effect of working capital on profitability in indian markets and zero working capital

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 )

Profitability, Working CapitalManagement and Zero Working Capital

I N TR O D U C T I O N 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, thecurrent assets, called working capital, may be regarded as the lifeblood of a businessenterprise. 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.

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 affectthe 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 byminimizing the amount of investment tied up in current assets. Lamberson (1995) indicatedthat 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).

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.

L I T E R AT UR E R E V I E W 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 andliquidity 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 bycomputing 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 theirstudy, 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 createShareholder 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

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 sizeof 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.

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.

M E T H O D O L O GY

Data CollectionA database was built from a selection of approximately 30 financial-reports (for the purpose of thisresearch, 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 to2010. The selection was drawn from Bombay Stock Exchange [h tt p: // w w w . b se i n d i a . c o m/ a bou t / a b i n d i c es / b se 3 0 . as p ] on the basis of free float market capitalization method. The balance sheets of the companies were taken from the ‘Capitaline’ [ h tt p: // w w w . c a p i t a l i n e . c o m / n e w / i nd e x . as p ].

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 themarket for trading in the normal course. The major advantages of free- float methodology isthat 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 anindustry 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-

http://www.capitaline.com/new/index.asp

http://www.bseindia.com/about/abindices/bse30.asp

100, the underlying index to the famous Exchange Traded Fund (ETF) - QQQ is based on theFree-float Methodology.

VariablesThe 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, thatis 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)

Data Analysis- Post Recession (2008-2010)

Descriptive Statistics

G R O SS P R L O S CC C D R FF A R A R A P I N V M e a n 0.707068614 10.15297 7.690029 0.36495 0.379376 53.7279 198.708 116.2949S t a nd a r d d e v i a t i o n 0.772435624 0.89115 227.9693 0.240711 0.274829 44.94332 121.2027 216.7434M i n i m u m -0.8196472 8.485658 -502.96 0 0 9.515915 36.93339 0M a x i m u m 3.530351987 12.04457 780.8749 0.773568 1.129756 211.2627 535.7428 1080.013

Correlation Analysis- Post recession (2008-2010)

G R O S S P R L O S CC C DR FF A R A R A P I N V GROSSPR 1 0.200821 -0.14155 0.175411 -0.23543 -0.31465 -0.02084 -0.21747LOS 0.20082069 1 -0.14247 0.049786 -0.16878 -0.25041 -0.11312 -0.31955CCC -0.14155456 -0.14247 1 0.067349 0.192017 0.239722 -0.22239 0.738308DR 0.17541076 0.049786 0.067349 1 0.782423 -0.22441 0.253636 0.11557FFAR -0.23542542 -0.16878 0.192017 0.782423 1 0.013397 0.256972 0.22589AR -0.31464537 -0.25041 0.239722 -0.22441 0.013397 1 -0.00483 0.115348AP -0.0208431 -0.11312 -0.22239 0.253636 0.256972 -0.00483 1 0.365828INV -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.

SUMMARY OUTPUT

Regression StatisticsMultiple R 0.626R Square 0.392 Goodness of Fit < 0.80Adjusted R Square 0.270Standard Error 0.674Observations 25

ANOVA

df SS MS F P-valueRegression 4 5.842577 1.460644 3.219461 0.034Residual 20 9.073843 0.453692Total 24 14.91642 Confidence Level

0.95 0.99Coefficien 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 5.81744LOS -0.04523 0.161065 -0.28085 0.782 -0.38121 0.290741 -0.50352 0.41305CCC -8.3E-05 0.00061 -0.13641 0.893 -0.00135 0.001188 -0.00182 0.001651DR 3.037306 0.946388 3.209368 0.004 1.063176 5.011436 0.344512 5.7301FFAR -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.

SUMMARY OUTPUT


ANOVA


0.95 0.99Coefficient 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 0.398552DR 2.8454771 0.986304 2.88499 0.009 0.788083 4.902871 0.039107 5.651847FFAR -2.639849 0.854454 -3.08951 0.006 -4.42221 -0.85749 -5.07106 -0.20864AR -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.

SUMMARY OUTPUT

Regression StatisticsMultiple R 0.626R Square 0.391 Goodness of Fit < 0.80Adjusted 0.270Standard 0.674Observati 25

ANOVA

df SS MS F P-valueRegressio 4 5.837249 1.459312 3.214638 0.034Residual 20 9.07917 0.453959Total 24 14.91642 Confidence Level


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 0.41461DR 3.060157 0.947418 3.229997 0.004 1.083878 5.036436 0.364431 5.755883FFAR -2.77247 0.836692 -3.31361 0.003 -4.51778 -1.02716 -5.15314 -0.3918Ap -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.

SUMMARY OUTPUT


ANOVA



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 0.391362DR 3.040488 0.92848 3.274695 0.004 1.103713 4.977262 0.398648 5.682327FFAR -2.69985 0.830232 -3.25192 0.004 -4.43169 -0.96802 -5.06214 -0.33756INV 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

G R O SS P R L O S CC C D R FF A R A R A P I N V m e a n 0.788228544 56.12815 198.2344 121.5334 -20.5729 0.339925 9.4331 0.341595m a x i m u m 2.592902249 177.3841 636.4138 1168.658 1188.669 0.738432 11.46191 1.08909Standard Deviation 0.507572269 43.31043 140.3418 236.4202 300.8036 0.241264 0.959618 0.268632V a r i a n c e 0.257629608 1875.794 19695.82 55894.51 90482.83 0.058208 0.920867 0.072163

Correlation Analysis- Pre recession (2005-2007)

G R O S S P R L O S CC C DR FF A R A R A P I N V GROSSPR 1 -0.43468 0.121837 -0.05941 -0.16613 -0.18915 0.198846 -0.28271AR -0.4346759 1 0.074751 0.553503 0.544139 0.217313 -0.67604 0.305006AP 0.12183696 0.074751 1 -0.03903 -0.48647 0.305506 -0.07537 0.264846INV -0.059413 0.553503 -0.03903 1 0.883868 0.415302 -0.50869 0.394123CCC -0.1661258 0.544139 -0.48647 0.883868 1 0.215165 -0.46199 0.230116DR -0.1891457 0.217313 0.305506 0.415302 0.215165 1 -0.30876 0.829285LOS 0.19884624 -0.67604 -0.07537 -0.50869 -0.46199 -0.30876 1 -0.42621FFAR -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.

SUMMARY OUTPUT

Regression StatisticsMultiple R 0.315R Square 0.100 Goodness of Fit < 0.80Adjusted -0.101Standard 0.532Observati 23

ANOVA



Intercept 0.682018 1.425708 0.478371 0.638 -2.31328 3.67732 -3.4218 4.785834CCC -0.00016 0.000428 -0.36515 0.719 -0.00105 0.000742 -0.00139 0.001075DR 0.306326 0.848756 0.360911 0.722 -1.47684 2.089495 -2.13677 2.749418LOS 0.024669 0.144624 0.170572 0.866 -0.27917 0.328513 -0.39162 0.44096FFAR -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.

SUMMARY OUTPUT

Regression StatisticsMultiple R 0.504R Square 0.254 Goodness of Fit < 0.80Adjusted 0.088Standard 0.485Observati 23

ANOVA



Intercept 2.643511 1.624431 1.627346 0.121 -0.76929 6.056313 -2.03232 7.319338AR -0.00638 0.00324 -1.96963 0.064 -0.01319 0.000425 -0.01571 0.002944DR 0.257103 0.769654 0.33405 0.742 -1.35988 1.874085 -1.9583 2.472505LOS -0.14506 0.154195 -0.94076 0.359 -0.46901 0.178892 -0.5889 0.298782FFAR -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.

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 StatisticsMultiple R 0.360R Square 0.129 Goodness of Fit < 0.80Adjusted -0.064Standard 0.524Observati 23

ANOVA



Intercept 0.413807 1.295302 0.319467 0.753 -2.30752 3.135135 -3.31464 4.142256AP 0.000727 0.000836 0.869819 0.396 -0.00103 0.002483 -0.00168 0.003133DR 0.164132 0.841521 0.195042 0.848 -1.60384 1.932101 -2.25814 2.586399LOS 0.043513 0.129147 0.33693 0.740 -0.22781 0.314841 -0.32823 0.415255FFAR -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%showingsignificant 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.

SUMMARY OUTPUT

Regression StatisticsMultiple R 0.317R Square 0.100 Goodness of Fit < 0.80Adjusted R Square -0.100Standard Error 0.532Observations 23

ANOVA



Intercept 0.244345 1.450171 0.168494 0.868 -2.80235 3.291041 -3.92989 4.418575INV 0.000227 0.000588 0.386211 0.704 -0.00101 0.001463 -0.00147 0.00192DR 0.20162 0.868131 0.232246 0.819 -1.62226 2.025495 -2.29724 2.700483LOS 0.071176 0.145603 0.488833 0.631 -0.23473 0.377076 -0.34793 0.490286FFAR -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*FFARThe 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.

F I N D I N GS

1).Comparison of Models in Pre and Post-Recession Scenario and their accuracyPo 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-RecessionR squared Adjusted R squared R squared Adjusted R squared

0.3920.4030.3910.408

0.270.284

0.270.289

0.10.2540.129

0.1

-0.1010.088

-0.064-0.1

2). Zero Working CapitalPost-recession Pre-Recession

Name of Company ZWC/SALES (DEBT+INV)/CR ZWC/SALES (DEBT+INV)/CRBajaj Auto Limited -0.14760968 0.305006917 NA NABharat Heavy Electrica 0.42632056 2.005056274 NA NABharti Airtel Ltd. -0.53057015 0.14011567 -0.513717141 0.350060673Cipla Ltd. 0.32091196 2.112357862 0.394976749 3.280783186DLF Ltd. 0.8628121 3.476930292 1.733576758 9.929247501Hindalco Industries Lt 0.17331781 2.84926696 0.039387942 1.125764298Hindustan Unilever Lt -0.21374335 0.356095324 -0.226908756 0.414290102Infosys Technologies 0.11309241 2.947323944 0.100052383 2.322943723ITC Ltd. -0.03208585 0.876324645 -0.030100031 0.883652606Jaiprakash Associates 0.32073783 1.818343488 0.199475522 1.793472369Jindal Steel & Power L -0.14302859 0.559426555 -0.231194816 0.539112119Larsen & Toubro Limit -0.15319893 0.754910487 -0.023156809 0.952462584Mahindra & Mahindra -0.07170058 0.778542677 -0.071739129 0.790302373Maruti Suzuki India Lt -0.10113661 0.41384391 0.005880034 1.066012686NTPC Ltd. -0.05024592 0.785914843 -0.067983523 0.646880958ONGC Ltd. -0.08407589 0.626334121 -0.037732679 0.777293528Reliance Communicat -0.3068965 0.350920962 -0.351683776 0.260418519Reliance Industries Lt -0.18848518 0.495807458 -0.155707724 0.488099774Reliance Infrastructur -0.13243581 0.604715984 -0.122591396 0.750957986Sterlite Industries (In -0.01913997 0.891083629 0.069588681 1.548428041Tata Consultancy Serv 0.03870822 1.217031577 0.089716164 1.620752145Tata Motors Ltd. -0.26378145 0.417184841 -0.105438414 0.56767143Tata Power Company -0.0017847 0.995389468 0.02260914 1.070131198Tata Steel Ltd. -0.07523184 0.785178178 -0.135814713 0.594773188Wipro Ltd. 0.08404132 1.600027293 0.110405943 2.038582934

Mean -0.00700835 1.126525334 0.030082627 1.47009104Standard deviation 0.27567901 0.91253253 0.415137223 1.982359862Maximum 1.72562419 6.953860584 1.733576758 9.929247501Minimum -0.53057015 0.14011567 -0.513717141 0.260418519Range 2.25619435 6.813744914 2.247293899 9.668828982

3).Comparison and differences of variables in Pre & Post Recession Scenario

(Note- the data in red corresponds to post recession, data in green is for pre recession and blue is their respective differences)

Name g ro ssp r g ro ssp r DIFF DR DR DIFF AP A P DIFF AR A R DIFF INV INV DIFF CCC CCC DIFF

B hart i A irt el 0 .6 4 2 3 0 .8 8 1 -0 .2 4 0 .2 58 0 .3 8 8 -0 .13 53 5.7 6 3 6 .4 -10 1 3 0 .6 7 9 9 .0 8 -6 8 .4 2 .117 4 .2 2 2 -2 .11 -50 3 -53 3 3 0 .15

Cip la Lt d . 0 .9 0 3 9 0 .3 9 2 0 .512 0 .0 9 6 0 .0 3 7 0 .0 59 16 2 .2 9 8 .56 6 3 .6 9 12 1.2 10 6 .3 14 .9 1 156 157.6 -1.6 4 114 .9 16 5.4 -50 .4

DLF Lt d . 0 .2 73 8 0 .52 -0 .2 5 0 .3 9 8 0 .73 8 -0 .3 4 3 6 5.1 157.4 2 0 7.7 6 5.9 4 177.4 -111 10 8 0 116 9 -8 8 .6 78 0 .9 118 9 -4 0 8

Hind alco Ind 0 .6 3 6 1.18 2 -0 .55 0 .58 8 0 .6 4 -0 .0 5 4 2 .6 5 16 1.7 -119 3 8 .2 1 3 0 .2 8 7.9 3 3 73 .8 7 13 9 .2 -6 5.4 6 9 .4 4 7.778 6 1.6 6

Hind ust an U 3 .53 0 4 2 .59 3 0 .9 3 7 0 .6 4 2 0 .0 4 7 0 .59 4 2 0 3 .8 2 3 6 .7 -3 2 .9 11.3 3 13 .6 8 -2 .3 6 53 .51 75.15 -2 1.6 -13 9 -14 8 8 .9 15

Inf o sys Tec 0 .3 8 71 0 .576 -0 .19 0 0 0 3 6 .9 3 4 9 .77 -12 .8 6 2 .4 8 6 4 .12 -1.6 5 0 0 0 2 5.54 14 .3 5 11.19

ITC Lt d . 1.178 6 1.2 4 2 -0 .0 6 0 .0 13 0 .0 2 1 -0 .0 1 2 72 .6 3 0 0 .6 -2 7.9 13 .4 4 14 .55 -1.1 2 0 0 .2 2 19 .3 -19 .1 -59 -6 6 .7 7.75

Jaip rakash A -0 .8 2 0 .718 -1.54 0 .74 7 0 .72 1 0 .0 2 6 3 3 3 .4 18 3 .7 14 9 .7 6 5.6 6 6 1.6 5 4 .0 1 4 53 .2 206 2 4 7.1 18 5.5 8 3 .9 9 10 1.5

Jind al St eel 0 .8 2 0 5 0 .8 3 2 -0 .0 1 0 .52 5 0 .6 6 3 -0 .14 2 9 4 .8 4 0 3 .9 -10 9 2 0 .8 5 3 3 .3 9 -12 .5 113 14 4 .1 -3 1 -16 1 -2 2 6 6 5.4 9

Larsen & To 0 .54 1 0 .6 6 8 -0 .13 0 .54 0 .4 55 0 .0 8 5 2 9 6 .6 2 2 3 .5 73 .0 3 10 2 .7 10 9 .4 -6 .72 9 0 .4 75.3 5 15.0 5 -10 3 -3 8 .8 -6 4 .7

M ahind ra & 0 .74 9 9 0 .8 8 7 -0 .14 0 .54 2 0 .56 -0 .0 2 16 5.8 178 .5 -12 .7 4 7.4 1 50 .74 -3 .3 3 6 2 .56 6 8 .55 -5.9 9 -55.8 -59 .2 3 .4 19

M arut i Suzu 0 .719 2 0 .9 0 9 -0 .19 0 .0 79 0 .0 8 9 -0 .0 1 8 7.8 8 4 9 .2 2 3 8 .6 6 10 .9 3 16 .4 7 -5.55 2 1.12 2 7.53 -6 .4 1 -55.8 -5.2 2 -50 .6

NTPC Lt d . 0 .2 0 3 2 0 .19 5 0 .0 0 9 0 .3 9 5 0 .3 3 0 .0 6 6 117.8 10 1.7 16 .0 6 3 9 .73 16 .3 2 3 .4 3 3 7.9 4 4 2 .2 -4 .2 6 -4 0 .1 -4 3 .2 3 .10 5

ONGC Lt d . 0 .6 74 0 .8 4 8 -0 .17 0 .18 2 0 .2 0 2 -0 .0 2 19 8 .3 18 3 .7 14 .56 2 4 .57 2 2 .8 6 1.711 6 4 .8 6 74 .8 9 -10 -10 9 -8 6 -2 2 .9

Reliance Co 0 .6 16 4 0 .4 74 0 .14 3 0 .58 1 0 .4 3 2 0 .14 9 4 4 1.3 4 14 .6 2 6 .75 52 .6 9 3 5.78 16 .9 1 2 0 .13 2 2 .5 -2 .3 7 -3 6 9 -3 56 -12 .2

Reliance Ind 0 .2 8 3 7 0 .50 2 -0 .2 2 0 .3 52 0 .3 16 0 .0 3 6 16 9 .7 156 .2 13 .52 15.0 1 15.51 -0 .4 9 6 5.4 7 54 .4 2 11.0 5 -8 9 .2 -8 6 .3 -2 .9 6

Reliance Inf r 0 .10 3 1 0 .2 11 -0 .11 0 .3 12 0 .4 11 -0 .1 14 5.2 3 13 .2 -16 8 59 .9 6 10 5.4 -4 5.4 16 .6 1 51.56 -3 5 -6 8 .6 -156 8 7.71

St erlit e Ind u 0 .2 6 0 2 0 .73 8 -0 .4 8 0 .16 8 0 .3 59 -0 .19 10 1.1 78 .4 5 2 2 .6 8 14 .59 2 8 .0 4 -13 .4 6 7.1 73 .9 8 -6 .8 8 153 .6 2 3 .57 13 0 .1

Tat a Co nsul 0 .6 3 0 4 0 .8 4 1 -0 .2 1 0 .0 2 3 0 .0 4 2 -0 .0 2 10 3 .4 8 4 .9 3 18 .51 78 .79 8 4 .15 -5.3 6 0 .6 9 8 2 .172 -1.4 7 2 5.3 6 1.3 8 7 2 3 .9 7

Tat a M o t o r 2 .0 774 1.3 14 0 .76 3 0 .774 0 .4 17 0 .3 57 2 16 .5 12 4 .5 9 2 .0 5 2 4 .4 4 18 .15 6 .2 9 9 58 .2 9 4 5.2 8 13 2 50 .4 -6 1 3 11.4

Tat a Po wer -0 .0 9 0 .0 9 1 -0 .18 0 .558 0 .4 3 7 0 .12 2 12 9 .2 13 1.3 -2 .11 110 .3 9 4 .2 2 16 .11 2 7.72 3 5.3 8 -7.6 6 4 6 .59 -1.71 4 8 .3

Tat a St eel L 1.0 8 7 0 .9 59 0 .12 8 0 .6 56 0 .4 9 0 .16 6 16 9 .2 231 -6 1.8 4 0 .75 23 17.75 78 .9 1 9 3 .9 6 -15 2 0 7.4 -114 3 2 1.4

Wip ro Lt d . 0 .53 2 0 .557 -0 .0 2 0 .2 73 0 .0 2 4 0 .2 5 74 .8 5 59 .8 7 14 .9 8 70 .75 70 .56 0 .18 6 16 .18 13 .17 3 .0 0 6 2 0 .2 9 2 3 .8 7 -3 .58

M e a n 0 . 6 9 0 . 7 9 - 0 . 1 0 . 3 8 0 . 3 4 0 . 0 4 2 0 3 19 8 4 . 5 5 4 8 . 8 5 6 . 1 - 7 . 3 12 0 12 2 - 1. 5 5 . 5 5 - 2 1 2 6 . 1

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-recessionscenario 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.

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 from0.391 to 0.129 and adjusted R-squared from 0.270 to -0.064. From the exceptionally low valuesof 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 asthe 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 from0.27 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.

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.

R E F E R E N CE S

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[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 firmsJournal of Business Finance & Accounting, 30(3-4), 573-588.

[4] Eljelly, A. M. (2004). Liquidity-profitability tradeoff: An Empirical Investigation in anEmerging 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.

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DARE TO MENTION ORIGINAL SOURCE ALSO….. (the one mentioned on 1st page)

effect of working capital on profitability in indian markets and zero working capital

Documents

management of working

management of current

fixed assets

firms capital

effect of working capital

fixed capital

current assets account

total assets