Effect of working capital on profitability in indian markets and concept of zero working capital
1. Effect of Working Capital on Profitability
And
Concept of Zero Working Capital
(Analysis for Indian Markets)
Term Paper
(Concept taken from:
The Relationship between Working Capital Management and Profitability: A Vietnam Case.
International Research Journal of Finance & Economics; 2010, Issue 49, p59-67, 9p )
2. Profitability, Working Capital
Management and Zero Working Capital
INTRODUCTION
This paper investigates the relationship between the working capital management and the
firms’ profitability for a sample of top 30 Indian companies listed on the Bombay Stock
Exchange for the period of 6 years from 2005-2010. Management of working capital is an
important component of corporate financial management because it directly affects the
profitability of the firms. Management of working capital refers to management of current
assets and of current liabilities.
Assets in commercial firm consist of two kinds: fixed assets and current assets. Fixed assets
include land, building, plant, furniture, etc. Investment in these assets represents that of part
of firm’s capital, which is permanently blocked on a permanent or fixed basis and is also called
fixed capital that generates productive capacity. The form of these assets does not change, in
the normal course. In the contrast, current assets consist of raw materials, work-in-progress,
finished goods, bills receivables, cash, bank balance, etc. These assets are bought for the
purpose of production and sales, like raw material into semi-finished products, semi- finished
products into finished products, finished products into debtors and debtors turned over cash
or bills receivables. The fixed assets are used in increasing production of an organization and
the current assets are utilized in using the fixed assets for day to day working. Therefore, the
current assets, called working capital, may be regarded as the lifeblood of a business
enterprise. It refers to that part of the firm’s capital, which is required for financing short term.
Researchers have approached working capital management in numerous ways. While some
studied the impact of proper or optimal inventory management, others studied the
management of accounts receivables trying to postulate an optimal way policy that leads to
profit maximization (Lazaridis and Tryfonidis, 2006). According to Deloof (2003), the way
that working capital is managed has a significant impact on profitability of firms. Such results
indicate that there is a certain level of working capital requirement, which potentially
maximizes returns.
Working capital management plays an important role in a firm’s profitability and risk as well as
its value (Smith, 1980). There are a lot of reasons for the importance of working capital
management. For a typical manufacturing firm, the current assets account for over half of its
total assets. For a distribution company, they account for even more. Excessive levels of
current assets can easily result in a firm’s realizing a substandard return on investment.
However, Van Horne and Wachowicz (2004) point out that excessive level of current assets
may have a negative effect of a firm’s profitability, whereas a low level of current assets may
lead to lowers of liquidity and stock-outs, resulting in difficulties in maintaining smooth
operations.
3. Efficient management of working capital plays an important role of overall corporate strategy
in order to create shareholder value. Working capital is regarded as the result of the time lag
between the expenditure for the purchase of raw material and the collection for the sale of the
finished good. The way of working capital management can have a significant impact on both
the liquidity and profitability of the company (Shin and Soenen, 1998). The main purpose of any
firm is maximum the profit. But, maintaining liquidity of the firm also is an important objective.
The problem is that increasing profits at the cost of liquidity can bring serious problems to the
firm. Thus, strategy of firm must be a balance between these two objectives of the firms.
Because the importance of profit and liquidity are the same so, one objective should not be at
cost of the other. If we ignore about profit, we cannot survive for a longer period. Conversely, if
we do not care about liquidity, we may face the problem of insolvency. For these reasons
working capital management should be given proper consideration and will ultimately affect
the profitability of the firm.
Working capital management involves planning and controlling current assets and current
liabilities in a manner that eliminates the risk of inability to meet due short term obligations on
the one hand and avoid excessive investment in these assets on the other hand( Eljelly,2004).
Lamberson (1995) showed that working capital management has become one of the most
important issues in organization, where many financial managers are finding it difficult to
identify the important drivers of working capital and the optimum level of working capital. As a
result, companies can minimize risk and improve their overall performance if they can
understand the role and determinants of working capital. A firm may choose an aggressive
working capital management policy with a low level of current assets as percentage of total
assets, or it may also be used for the financing decisions of the firm in the form of high level of
current liabilities as percentage of total liabilities (Afza and Nazir, 2009).Keeping an optimal
balance among each of the working capital components is the main objective of working
capital management. Business success heavily depends on the ability of the financial managers
to effectively manage receivables, inventory, and payables (Filbeck and Krueger, 2005). Firms
can decrease their financing costs and raise the funds available for expansion projects by
minimizing the amount of investment tied up in current assets. Lamberson (1995) indicated
that most of the financial managers’ time and efforts are consumed in identifying the non-
optimal levels of current assets and liabilities and bringing them to optimal levels. An optimal
level of working capital is a balance between risk and efficiency. It asks continuous monitoring
to maintain the optimum level of various components of working capital, such as cash
receivables, inventory and payables (Afza and Nazir, 2009). A popular measure of working
capital management is the cash conversion cycle, which is defined as the sum of days of sales
outstanding (average collection period) and days of sales in inventory less days of payables
outstanding (Keown et al, 2003). The longer this time lag, the larger the investment in working
capital. A longer cash conversion cycle might increase profitability because it leads to higher
sales. However, corporate profitability might also decrease with the cash conversion cycle, if
the costs of higher investment in working capital is higher and rises faster than the benefits of
holding more inventories and granting more inventories and trade credit to customers (Deloof,
2003).
4. Lastly, working capital management plays an important role in managerial enterprise, it may
impact to success or failure of firm in business because working capital management affect to
the profitability of the firm. The thesis is expected to contribute to better understanding of
relationship between working capital management and profitability in order to help managers
take a lot of solutions to create value for their shareholders, especially in emerging markets
like India.
LITERATURE REVIEW
The management of working capital is defined as the “management of current assets and
current liabilities, and financing these current assets.” Working capital management is
important for creating value for shareholders according to Shin and Soenen (1998).
Management of working capital was found to have a significant impact on both profitability and
liquidity in studies in different countries.
Long et al. developed a model of trade credit in which asymmetric information leads good
firms to extend trade credit so that buyers can verify product quality before payment. Their
sample contained all industrial (SIC 2000 through 3999) firms with data available from
COMPUSTAT for the three-year period ending in 1987 and used regression analysis. They
defined trade credit policy as the average time receivables are outstanding and measured this
variable by
computing each firm's days of sales outstanding (DSO), as accounts receivable per dollar of
daily sales. To reduce variability, they averaged DSO and all other measures over a three year
period. They found evidence consistent with the model. The findings suggest that producers
may increase the implicit cost of extending trade credit by financing their receivables
through payables and short-term borrowing.
Shin and Soenen(1998) researched the relationship between working capital management
and value creation for shareholders. The standard measure for working capital management is
the cash conversion cycle (CCC). Cash conversion period reflects the time span between
disbursement and collection of cash. It is measured by estimating the inventory conversion
period and the receivable Conversion period, less the payables conversion period. In their
study, Shin and Soenen(1998) used net-trade cycle (NTC) as a measure of working capital
management. NTC is basically equal to the cash conversion cycle (CCC) where all three
components are expressed as a percentage of sales. NTC may be a proxy for additional
working capital needs as a function of the projected sales growth. They examined this
relationship by using correlation and regression analysis, by industry, and working capital
intensity. Using a COMPUSTAT sample of 58,985 firm years covering the period 1975-1994,
they found a strong negative relationship between the length of the firm's net-trade cycle and
its profitability. Based on the findings, they suggest that one possible way to create
Shareholder value is to reduce firm’s NTC.
To test the relationship between working capital management and corporate profitability,
Deloof (2003) used a sample of 1,009 large Belgian non-financial firms for the 1992-1996
periods. The result from analysis showed that there was a negative between profitability
that was measured by gross operating income and cash conversion cycle as well number of
day’s
5. accounts receivable and inventories. He suggested that managers can increase corporate
profitability by reducing the number of day’s accounts receivable and inventories. Less
profitable firms waited longer to pay their bills.
Ghosh and Maji (2003) attempted to examine the efficiency of working capital management of
Indian cement companies during 1992 - 93 to 2001 - 2002. They calculated three index values -
performance index, utilization index, and overall efficiency index to measure the efficiency of
working capital management, instead of using some common working capital management
ratios. By using regression analysis and industry norms as a target efficiency level of individual
firms, Ghosh and Maji (2003) tested the speed of achieving that target level of efficiency by
individual firms during the period of study and found that some of the sample firms
successfully improved efficiency during these years.
Singh and Pandey (2008) had an attempt to study the working capital components and the
impact of working capital management on profitability of Hindalco Industries Limited for
period from 1990 to 2007. Results of the study showed that current ratio, liquid ratio,
receivables turnover ratio and working capital to total assets ratio had statistically significant
impact on the profitability of Hindalco Industries Limited.
Lazaridis and Tryfonidis (2006) 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 (2007) have selected a sample of 94 Pakistani firms listed on Karachi Stock
Exchange for a period of 6 years from 1999-2004 to study the effect of different variables of
working capital management on the net operating profitability. From result of study, they
showed that there was a negative relationship between variables of working capital
management including the average collection period, inventory turnover in days, average
collection period, cash conversion cycle and profitability. Besides, they also indicated that
size
of the firm, measured by natural logarithm of sales, and profitability had a positive relationship.
Falope and Ajilore (2009) 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.
6. Finally, Afza and Nazir (2009) made an attempt in order to investigate the traditional
relationship between working capital management policies and a firm’s profitability for a
sample of 204 non-financial firms listed on Karachi Stock Exchange (KSE) for the period
1998-
2005.The study found significant different among their working capital requirements and
financing policies across different industries. Moreover, regression result found a negative
relationship between the profitability of firms and degree of aggressiveness of working
capital investment and financing policies. They suggested that managers could crease value if
they adopt a conservative approach towards working capital investment and working capital
financing policies.
METHODOLOGY
Data Collection
A database was built from a selection of approximately 30 financial-reports (for the purpose of this
research, firms in financial sector, banking and finance, insurance, leasing, business service,
renting,and other service are excluded from the sample) of Bombay Stock Exchange-30 for 6 years
from 2005 to
2010. The selection was drawn from Bombay Stock Exchange [http://www.bseindia.com/about/
abindices/bse30.asp] on the basis of free float market capitalization method. The balance sheets of
the companies were taken from the ‘Capitaline’ [http://www.capitaline.com/new/index.asp].
For the purpose of this research out of top 30 BSE companies 25 were found apt for the study.
We used cross sectional yearly data in this study. Thus 25 companies yielded 150 observations
for 6 years. The data analysis has been done in two steps [Pre-Recession and Post-
Recession].The post-recession period is taken from 2008 onwards. The objective of the
research is to make a comparative study amongst the top 30 companies in pre and post-
recession. The analysis of zero working capital has also been done in both the scenarios.
The selection of the companies is done on the free float market capitalization method.Free-float
market capitalization takes into consideration only those shares issued by the company that
are readily available for trading in the market. It generally excludes promoters' holding,
government holding, strategic holding and other locked-in shares that will not come to the
market for trading in the normal course. The major advantages of free- float methodology is
that it reflects the market trends more rationally as it takes into consideration only those
shares that are available for trading in the market. It makes the index more broad-based by
reducing the concentration of top few companies in Index and aids both active and passive
investing styles. Globally, the free-float Methodology of index construction is considered to be
an
industry best practice and all major index providers like MSCI, FTSE, S&P and STOXX have
adopted the same. The MSCI India Standard Index, which is followed by Foreign Institutional
Investors (FIIs) to track Indian equities, is also based on the Free-float Methodology.
NASDAQ-
7. 100, the underlying index to the famous Exchange Traded Fund (ETF) - QQQ is based on the
Free-float Methodology.
Variables
The variables used in this study based on previous researches about the relationship
between working capital management and profitability.
Gross operating profitability that is a measure of profitability of firm is used as dependent
variable. It is defined as sales minus cost of goods sold, and divided by total assets minus
financial assets. For a number of firms in the sample, financial assets, which are chiefly shares
in affiliated firms, are a significant part of total assets. When the financial assets are main part
of total assets, its operating activities will contribute little to overall return on assets. Hence,
that
is the reason why return on assets is not considered as a measure of profitability. Number of
days accounts receivable used as proxy for the collection policy is an independent variable. It
is calculated as (accounts receivable x 365)/sales. Number of days inventories used as proxy
for the inventory policy is an independent variable. It is calculated as (inventories x 365)/ cost
of goods sold. Number of days accounts payable used as proxy for the payment policy is an
independent variable. It is calculated as (accounts payable x 365)/ cost of goods sold.
The cash conversion cycle used as a comprehensive measure of working capital management is
another independent variable. It is calculated as (number of days accounts receivable +
number of days inventory – number of days accounts payable).
Various studies have utilized the control variables along with the main variables of working
capital in order to have an opposite analysis of working capital management on the firm’s
profitability (Deloof, 2003; Lazaridis and Tryfonidis, 2006). The logarithm of sales used to
measure size of firm is a control variable. In addition, debt ratio used as proxy for leverage,
calculated by dividing total debt by total assets, and ratio of fixed financial assets to total
assets are also control variable in the regressions. According to Deloof (2003) fixed financial
assets are mainly shares in affiliated firms, intended to contribute to the activities of the firm
that holds them, by establishing a lasting and specific relation and loans that were granted
with the same purpose.
• Number of days accounts receivable (AR)= Average of accounts receivable / Sales* 365
• Number of days accounts payable (AP)= Average of accounts payable / Cost of
goods sold *365
• Number of days inventory (INV) = Average of inventory / Cost of goods sold * 365
• Cash conversion cycle (CCC) = AR+ INV- AP
• Natural Logarithm of sales (LOS) = ln(sale)
• Debt ratio (DR)= Total debt/ Total assets
• Fixed financial assets to total assets (FFAR) = Secured Loans +Unsecured Loans / Total
assets
• Gross operating profitability (GROSSPR) = ( Sales – Cost of goods sold)/ (Total assets –
Financial assets)
8. Data Analysis- Post Recession (2008-2010)
Descriptive Statistics
GROSSPR LOS CCC DR FFAR AR AP IN V
Mean 0.707068614 10.15297 7.690029 0.36495 0.379376 53.7279 198.708 116.2949
Standard deviation 0.772435624 0.89115 227.9693 0.240711 0.274829 44.94332 121.2027 216.7434
Minimum -0.8196472 8.485658 -502.96 0 0 9.515915 36.93339 0
Maximum 3.530351987 12.04457 780.8749 0.773568 1.129756 211.2627 535.7428 1080.013
Correlation Analysis- Post recession (2008-2010)
GR O S S P R L O S CCC DR FFA R AR AP IN V
GROSSPR 1 0.200821 -0.14155 0.175411 -0.23543 -0.31465 -0.02084 -0.21747
LOS 0.20082069 1 -0.14247 0.049786 -0.16878 -0.25041 -0.11312 -0.31955
CCC -0.14155456 -0.14247 1 0.067349 0.192017 0.239722 -0.22239 0.738308
DR 0.17541076 0.049786 0.067349 1 0.782423 -0.22441 0.253636 0.11557
FFAR -0.23542542 -0.16878 0.192017 0.782423 1 0.013397 0.256972 0.22589
AR -0.31464537 -0.25041 0.239722 -0.22441 0.013397 1 -0.00483 0.115348
AP -0.0208431 -0.11312 -0.22239 0.253636 0.256972 -0.00483 1 0.365828
INV -0.21746906 -0.31955 0.738308 0.11557 0.22589 0.115348 0.365828 1
Multiple Regression Analysis (2008-2010)
Model 1
GROSSPR is used a dependent variable.Cash Conversion Cycle is used as an independent
variable while Debt Ratio (DR) , Natural Logarithm of sales (LOS), Fixed Financial Assets
Ratio(FFAR) are used as control variables.
The cash conversion cycle is used popular to measure efficiency of working capital
management. From result of regression running indicates that there is a negative relationship
between cash conversion cycle and operating profitability. The coefficient is -8.3E-05 with p-
value 0.000. It is highly significant at α= 0.01. This implies that the increase or decrease in the
cash conversion cycle does not significantly affect profitability of the firm with such a low
coefficient. The adjusted R-squaredis 27% showing significant fitting of the model in post-
recession scenario.
9. SUMMARY OUTPUT
Regression Statistics
Multiple R 0.626
R Square 0.392 Goodness of Fit < 0.80
Adjusted R Square 0.270
Standard Error 0.674
Observations 25
ANOVA
df SS MS F P-value
Regression 4 5.842577 1.460644 3.219461 0.034
Residual 20 9.073843 0.453692
Total 24 14.91642 Confidence Level
0.95 0.99
Coefficien Standard t Stat P-value Lower 95%Upper 95%Lower 99%Upper 99%
Intercept 1.103556 1.656703 0.666116 0.513 -2.35227 4.559379 -3.61033 5.81744
LOS -0.04523 0.161065 -0.28085 0.782 -0.38121 0.290741 -0.50352 0.41305
CCC -8.3E-05 0.00061 -0.13641 0.893 -0.00135 0.001188 -0.00182 0.001651
DR 3.037306 0.946388 3.209368 0.004 1.063176 5.011436 0.344512 5.7301
FFAR -2.75464 0.850236 -3.23985 0.004 -4.5282 -0.98108 -5.17385 -0.33543
GROSSPR = 1.104 -0.045*LOS-(8.3E-05)*CCC +3.037*DR -2.755*FFAR
Model 2
GROSSPR is used a dependent variable. Number of days accounts receivable (AR) is used as an
independent variable while Debt Ratio (DR) , Natural Logarithm of sales (LOS), Fixed Financial
Assets Ratio(FFAR) are used as control variables.
The result of this regression indicates that the coefficient ofaccount receivable is negative with -
0.002 and p-value is 0.001. It shows highly significant at α = 0.01.This implies that the increase
or decrease in accounts receivable will significantly affect profitability of firm. Debt ratio is
used as a proxy for leverage, from analysis of regression shows that there is a positive
relationship with dependent variable. The coefficient is 2.845 and has significant at α=
0.01.This means that if there is an increase in debt ratio it will lead to increase in profitability of
firm. The result also indicates that there is a negative relationship among logarithm of sale,
fixed financial assetsto total assets and profitability. The coefficients are -0.145 and -0.633
respectively. Both of them aresignificant at α = 0.01. It implies that the size of firm has effect
on profitability of firm. The larger size leads to more profitable. The adjusted Rsquared, also
called the coefficient of multiple determinations, is the percent of the variance in the
dependent explained uniquely or jointly by the independent variables and is 28.4% showing
predicted model is highly accurate.
10. SUMMARY OUTPUT
Regression Statistics
Multiple R 0.635
R Square 0.403 Goodness of Fit < 0.80
Adjusted R Square 0.284
Standard Error 0.667
Observations 25
ANOVA
df SS MS F P-value
Regression 4 6.014731 1.503683 3.378421 0.029
Residual 20 8.901689 0.445084
Total 24 14.91642 Confidence Level
0.95 0.99
Coefficient Standard t Stat P-value Lower 95%Upper 95%Lower 99%Upper 99%
Intercept 1.3880644 1.703477 0.814842 0.425 -2.16533 4.941455 -3.45891 6.235035
ln(sale) -0.059769 0.161078 -0.37106 0.714 -0.39577 0.276233 -0.51809 0.398552
DR 2.8454771 0.986304 2.88499 0.009 0.788083 4.902871 0.039107 5.651847
FFAR -2.639849 0.854454 -3.08951 0.006 -4.42221 -0.85749 -5.07106 -0.20864
AR -0.002068 0.003247 -0.63699 0.531 -0.00884 0.004705 -0.01131 0.00717
GROSSPR = 1.388 -0.06*LOS +2.845*DR -2.64*FFAR -0.002*AR
Model 3
The dependent variable gross operating profit and the control variables are the same as the
previous models. The only difference is number of days accounts receivable variable
replaced by number of days accounts payable variable.
Looking at coefficients, we see that there is a negative relationship between number of days
accounts payable and profitability of firm. The coefficient is 0.001. It implies that the increase
or decrease in the average payment period significantly affects profitability of the firm. The
negative relationship between the average payment period and profitability indicates that the
more profitable firms wait shorter to pay their bill.The adjusted R2 is 27.0%showing significant
fitting of the model in post-recession scenario.
11. SUMMARY OUTPUT
Regression Statistics
Multiple R 0.626
R Square 0.391 Goodness of Fit < 0.80
Adjusted 0.270
Standard 0.674
Observati 25
ANOVA
df SS MS F P-value
Regressio 4 5.837249 1.459312 3.214638 0.034
Residual 20 9.07917 0.453959
Total 24 14.91642 Confidence Level
0.95 0.99
Coefficien Standard t Stat P-value Lower 95%Upper 95%Lower 99%Upper 99%
Intercept 1.117254 1.688986 0.661494 0.516 -2.40591 4.640417 -3.68848 5.922993
ln(sale) -0.04492 0.161503 -0.27813 0.784 -0.38181 0.291969 -0.50445 0.41461
DR 3.060157 0.947418 3.229997 0.004 1.083878 5.036436 0.364431 5.755883
FFAR -2.77247 0.836692 -3.31361 0.003 -4.51778 -1.02716 -5.15314 -0.3918
Ap -9.6E-05 0.001161 -0.08284 0.935 -0.00252 0.002326 -0.0034 0.003208
GROSSPR = 1.117 -0.045*LOS +3.06*DR -2.772*FFAR -(9.6E-05)*Ap
Model 4
This model is run using the number of days inventories as an independent variable as substitute
of average payment period. The other variables are same as they have been in first and second
model.
The result of regression indicates that the relationship between number of days inventories
and profitability is negative. The coefficient of this relationship is -0.00049and significant at α =
0.01.This means that if the inventory takes less time to sell, it will adversely affect
profitability.The adjusted R2 is 28.9% demonstrating the desirable superposition of
predicted and actual values.
12. SUMMARY OUTPUT
Regression Statistics
Multiple R 0.639
R Square 0.408 Goodness of Fit < 0.80
Adjusted R Square 0.289
Standard Error 0.665
Observations 25
ANOVA
df SS MS F P-value
Regression 4 6.082874 1.520718 3.443053 0.027
Residual 20 8.833546 0.441677
Total 24 14.91642 Confidence Level
0.95 0.99
Coefficien Standard t Stat P-value Lower 95%Upper 95%Lower 99%Upper 99%
Intercept 1.469101 1.70804 0.86011 0.400 -2.09381 5.032009 -3.39085 6.329054
ln(sale) -0.0778 0.164889 -0.47185 0.642 -0.42175 0.26615 -0.54697 0.391362
DR 3.040488 0.92848 3.274695 0.004 1.103713 4.977262 0.398648 5.682327
FFAR -2.69985 0.830232 -3.25192 0.004 -4.43169 -0.96802 -5.06214 -0.33756
INV PERIOD) -0.00049 0.000659 -0.75045 0.462 -0.00187 0.000879 -0.00237 0.00138
GROSSPR= 1.469 -0.078*ln(sale) +3.04*DR -2.7*FFAR 0*INV PERIOD)
Data Analysis- Pre Recession (2005-2007)
Descriptive Statistics
GROSSPR LOS CCC DR FFAR AR AP IN V
m e an 0.788228544 56.12815 198.2344 121.5334 -20.5729 0.339925 9.4331 0.341595
maximum 2.592902249 177.3841 636.4138 1168.658 1188.669 0.738432 11.46191 1.08909
Standard Deviation 0.507572269 43.31043 140.3418 236.4202 300.8036 0.241264 0.959618 0.268632
Variance 0.257629608 1875.794 19695.82 55894.51 90482.83 0.058208 0.920867 0.072163
13. Correlation Analysis- Pre recession (2005-2007)
GR O S S P R L O S CCC DR FFAR AR AP IN V
GROSSPR 1 -0.43468 0.121837 -0.05941 -0.16613 -0.18915 0.198846 -0.28271
AR -0.4346759 1 0.074751 0.553503 0.544139 0.217313 -0.67604 0.305006
AP 0.12183696 0.074751 1 -0.03903 -0.48647 0.305506 -0.07537 0.264846
INV -0.059413 0.553503 -0.03903 1 0.883868 0.415302 -0.50869 0.394123
CCC -0.1661258 0.544139 -0.48647 0.883868 1 0.215165 -0.46199 0.230116
DR -0.1891457 0.217313 0.305506 0.415302 0.215165 1 -0.30876 0.829285
LOS 0.19884624 -0.67604 -0.07537 -0.50869 -0.46199 -0.30876 1 -0.42621
FFAR -0.2827125 0.305006 0.264846 0.394123 0.230116 0.829285 -0.42621 1
Multiple Regression Analysis (2005-2007)
Model 1
GROSSPR is used a dependent variable.Cash Conversion Cycle is used as an independent
variable while Debt Ratio (DR) , Natural Logarithm of sales (LOS), Fixed Financial Assets
Ratio(FFAR) are used as control variables.
14. SUMMARY OUTPUT
Regression Statistics
Multiple R 0.315
R Square 0.100 Goodness of Fit < 0.80
Adjusted -0.101
Standard 0.532
Observati 23
ANOVA
df SS MS F P-value
Regressio 4 0.564121 0.14103 0.49739 0.738
Residual 18 5.10373 0.283541
Total 22 5.667851 Confidence Level
0.95 0.99
Coefficien Standard t Stat P-value Lower 95%Upper 95%Lower 99%Upper 99%
Intercept 0.682018 1.425708 0.478371 0.638 -2.31328 3.67732 -3.4218 4.785834
CCC -0.00016 0.000428 -0.36515 0.719 -0.00105 0.000742 -0.00139 0.001075
DR 0.306326 0.848756 0.360911 0.722 -1.47684 2.089495 -2.13677 2.749418
LOS 0.024669 0.144624 0.170572 0.866 -0.27917 0.328513 -0.39162 0.44096
FFAR -0.68453 0.799201 -0.85652 0.403 -2.36359 0.994526 -2.98498 1.61592
GROSSPR = 0.682-0.000156151*CCC +0.306*DR +0.025*ln(sale) -0.685*FFAR
The cash conversion cycle is used popular to measure efficiency of working capital
management. From result of regression running indicates that there is a negative relationship
between cash conversion cycle and operating profitability. The coefficient is -0.000156151
with p-value 0.000. It is highly significant at α= 0.01. This implies that the increase or decrease
in the cash conversion cycle does not significantly affects profitability of the firm with such a
low coefficient. The adjusted R-squaredis -10.1% showing significant non-fitting of the model in
pre- recession scenario.
Model 2
GROSSPR is used a dependent variable. Accounts Receivable Periodis used as an independent
variable while Debt Ratio (DR), Natural Logarithm of sales (LOS), Fixed Financial Assets
Ratio(FFAR) are used as control variables.
15. SUMMARY OUTPUT
Regression Statistics
Multiple R 0.504
R Square 0.254 Goodness of Fit < 0.80
Adjusted 0.088
Standard 0.485
Observati 23
ANOVA
df SS MS F P-value
Regressio 4 1.437961 0.35949 1.529785 0.236
Residual 18 4.22989 0.234994
Total 22 5.667851 Confidence Level
0.95 0.99
Coefficien Standard t Stat P-value Lower 95%Upper 95%Lower 99%Upper 99%
Intercept 2.643511 1.624431 1.627346 0.121 -0.76929 6.056313 -2.03232 7.319338
AR -0.00638 0.00324 -1.96963 0.064 -0.01319 0.000425 -0.01571 0.002944
DR 0.257103 0.769654 0.33405 0.742 -1.35988 1.874085 -1.9583 2.472505
LOS -0.14506 0.154195 -0.94076 0.359 -0.46901 0.178892 -0.5889 0.298782
FFAR -0.63273 0.726966 -0.87037 0.396 -2.16003 0.894568 -2.72526 1.459797
GROSSPR = 2.644 -0.006*AR +0.257*DR -0.145*LOS -0.633*FFAR
The result of this regression indicates that the coefficient ofaccount receivable is negative with -
0.006 and p-value is 0.001. It shows highly significant at α = 0.01.This implies that the increase
or decrease in accounts receivable will significantly affect profitability of firm. Debt ratio is
used as a proxy for leverage, from analysis of regression shows that there is apositive
relationship with dependent variable. The coefficient is 0.257 and has significant at α=
0.01.This means that if there is an increase in debt ratio it will lead to increase in profitability of
firm. The result also indicates that there is a negative relationship among logarithm of sale,
fixed financial assetsto total assets and profitability. The coefficients are -0.145 and -0.633
respectively. Both of them aresignificant at α = 0.01. It implies that the size of firm has effect
on profitability of firm. The larger size leads to more profitable. The adjusted Rsquared, also
called the coefficient of multiple determinations, is the percent of thevariance in the
dependent explained uniquely or jointly by the independent variables and is 8.8% showing
significant non-fitting of the model in pre- recession scenario.
16. Model 3
The dependent variable gross operating profit and the control variables are the same as the
previous models. The only difference is number of days accountsreceivable variable replaced
by number of days accounts payable variable.
Regression Statistics
Multiple R 0.360
R Square 0.129 Goodness of Fit < 0.80
Adjusted -0.064
Standard 0.524
Observati 23
ANOVA
df SS MS F P-value
Regressio 4 0.733709 0.183427 0.669151 0.622
Residual 18 4.934143 0.274119
Total 22 5.667851 Confidence Level
0.95 0.99
Coefficien Standard t Stat P-value Lower 95%Upper 95%Lower 99%Upper 99%
Intercept 0.413807 1.295302 0.319467 0.753 -2.30752 3.135135 -3.31464 4.142256
AP 0.000727 0.000836 0.869819 0.396 -0.00103 0.002483 -0.00168 0.003133
DR 0.164132 0.841521 0.195042 0.848 -1.60384 1.932101 -2.25814 2.586399
LOS 0.043513 0.129147 0.33693 0.740 -0.22781 0.314841 -0.32823 0.415255
FFAR -0.69077 0.785234 -0.8797 0.391 -2.34049 0.958946 -2.95102 1.569481
GROSSPR= 0.414 +0.001*AP +0.164*DR +0.044*LOS -0.691*FFAR
Looking at coefficients, we see that there is a positive relationship between number of days
accounts payable and profitability of firm. The coefficient is 0.001. It implies that the increase
or decrease in the average payment period significantly affects profitability of the firm. The
positive relationship between the average paymentperiod and profitability indicates that the
more profitable firms wait longer to pay their bill.The adjusted R2 is -6.4%showing
significant non-fitting of the model in pre-recession scenario.
Model 4
This model is run using the number of days inventories as an independent variable as
substitute of average payment period. The other variables are same as they have been in first
andsecond model.
17. SUMMARY OUTPUT
Regression Statistics
Multiple R 0.317
R Square 0.100 Goodness of Fit < 0.80
Adjusted R Square -0.100
Standard Error 0.532
Observations 23
ANOVA
df SS MS F P-value
Regression 4 0.56857 0.142143 0.50175 0.735
Residual 18 5.099281 0.283293
Total 22 5.667851 Confidence Level
0.95 0.99
Coefficien Standard t Stat P-value Lower 95%Upper 95%Lower 99%Upper 99%
Intercept 0.244345 1.450171 0.168494 0.868 -2.80235 3.291041 -3.92989 4.418575
INV 0.000227 0.000588 0.386211 0.704 -0.00101 0.001463 -0.00147 0.00192
DR 0.20162 0.868131 0.232246 0.819 -1.62226 2.025495 -2.29724 2.700483
LOS 0.071176 0.145603 0.488833 0.631 -0.23473 0.377076 -0.34793 0.490286
FFAR -0.65478 0.798929 -0.81957 0.423 -2.33326 1.023712 -2.95445 1.644894
GROSSPR = 0.244 +0*INV +0.202*DR +0.071*LOS -0.655*FFAR
The result of regression indicates that the relationship between number of days inventories
and profitability is positive. The coefficient of this relationship is 0.000227and significant at α =
0.01.This means that if the inventory takes more time to sell, it will adversely affect
profitability.The adjusted R2 is -10.0% demonstrating the poor mismatch of predicted and
actual values.
18. FINDINGS
1).Comparison of Models in Pre and Post-Recession Scenario and their accuracy
Po s t-Re ce s s i o n (2008-10) Pre -Re ce s s i o n (2005-2007)
y = 1.104 -(8.3E-05)*CCC+3.037*DR-0.045*LOS -2.755*FFAR y = 0.682-0.000156151*CCC +0.306*DR +0.025*LOS -0.685*FFAR
y = 1.388-0.002*AR+2.845*DR -0.06*LOS -2.64*FFAR y = 2.644 -0.006*AR +0.257*DR -0.145*LOS -0.633*FFAR
y = 1.117-(9.6E-05)*AP +3.06*DR-0.045*LOS -2.772*FFAR y= 0.414 +0.001*AP +0.164*DR +0.044*LOS -0.691*FFAR
y= 1.469-0.00049*I NV+3.04*DR-0.078*LOS -2.7*FFAR y = 0.244 +0.000227*I NV +0.202*DR +0.071*LOS -0.655*FFAR
y=GROSSPR y=GROSSPR
Post-Recession Post-Recession Pre-Recession Pre-Recession
R squared Adjusted R squared R squared Adjusted R squared
0.392 0.27 0.1 -0.101
0.403 0.284 0.254 0.088
0.391 0.27 0.129 -0.064
0.408 0.289 0.1 -0.1
19. 2). Zero Working Capital
Post-recession Pre-Recession
Name of Company ZWC/SALES (DEBT+INV)/CR ZWC/SALES (DEBT+INV)/CR
Bajaj Auto Limited -0.14760968 0.305006917 NA NA
Bharat Heavy Electrica 0.42632056 2.005056274 NA NA
Bharti Airtel Ltd. -0.53057015 0.14011567 -0.513717141 0.350060673
Cipla Ltd. 0.32091196 2.112357862 0.394976749 3.280783186
DLF Ltd. 0.8628121 3.476930292 1.733576758 9.929247501
Hindalco Industries Lt 0.17331781 2.84926696 0.039387942 1.125764298
Hindustan Unilever Lt -0.21374335 0.356095324 -0.226908756 0.414290102
Infosys Technologies 0.11309241 2.947323944 0.100052383 2.322943723
ITC Ltd. -0.03208585 0.876324645 -0.030100031 0.883652606
Jaiprakash Associates 0.32073783 1.818343488 0.199475522 1.793472369
Jindal Steel & Power L -0.14302859 0.559426555 -0.231194816 0.539112119
Larsen & Toubro Limit -0.15319893 0.754910487 -0.023156809 0.952462584
Mahindra & Mahindra -0.07170058 0.778542677 -0.071739129 0.790302373
Maruti Suzuki India Lt -0.10113661 0.41384391 0.005880034 1.066012686
NTPC Ltd. -0.05024592 0.785914843 -0.067983523 0.646880958
ONGC Ltd. -0.08407589 0.626334121 -0.037732679 0.777293528
Reliance Communicat -0.3068965 0.350920962 -0.351683776 0.260418519
Reliance Industries Lt -0.18848518 0.495807458 -0.155707724 0.488099774
Reliance Infrastructur -0.13243581 0.604715984 -0.122591396 0.750957986
Sterlite Industries (In -0.01913997 0.891083629 0.069588681 1.548428041
Tata Consultancy Serv 0.03870822 1.217031577 0.089716164 1.620752145
Tata Motors Ltd. -0.26378145 0.417184841 -0.105438414 0.56767143
Tata Power Company -0.0017847 0.995389468 0.02260914 1.070131198
Tata Steel Ltd. -0.07523184 0.785178178 -0.135814713 0.594773188
Wipro Ltd. 0.08404132 1.600027293 0.110405943 2.038582934
Mean -0.00700835 1.126525334 0.030082627 1.47009104
Standard deviation 0.27567901 0.91253253 0.415137223 1.982359862
Maximum 1.72562419 6.953860584 1.733576758 9.929247501
Minimum -0.53057015 0.14011567 -0.513717141 0.260418519
Range 2.25619435 6.813744914 2.247293899 9.668828982
21. Conclusions-
Let us first of all try to compare the models derived using multiple regressions and check
their verifications-
1).The first model is
GROSSPRit= B0 + B1 (CCCit) + B2 (DRit) + B3 (LOSit) + B4 (FFARit)
in pre & post - recession scenario. The coefficient of LOS( log of sales ) changes its sign from -
0.045 in post – recession to +0.025 in pre-recession model. Also, there is dramatic change in
the coefficient of CCC from a very low negative value in post-recession to a higher absolute
value in pre-recession model. This clearly demonstrates the impact on sales after recession and
cash conversion cycle. Ideally speaking, the coefficient of LOS should have been positive and
GROSSPR must increase with increase of sales (LOS). This is truly encountered before 2008 as
the coefficient is positive. But after recession the coefficient of LOS is negative clearly
demonstrating abrupt changes in market due to unexplained forcing factors in times of
recession. Our finding shows that there is a strong negative relationship between profitability,
measured through gross operating profit, and the cash conversion cycle. This means that as the
cash conversion cycle increases, it will lead to declining of profitability of firm. Therefore, the
managers can create a positive value for the shareholders by handling the adequate cash
conversion cycle and keeping each different component to an optimum level.
The most striking comparison is yielded by the R-squared values and adjusted R-squared
values. The R-squared value changes from 0.392 to 0.1 and adjusted R-squared from 0.27 to
-0.101. From the exceptionally low values of R-squared and adjusted R-squared for the pre-
recession scenario we conclude that the same model is no longer applicable for the pre-
recession
scenario which is expected in the wake of extreme fluctuations in two data sets.
2).The second model is
GROSSPRit= B0 + B1 (ARit) + B2 (DRit) + B3 (LOSit) + B4 (FFARit)
in pre & post - recession scenario. The intercept is now about twice in pre-scenario as that
of post and simultaneously the gross profitability now decreases almost 4 times rapidly in
post- recession as compared to pre-recession scenario. The sign of coefficient of LOS is
inversed to ideal behavior that is, negative. The coefficient of AR is ideal negative and is 3
times in pre- recession than post-recession. This means as accounts receivables period
increases the gross profitability decreases three times faster before recession as compared
to post-period. The most striking comparison is yielded by the R-squared values and
adjusted R-squared values.
The R-squared value changes from 0.403 to 0.254 and adjusted R-squared from 0.284 to
0.088. From the exceptionally low values of R-squared and adjusted R-squared for the pre-
recession scenario we conclude that the same model is no longer applicable for the pre-
recession scenario which is expected in the wake of extreme fluctuations in two data sets.
22. 3).The third model is
GROSSPRit= B0 + B1 (APit) + B2 (DRit) + B3 (LOSit) + B4 (FFARit)
The coefficient of LOS (log of sales) abruptly changes its sign from -0.045 in post–recession to
+0.044 in pre-recession model. At the same time the GROSSPR is increasing ideally at the
positive rate of 0.001 per unit increase of Accounts Payable Period. On the other hand, the
same decreases after recession with AP as opposed to ideal expected behavior. This clearly
demonstrates the impact on sales and accounts payable cycle after recession. The rate of
decrease of GROSSPR with FFAR has almost quadrupled after recession as expected in terms
of exponential increase in secured and unsecured loans. The most striking comparison is
yielded by the R-squared values and adjusted R-squared values. The R-squared value changes
from
0.391 to 0.129 and adjusted R-squared from 0.270 to -0.064. From the exceptionally low values
of R-squared and adjusted R-squared for the pre-recession scenario we conclude that the
same model is no longer applicable for the pre-recession scenario which is expected in the
wake of extreme fluctuations in two data sets.
4).The fourth model is
GROSSPRit= B0 + B1 (INVit) + B2 (DRit) + B3 (LOSit) + B4 (FFARit)
in pre & post - recession scenario. The coefficient of LOS( log of sales ) changes its sign from
non-ideal negative 0.078 in post – recession to +0.071 in pre-recession model. Also, there is
dramatic change in the coefficient of INV from a negative value in post-recession to a higher
positive in pre-recession model. This clearly demonstrates the impact on sales after recession
and inventory period. Ideally speaking, the coefficient of LOS should have been positive and
GROSSPR must increase with increase of sales (LOS). This is truly encountered before 2008 as
the coefficient is positive. But after recession the coefficient of LOS is negative clearly
demonstrating abrupt changes in market due to unexplained forcing factors in times of
recession. Our finding shows that there is a strong negative relationship between
profitability,
measured through gross operating profit, and the Inventory turnover period. This means that as
the Inventory turnover period increases, it will lead to increase or decrease in the
profitability of firm. The most striking comparison is yielded by the R-squared values and
adjusted R- squared values. The R-squared value changes from 0.392 to 0.1 and adjusted R-
squared from
0.27 to -0.101. From the exceptionally low values of R-squared and adjusted R-squared for
the pre-recession scenario we conclude that the same model is no longer applicable for the
pre- recession scenario which is expected in the wake of extreme fluctuations in two data
sets.
5).For perfect zero working capital ZWC/sales should be 0 and (Debtors + Inventories)/
creditors should be 1. A close look at the values mentioned in the table above yield some
useful trends in the shift of the concept of Zero Working Capital in Indian Markets.
23. The mean value of ZWC/sales is reduced to about one-fourth in post-recession scenario as
that of pre-recession scenario. Also the values deviate about its mean values about 41.5% in
pre- recession while the window of fluctuations is narrowed down to 27.5% in post-recession
scenario. The range of variation of values is still very much the same. Similar trends are
depicted for (Debtors + Inventories)/Creditors. Mean value plums to 1.12 from 1.47 after
recession, deviating from mean position about 198% before recession and about 91% after
recession. The range of variation has also been reduced by one-third.
This concludes that firms have become more critical of their operating cycle costs. Due to the
exponential fall in debtors and simultaneously accelerated increase in creditors has forced
the firms to manage their operating cycle more efficiently. They are more inclined to covering
creditors from debtors and inventories alone and are more inclined to reduce their cash
conversion cycle in the wake of low liquidity.
REFERENCES
[1] Afza, T., &Nazir, M. (2009). Impact of aggressive working capital management policy
on firms' profitability. The IUP Journal of Applied Finance, 15(8), 20-30.
[2]AmarjitGill , 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.
[3] Deloof, M. (2003). Does working capital management affect profitability of Belgian firms
Journal of Business Finance & Accounting, 30(3-4), 573-588.
[4] Eljelly, A. M. (2004). Liquidity-profitability tradeoff: An Empirical Investigation in an
Emerging Market. International Journal of Commerce and Management, 14(2), 48-61.
[5] Filbeck, G., & Krueger, T. (2005). Industry related differences in working
capital management. Journal of Business, 20(2), 11-18.
[6] Garcia-Teruel, P. J., &Martínez-Solano, P. (2007). Effects of working capital management on
SME profitability. International Journal of Managerial Finance, 3(2), 164-177.
[7]Ghosh SK, Maji SG, 2003. Working capital management efficiency: a study on the
Indian cement industry. The Institute of Cost and Works Accountants of India.
[http://www.icwai.org/icwai/knowledgebank/fm47.pdf]
24. [8] Huynh Phuong Dong &Jyh-tay Su (2010). The Relationship between Working Capital
Management and Profitability: A Vietnam Case International Research Journal of Finance
and Economics ISSN 1450-2887 Issue 49 (2010).
[9] Keown, A. J., Martin, J. D., Petty, J. W., & Scott, D. (2003). Foundations of Finance,
4ed:Pearson Education, New Jersey
[10] Lamberson, M. (1995).Changes in working capital of small firms in relation to changes
in economic activity. Journal of Business, 10(2), 45-50.
[11] 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, 19(1), 26-35
[12] Raheman, A., & Nasr, M. (2007).Working capital management and profitability-case of
Pakistani firms. International Review of Business Research Papers 3(1), 279-300.
[13] Shin, H. H., &Soenen, L. (1998).Efficiency of working capital management and
corporate profitability. Financial Practice and Education, 8(2), 37-45.
[14] Singh, J. P., &Pandey, S. (2008).Impact of working Capital Management in the
Profitability of Hindalco Industries Limited. Icfai University Journal of Financial Economics,
6(4), 62-72.
[15] Smith. (1980). Profitability versus liquidity tradeoffs in working capital management, in
readings on the management of working capital. New York,St. Paul: West Publishing
Company.
[16] Van Horne, J. C., &Wachowicz, J. M. (2004). Fundamentals of Financial Management
(12 ed.). New york: Prentice Hall.
DARE TO MENTION ORIGINAL SOURCE ALSO….. (the one mentioned on 1st page)
