Effect of working capital on profitability in indian markets and concept of zero working capital

  • 5,031 views
Uploaded on

 

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
No Downloads

Views

Total Views
5,031
On Slideshare
0
From Embeds
0
Number of Embeds
0

Actions

Shares
Downloads
195
Comments
0
Likes
1

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 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 CapitalManagement and Zero Working CapitalINTRODUCTIONThis paper investigates the relationship between the working capital management and thefirms’ profitability for a sample of top 30 Indian companies listed on the Bombay StockExchange for the period of 6 years from 2005-2010. Management of working capital is animportant component of corporate financial management because it directly affects theprofitability of the firms. Management of working capital refers to management of currentassets and of current liabilities.Assets in commercial firm consist of two kinds: fixed assets and current assets. Fixed assetsinclude land, building, plant, furniture, etc. Investment in these assets represents that of partof firm’s capital, which is permanently blocked on a permanent or fixed basis and is also calledfixed capital that generates productive capacity. The form of these assets does not change, inthe 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 thepurpose of production and sales, like raw material into semi-finished products, semi- finishedproducts into finished products, finished products into debtors and debtors turned over cashor bills receivables. The fixed assets are used in increasing production of an organization andthe 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 somestudied the impact of proper or optimal inventory management, others studied themanagement of accounts receivables trying to postulate an optimal way policy that leads toprofit maximization (Lazaridis and Tryfonidis, 2006). According to Deloof (2003), the waythat working capital is managed has a significant impact on profitability of firms. Such resultsindicate that there is a certain level of working capital requirement, which potentiallymaximizes returns.Working capital management plays an important role in a firm’s profitability and risk as well asits value (Smith, 1980). There are a lot of reasons for the importance of working capitalmanagement. For a typical manufacturing firm, the current assets account for over half of itstotal assets. For a distribution company, they account for even more. Excessive levels ofcurrent 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 assetsmay have a negative effect of a firm’s profitability, whereas a low level of current assets maylead to lowers of liquidity and stock-outs, resulting in difficulties in maintaining smoothoperations.
  • 3. Efficient management of working capital plays an important role of overall corporate strategyin order to create shareholder value. Working capital is regarded as the result of the time lagbetween the expenditure for the purchase of raw material and the collection for the sale of thefinished good. The way of working capital management can have a significant impact on boththe liquidity and profitability of the company (Shin and Soenen, 1998). The main purpose of anyfirm 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 thefirm. 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 atcost of the other. If we ignore about profit, we cannot survive for a longer period. Conversely, ifwe do not care about liquidity, we may face the problem of insolvency. For these reasonsworking 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 currentliabilities in a manner that eliminates the risk of inability to meet due short term obligations onthe 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 mostimportant issues in organization, where many financial managers are finding it difficult toidentify the important drivers of working capital and the optimum level of working capital. As aresult, companies can minimize risk and improve their overall performance if they canunderstand the role and determinants of working capital. A firm may choose an aggressiveworking capital management policy with a low level of current assets as percentage of totalassets, or it may also be used for the financing decisions of the firm in the form of high level ofcurrent liabilities as percentage of total liabilities (Afza and Nazir, 2009).Keeping an optimalbalance among each of the working capital components is the main objective of workingcapital management. Business success heavily depends on the ability of the financial managersto effectively manage receivables, inventory, and payables (Filbeck and Krueger, 2005). Firmscan 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 optimallevel of working capital is a balance between risk and efficiency. It asks continuous monitoringto maintain the optimum level of various components of working capital, such as cashreceivables, inventory and payables (Afza and Nazir, 2009). A popular measure of workingcapital management is the cash conversion cycle, which is defined as the sum of days of salesoutstanding (average collection period) and days of sales in inventory less days of payablesoutstanding (Keown et al, 2003). The longer this time lag, the larger the investment in workingcapital. A longer cash conversion cycle might increase profitability because it leads to highersales. However, corporate profitability might also decrease with the cash conversion cycle, ifthe costs of higher investment in working capital is higher and rises faster than the benefits ofholding 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 mayimpact to success or failure of firm in business because working capital management affect tothe profitability of the firm. The thesis is expected to contribute to better understanding ofrelationship between working capital management and profitability in order to help managerstake a lot of solutions to create value for their shareholders, especially in emerging marketslike India.LITERATURE REVIEWThe management of working capital is defined as the “management of current assets andcurrent liabilities, and financing these current assets.” Working capital management isimportant 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 goodfirms to extend trade credit so that buyers can verify product quality before payment. Theirsample contained all industrial (SIC 2000 through 3999) firms with data available fromCOMPUSTAT for the three-year period ending in 1987 and used regression analysis. Theydefined trade credit policy as the average time receivables are outstanding and measured thisvariable bycomputing each firms days of sales outstanding (DSO), as accounts receivable per dollar ofdaily sales. To reduce variability, they averaged DSO and all other measures over a three yearperiod. They found evidence consistent with the model. The findings suggest that producersmay increase the implicit cost of extending trade credit by financing their receivablesthrough payables and short-term borrowing.Shin and Soenen(1998) researched the relationship between working capital managementand value creation for shareholders. The standard measure for working capital management isthe cash conversion cycle (CCC). Cash conversion period reflects the time span betweendisbursement and collection of cash. It is measured by estimating the inventory conversionperiod 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 capitalmanagement. NTC is basically equal to the cash conversion cycle (CCC) where all threecomponents are expressed as a percentage of sales. NTC may be a proxy for additionalworking capital needs as a function of the projected sales growth. They examined thisrelationship by using correlation and regression analysis, by industry, and working capitalintensity. 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 firms net-trade cycle andits 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 1992-1996periods. The result from analysis showed that there was a negative between profitabilitythat was measured by gross operating income and cash conversion cycle as well number ofday’s
  • 5. accounts receivable and inventories. He suggested that managers can increase corporateprofitability by reducing the number of day’s accounts receivable and inventories. Lessprofitable firms waited longer to pay their bills.Ghosh and Maji (2003) attempted to examine the efficiency of working capital management ofIndian 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 ofworking capital management, instead of using some common working capital managementratios. By using regression analysis and industry norms as a target efficiency level of individualfirms, Ghosh and Maji (2003) tested the speed of achieving that target level of efficiency byindividual firms during the period of study and found that some of the sample firmssuccessfully improved efficiency during these years.Singh and Pandey (2008) had an attempt to study the working capital components and theimpact of working capital management on profitability of Hindalco Industries Limited forperiod 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 significantimpact on the profitability of Hindalco Industries Limited.Lazaridis and Tryfonidis (2006) conducted a cross sectional study by using a sample of 131firms listed on the Athens Stock Exchange for the period of 2001 - 2004 and found statisticallysignificant relationship between profitability, measured through gross operating profit, and thecash conversion cycle and its components (accounts receivables, accounts payables, andinventory). Based on the results analysis of annual data by using correlation and regressiontests, they suggest that managers can create profits for their companies by correctly handlingthe cash conversion cycle and by keeping each component of the conversion cycle (accountsreceivables, accounts payables, and inventory) at an optimal level.Raheman and Nasr (2007) have selected a sample of 94 Pakistani firms listed on Karachi StockExchange for a period of 6 years from 1999-2004 to study the effect of different variables ofworking capital management on the net operating profitability. From result of study, theyshowed that there was a negative relationship between variables of working capitalmanagement including the average collection period, inventory turnover in days, averagecollection period, cash conversion cycle and profitability. Besides, they also indicated thatsizeof 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 theperiod 1996 -2005. Their study utilized panel data econometrics in a pooled regression, wheretime-series and cross-sectional observations were combined and estimated. They found asignificant negative relationship between net operating profitability and the average collectionperiod, inventory turnover in days, average payment period and cash conversion cycle for asample of fifty Nigerian firms listed on the Nigerian Stock Exchange. Furthermore, they foundno significant variations in the effects of working capital management between large andsmall firms.
  • 6. Finally, Afza and Nazir (2009) made an attempt in order to investigate the traditionalrelationship between working capital management policies and a firm’s profitability for asample of 204 non-financial firms listed on Karachi Stock Exchange (KSE) for the period1998-2005.The study found significant different among their working capital requirements andfinancing policies across different industries. Moreover, regression result found a negativerelationship between the profitability of firms and degree of aggressiveness of workingcapital investment and financing policies. They suggested that managers could crease value ifthey adopt a conservative approach towards working capital investment and working capitalfinancing policies.METHODOLOGYData 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 yearsfrom 2005 to2010. 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 ofthe 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 observationsfor 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 theresearch 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-floatmarket capitalization takes into consideration only those shares issued by the company thatare 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 thoseshares that are available for trading in the market. It makes the index more broad-based byreducing the concentration of top few companies in Index and aids both active and passiveinvesting styles. Globally, the free-float Methodology of index construction is considered to beanindustry best practice and all major index providers like MSCI, FTSE, S&P and STOXX haveadopted the same. The MSCI India Standard Index, which is followed by Foreign InstitutionalInvestors (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 theFree-float Methodology.VariablesThe variables used in this study based on previous researches about the relationshipbetween working capital management and profitability.Gross operating profitability that is a measure of profitability of firm is used as dependentvariable. It is defined as sales minus cost of goods sold, and divided by total assets minusfinancial assets. For a number of firms in the sample, financial assets, which are chiefly sharesin affiliated firms, are a significant part of total assets. When the financial assets are main partof 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 ofdays accounts receivable used as proxy for the collection policy is an independent variable. Itis calculated as (accounts receivable x 365)/sales. Number of days inventories used as proxyfor the inventory policy is an independent variable. It is calculated as (inventories x 365)/ costof goods sold. Number of days accounts payable used as proxy for the payment policy is anindependent 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 isanother 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 workingcapital in order to have an opposite analysis of working capital management on the firm’sprofitability (Deloof, 2003; Lazaridis and Tryfonidis, 2006). The logarithm of sales used tomeasure 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 totalassets are also control variable in the regressions. According to Deloof (2003) fixed financialassets are mainly shares in affiliated firms, intended to contribute to the activities of the firmthat holds them, by establishing a lasting and specific relation and loans that were grantedwith 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 VMean 0.707068614 10.15297 7.690029 0.36495 0.379376 53.7279 198.708 116.2949Standard deviation 0.772435624 0.89115 227.9693 0.240711 0.274829 44.94332 121.2027 216.7434Minimum -0.8196472 8.485658 -502.96 0 0 9.515915 36.93339 0Maximum 3.530351987 12.04457 780.8749 0.773568 1.129756 211.2627 535.7428 1080.013Correlation Analysis- Post recession (2008-2010) GR O S S P R L O S CCC DR FFA R AR AP IN VGROSSPR 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 1Multiple Regression Analysis (2008-2010)Model 1GROSSPR is used a dependent variable.Cash Conversion Cycle is used as an independentvariable while Debt Ratio (DR) , Natural Logarithm of sales (LOS), Fixed Financial AssetsRatio(FFAR) are used as control variables.The cash conversion cycle is used popular to measure efficiency of working capitalmanagement. From result of regression running indicates that there is a negative relationshipbetween 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 thecash conversion cycle does not significantly affect profitability of the firm with such a lowcoefficient. The adjusted R-squaredis 27% showing significant fitting of the model in post-recession scenario.
  • 9. SUMMARY OUTPUTRegression StatisticsMultiple R 0.626R Square 0.392 Goodness of Fit < 0.80Adjusted R Square 0.270Standard Error 0.674Observations 25ANOVA 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 0.99 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 5.81744LOS -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 -0.33543GROSSPR = 1.104 -0.045*LOS-(8.3E-05)*CCC +3.037*DR -2.755*FFARModel 2GROSSPR is used a dependent variable. Number of days accounts receivable (AR) is used as anindependent variable while Debt Ratio (DR) , Natural Logarithm of sales (LOS), Fixed FinancialAssets 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 increaseor decrease in accounts receivable will significantly affect profitability of firm. Debt ratio isused as a proxy for leverage, from analysis of regression shows that there is a positiverelationship 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 offirm. 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 -0.633respectively. Both of them aresignificant at α = 0.01. It implies that the size of firm has effecton profitability of firm. The larger size leads to more profitable. The adjusted Rsquared, alsocalled the coefficient of multiple determinations, is the percent of the variance in thedependent explained uniquely or jointly by the independent variables and is 28.4% showingpredicted model is highly accurate.
  • 10. SUMMARY OUTPUTRegression StatisticsMultiple R 0.635R Square 0.403 Goodness of Fit < 0.80Adjusted R Square 0.284Standard Error 0.667Observations 25ANOVA df SS MS F P-valueRegression 4 6.014731 1.503683 3.378421 0.029Residual 20 8.901689 0.445084Total 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 6.235035ln(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 0.00717GROSSPR = 1.388 -0.06*LOS +2.845*DR -2.64*FFAR -0.002*ARModel 3The dependent variable gross operating profit and the control variables are the same as theprevious models. The only difference is number of days accounts receivable variablereplaced by number of days accounts payable variable.Looking at coefficients, we see that there is a negative relationship between number of daysaccounts payable and profitability of firm. The coefficient is 0.001. It implies that the increaseor decrease in the average payment period significantly affects profitability of the firm. Thenegative relationship between the average payment period and profitability indicates that themore profitable firms wait shorter to pay their bill.The adjusted R2 is 27.0%showing significantfitting of the model in post-recession scenario.
  • 11. SUMMARY OUTPUTRegression StatisticsMultiple R 0.626R Square 0.391 Goodness of Fit < 0.80Adjusted 0.270Standard 0.674Observati 25ANOVA 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 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 5.922993ln(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 0.003208GROSSPR = 1.117 -0.045*LOS +3.06*DR -2.772*FFAR -(9.6E-05)*ApModel 4This model is run using the number of days inventories as an independent variable as substituteof average payment period. The other variables are same as they have been in first and secondmodel.The result of regression indicates that the relationship between number of days inventoriesand 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 affectprofitability.The adjusted R2 is 28.9% demonstrating the desirable superposition ofpredicted and actual values.
  • 12. SUMMARY OUTPUTRegression StatisticsMultiple R 0.639R Square 0.408 Goodness of Fit < 0.80Adjusted R Square 0.289Standard Error 0.665Observations 25ANOVA df SS MS F P-valueRegression 4 6.082874 1.520718 3.443053 0.027Residual 20 8.833546 0.441677Total 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 6.329054ln(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 0.00138GROSSPR= 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 Vm e an 0.788228544 56.12815 198.2344 121.5334 -20.5729 0.339925 9.4331 0.341595maximum 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 0.268632Variance 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 VGROSSPR 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 1Multiple Regression Analysis (2005-2007)Model 1GROSSPR is used a dependent variable.Cash Conversion Cycle is used as an independentvariable while Debt Ratio (DR) , Natural Logarithm of sales (LOS), Fixed Financial AssetsRatio(FFAR) are used as control variables.
  • 14. SUMMARY OUTPUTRegression StatisticsMultiple R 0.315R Square 0.100 Goodness of Fit < 0.80Adjusted -0.101Standard 0.532Observati 23ANOVA df SS MS F P-valueRegressio 4 0.564121 0.14103 0.49739 0.738Residual 18 5.10373 0.283541Total 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 4.785834CCC -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 1.61592GROSSPR = 0.682-0.000156151*CCC +0.306*DR +0.025*ln(sale) -0.685*FFARThe cash conversion cycle is used popular to measure efficiency of working capitalmanagement. From result of regression running indicates that there is a negative relationshipbetween cash conversion cycle and operating profitability. The coefficient is -0.000156151with p-value 0.000. It is highly significant at α= 0.01. This implies that the increase or decreasein the cash conversion cycle does not significantly affects profitability of the firm with such alow coefficient. The adjusted R-squaredis -10.1% showing significant non-fitting of the model inpre- recession scenario.Model 2GROSSPR is used a dependent variable. Accounts Receivable Periodis used as an independentvariable while Debt Ratio (DR), Natural Logarithm of sales (LOS), Fixed Financial AssetsRatio(FFAR) are used as control variables.
  • 15. SUMMARY OUTPUTRegression StatisticsMultiple R 0.504R Square 0.254 Goodness of Fit < 0.80Adjusted 0.088Standard 0.485Observati 23ANOVA df SS MS F P-valueRegressio 4 1.437961 0.35949 1.529785 0.236Residual 18 4.22989 0.234994Total 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 7.319338AR -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 1.459797GROSSPR = 2.644 -0.006*AR +0.257*DR -0.145*LOS -0.633*FFARThe 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 increaseor decrease in accounts receivable will significantly affect profitability of firm. Debt ratio isused as a proxy for leverage, from analysis of regression shows that there is apositiverelationship 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 offirm. 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 -0.633respectively. Both of them aresignificant at α = 0.01. It implies that the size of firm has effecton profitability of firm. The larger size leads to more profitable. The adjusted Rsquared, alsocalled the coefficient of multiple determinations, is the percent of thevariance in thedependent explained uniquely or jointly by the independent variables and is 8.8% showingsignificant non-fitting of the model in pre- recession scenario.
  • 16. Model 3The dependent variable gross operating profit and the control variables are the same as theprevious models. The only difference is number of days accountsreceivable variable replacedby number of days accounts payable variable.Regression StatisticsMultiple R 0.360R Square 0.129 Goodness of Fit < 0.80Adjusted -0.064Standard 0.524Observati 23ANOVA df SS MS F P-valueRegressio 4 0.733709 0.183427 0.669151 0.622Residual 18 4.934143 0.274119Total 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 4.142256AP 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 1.569481GROSSPR= 0.414 +0.001*AP +0.164*DR +0.044*LOS -0.691*FFARLooking at coefficients, we see that there is a positive relationship between number of daysaccounts payable and profitability of firm. The coefficient is 0.001. It implies that the increaseor decrease in the average payment period significantly affects profitability of the firm. Thepositive relationship between the average paymentperiod and profitability indicates that themore 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 4This model is run using the number of days inventories as an independent variable assubstitute of average payment period. The other variables are same as they have been in firstandsecond model.
  • 17. SUMMARY OUTPUTRegression StatisticsMultiple R 0.317R Square 0.100 Goodness of Fit < 0.80Adjusted R Square -0.100Standard Error 0.532Observations 23ANOVA df SS MS F P-valueRegression 4 0.56857 0.142143 0.50175 0.735Residual 18 5.099281 0.283293Total 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 4.418575INV 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 1.644894GROSSPR = 0.244 +0*INV +0.202*DR +0.071*LOS -0.655*FFARThe result of regression indicates that the relationship between number of days inventoriesand 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 affectprofitability.The adjusted R2 is -10.0% demonstrating the poor mismatch of predicted andactual values.
  • 18. FINDINGS1).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*FFARy = 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*FFARy = 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*FFARy= 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*FFARy=GROSSPR y=GROSSPRPost-Recession Post-Recession Pre-Recession Pre-RecessionR 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-RecessionName 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 2.038582934Mean -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
  • 20. 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 andblue is their respective differences)Name g ro ssp r g ro ssp r DIFF DR DR DIFF AP AP DIFF AR AR DIFF INV INV DIFF CCC CCC DIFFB 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 .15Cip 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 .4DLF 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 8Hind 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 6Hind 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 15Inf 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 11.19ITC 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 7.75Jaip 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 1.5Jind 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 9Larsen & 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 .7M 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 19M 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 .6NTPC 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 5ONGC 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 .9Reliance 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 .2Reliance 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 6Reliance 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 7.71St 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 .1Tat 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 7Tat 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 11.4Tat 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 .3Tat 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 1.4Wip 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 .58M ean 0 .69 0 .79 -0.1 0 .38 0 .34 0 .04 203 19 8 4 .55 48 .8 56 .1 -7.3 12 0 12 2 - 1. 5 5.55 -21 2 6.1
  • 21. Conclusions-Let us first of all try to compare the models derived using multiple regressions and checktheir 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 inthe coefficient of CCC from a very low negative value in post-recession to a higher absolutevalue in pre-recession model. This clearly demonstrates the impact on sales after recession andcash conversion cycle. Ideally speaking, the coefficient of LOS should have been positive andGROSSPR must increase with increase of sales (LOS). This is truly encountered before 2008 asthe coefficient is positive. But after recession the coefficient of LOS is negative clearlydemonstrating abrupt changes in market due to unexplained forcing factors in times ofrecession. 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 thecash conversion cycle increases, it will lead to declining of profitability of firm. Therefore, themanagers can create a positive value for the shareholders by handling the adequate cashconversion cycle and keeping each different component to an optimum level.The most striking comparison is yielded by the R-squared values and adjusted R-squaredvalues. 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 thatof post and simultaneously the gross profitability now decreases almost 4 times rapidly inpost- recession as compared to pre-recession scenario. The sign of coefficient of LOS isinversed to ideal behavior that is, negative. The coefficient of AR is ideal negative and is 3times in pre- recession than post-recession. This means as accounts receivables periodincreases the gross profitability decreases three times faster before recession as comparedto post-period. The most striking comparison is yielded by the R-squared values andadjusted R-squared values.The R-squared value changes from 0.403 to 0.254 and adjusted R-squared from 0.284 to0.088. 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 thepositive rate of 0.001 per unit increase of Accounts Payable Period. On the other hand, thesame decreases after recession with AP as opposed to ideal expected behavior. This clearlydemonstrates the impact on sales and accounts payable cycle after recession. The rate ofdecrease of GROSSPR with FFAR has almost quadrupled after recession as expected in termsof exponential increase in secured and unsecured loans. The most striking comparison isyielded by the R-squared values and adjusted R-squared values. The R-squared value changesfrom0.391 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 thesame model is no longer applicable for the pre-recession scenario which is expected in thewake 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 fromnon-ideal negative 0.078 in post – recession to +0.071 in pre-recession model. Also, there isdramatic change in the coefficient of INV from a negative value in post-recession to a higherpositive in pre-recession model. This clearly demonstrates the impact on sales after recessionand inventory period. Ideally speaking, the coefficient of LOS should have been positive andGROSSPR must increase with increase of sales (LOS). This is truly encountered before 2008 asthe coefficient is positive. But after recession the coefficient of LOS is negative clearlydemonstrating abrupt changes in market due to unexplained forcing factors in times ofrecession. Our finding shows that there is a strong negative relationship betweenprofitability,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 theprofitability of firm. The most striking comparison is yielded by the R-squared values andadjusted 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 forthe pre-recession scenario we conclude that the same model is no longer applicable for thepre- recession scenario which is expected in the wake of extreme fluctuations in two datasets.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 someuseful 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 asthat of pre-recession scenario. Also the values deviate about its mean values about 41.5% inpre- recession while the window of fluctuations is narrowed down to 27.5% in post-recessionscenario. The range of variation of values is still very much the same. Similar trends aredepicted for (Debtors + Inventories)/Creditors. Mean value plums to 1.12 from 1.47 afterrecession, deviating from mean position about 198% before recession and about 91% afterrecession. 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 theexponential fall in debtors and simultaneously accelerated increase in creditors has forcedthe firms to manage their operating cycle more efficiently. They are more inclined to coveringcreditors from debtors and inventories alone and are more inclined to reduce their cashconversion cycle in the wake of low liquidity.REFERENCES[1] Afza, T., &Nazir, M. (2009). Impact of aggressive working capital management policyon firms profitability. The IUP Journal of Applied Finance, 15(8), 20-30.[2]AmarjitGill , Nahum Biger , Neil Mathur (2010). The Relationship Between WorkingCapital Management And Profitability: Evidence From The United States Business andEconomics 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 workingcapital management. Journal of Business, 20(2), 11-18.[6] Garcia-Teruel, P. J., &Martínez-Solano, P. (2007). Effects of working capital management onSME profitability. International Journal of Managerial Finance, 3(2), 164-177.[7]Ghosh SK, Maji SG, 2003. Working capital management efficiency: a study on theIndian 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 CapitalManagement and Profitability: A Vietnam Case International Research Journal of Financeand 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 changesin economic activity. Journal of Business, 10(2), 45-50.[11] Lazaridis, I., &Tryfonidis, D. (2006).Relationship between working capital management andprofitability of listed companies in the Athens stock exchange. Journal of FinancialManagement and Analysis, 19(1), 26-35[12] Raheman, A., & Nasr, M. (2007).Working capital management and profitability-case ofPakistani firms. International Review of Business Research Papers 3(1), 279-300.[13] Shin, H. H., &Soenen, L. (1998).Efficiency of working capital management andcorporate profitability. Financial Practice and Education, 8(2), 37-45.[14] Singh, J. P., &Pandey, S. (2008).Impact of working Capital Management in theProfitability 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, inreadings on the management of working capital. New York,St. Paul: West PublishingCompany.[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)