050322

INTERCEPT -0.393741 0.5440556 -0.72 0.469 -1.46007 0.6725885


Arellano-Bond test for AR(2) in first differences: z = 0.23 Pr> z = 0.817

Sargan test of overriding restrictions: chi2(26) = 19.99 Probability> chi2 = 0.792

Table 7: Effect of cash conversion cycle on firm profitability

Prob> chi2 = 0.000, Wald chi2(4) = 37.90


GOPt-1 0.1674274 0.15636 1.07 0.284 -0.1390325 0.4738873

CCC -0.0017719 0.0011856 -1.49 0.135 -0.0040957 0.0005519

LNSALES 0.067031 0.0592343 1.13 0.258 -0.0490661 0.183128

INFL 0.00063 0.0038416 0.16 0.870 -0.0068995 0.0081594

INTERCEPT 0.0255214 0.4532583 0.06 0.955 -0.8628486 0.9138914



Sargan test of overriding restrictions: chi2(26) = 19.64 Prob> chi2 = 0.808




13

Table 8: Effect of working capital management on firm profitability

Prob> chi2 = 0.000, Wald chi2(7) = 368.53


GOPt-1 0.1531341 0.1269879 1.21 0.228 -0.095758 0.4020259

AR -0.126363 0.0364225 -3.47 0.001 -0.197750 -0.054976

AP 0.12555 0.035491 3.54 0.000 0.0559839 0.195106

INV -0.122828 0.0348552 -3.52 0.000 -0.191143 -0.0545129

CCC 0.123566 0.0356369 3.47 0.001 0.0537186 0.1934127

LNSALES 0.0834587 0.0592553 1.41 0.159 -0.032680 0.199597

INFL -0.000577 0.0033513 -0.17 0.863 -0.007146 0.0059914

INTERCEPT -0.32832 0.5222939 -0.63 0.530 -1.351997 0.6953573



Sargan test of overriding. restrictions: chi2(58) = 38.54 Prob> chi2 = 0.977

APPENDIX 2: ANOVA RESULTS

Table 9: ANOVA of days of accounts receivables

Source SS df MS F Prob> F

Between groups 59683.704 5 11936.74 6.28 0.0000

Within groups 501755.94 264 1900.59

Total 561439.65 269 2087.136

Table 10: ANOVA of days of accounts receivables by sector (bonferroni correction)

Row Mean-

Col Mean

AGR AUTO_ACC COMMERCI CONSTRU

C

ENERGY

AUTO_ACC -1.46(1.000)

COMMERCI -12.80(1.000) -11.34(1.000)

CONSTRUC -34.04(0.001*) -32.58(0.008

*) -21.24(0.336)

ENERGY -21.23(0.905) -19.77(1.000) -8.43(1.000) 12.81(1.000)

MANUFACT -33.92(0.000*) -32.46(0.005

*) -21.11(0.276) 0.13(1.000) -12.68(1.000)

* ** Significant at 0.05 and 0.10 significance levels.

Table 11: ANOVA of days of accounts payables

SS df MS F Prob> F

Between groups 985523.252 5 197104.65 32.27 0.0000

Within groups 1612277.81 264 6107.112

Total 2597801.06 269 9657.253 Total 2597801.06

Table 12: ANOVA of days of accounts payables by sector (bonferroni correction)

Row Mean-

Col Mean

AGR AUTO_ACC COMMERCI CONSTRUC ENERGY

AUTO_ACC 57.24 (0.006*)

COMMERCI 177.53(0.000*) 120.28(0.000

*)

CONSTRUC 10.55(1.000) -46.69(0.078**

) -166.98(0.000*)

ENERGY 1.84(1.000) -55.40(0.153) -175.69(0.000*) -8.71(1.000)

MANUFACT 83.30(1.000) 26.06(0.000*) -94.23(0.000

*) 72.75(0.001

*) 81.46(0.000

*)

*** Significant at level of 0.05 and 0.10 respectively.

Table 13: ANOVA of number of days in inventory


Between groups 447709.961 5 89541.992 28.70 0.0000

Within groups 789456.145 253 3120.380

Total 1237166.11 258 4795.217




14

Table 14: ANOVA of number of days in Inventory by sector (bonferroni correction)

Row Mean-

Col Mean


AUTO_ACC 117.98(0.000*)

COMMERCI 13.93(1.000) -104.06(0.000*)

CONSTRUC 31.36(0.078**

) -86.63(0.000*) 17.43(1.000)

ENERGY -32.20(0.448) -150.18(0.000*) -46.12(0.046

*) -63.55(0.000

*)

MANUFACT 41.53(0.002*) -76.46(0.000

*) 27.60(0.269) 10.17(1.000) 73.72(0.000

*)

* ** Significant at 0.05 and 0.10 significance levels respectively.

Table 15: ANOVA of cash conversion cycle


Between groups 1350267.87 5 270053.575 64.66 0.0000

Within groups 1102572.35 264 4176.41041

Total 2452840.22 269 9118.36514

Table 16: ANOVA of cash conversion cycle by sector (bonferroni correction)

Row Mean-|

Col Mean


AUTO_ACC 70.33(0.000*)

COMMERCI -169.73(0.000*) -240.06(0.000

*)

CONSTRUC -2.19(1.000) -72.52(0.000*) 167.54(0.000

*)

ENERGY -44.22(0.128) -114.55(0.000*) 125.51(0.000

*) -42.03(0.219)

MANUFACT -64.64(0.000*) -134.97(0.000

*) 105.09(0.000

*) -62.45(0.000

*) -20.42(1.000)

* Significant at 0.05 significance level.
