Working capital management profitability empirical

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  • 1. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –INTERNATIONAL JOURNAL OF MANAGEMENT (IJM) 6510(Online), Volume 4, Issue 1, January- February (2013)ISSN 0976-6502 (Print)ISSN 0976-6510 (Online)Volume 4, Issue 1, January- February (2013), pp. 121-129 IJM© IAEME: www.iaeme.com/ijm.asp ©IAEMEJournal Impact Factor (2012): 3.5420 (Calculated by GISI)www.jifactor.com WORKING CAPITAL MANAGEMENT AND PROFITABILITY: AN EMPIRICAL ANALYSIS OF INDIAN MANUFACTURING FIRMS Arunkumar O. N1 and T. Radharamanan 2 1 Research Scholar, Department of Mechanical Engineering, National Institute of Technology Calicut, Calicut 673601, Kerala, India E-mail: arunkumar_pme09@nitc.ac.in; 2 Assistant Professor, Department of Mechanical Engineering, National Institute of Technology Calicut, Calicut 673601, Kerala, India E-mail: radha_ramaman@nitc.ac.in; ABSTRACT This paper analyzes the effect of working capital management on the profitability of manufacturing firms. The study considers different variables affecting working capital management and their effect on the profitability of manufacturing firms. The authors apply correlation analysis and group wise weighted least squares regression analysis to identify the effects of these variables on profitability. The results of correlation analysis show that there is a negative relation between profitability and debtor’s days, inventory days, creditor’s days, and cash conversion cycle. The data analysis was carried for 1198 manufacturing firms listed in Centre for Monitoring Indian Economy for a period of 5 years. We find a significant negative relationship between profitability and debtors’ days, inventory days, creditors’ days and cash conversion cycle. We also find a significant positive relationship between the size of the firm and profitability. Keywords: working capital management, manufacturing firms, profitability, correlation analysis, regression analysis. 1. INTRODUCTION Well managed working capital is a barometer of operation efficiency, a source of operational and strategic flexibility, and a basis for improved vendor and client relationships. Yet many functions within the company are making decisions that directly and indirectly impact working capital management. Typically organizational efforts that are limited to squeezing suppliers, aggressive collections and drastic inventory reductions fail to address the 121
  • 2. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –6510(Online), Volume 4, Issue 1, January- February (2013)root causes leading to unsatisfactory returns on working capital improvement initiatives.Working capital management (WCM) due to its multifaceted nature is the most complexbusiness process to manage. It requires seamless integration of functionalinterdependencies of sales, operations and finance and a flawless execution to unlock thehidden working capital. Many existing research papers have found that managers spend aconsiderable time on day to day working of capital decisions since current assets are shortlived investments that are continually being converted to other assets type (Rao 1989).The chief financial officers of most companies spend most of their time and effort on dayto day WCM. Still, due to the inability of financial managers to properly plan and controlthe current assets and current liabilities of their companies, likewise, the failure of a largenumber of businesses can be attributed to the inefficient WCM (Smith 1973). Businesssuccess heavily depends on the financial executives’ ability to effectively managereceivables, inventory, and payables (Filbeck and Krueger 2005). Kumar and Tayyab(1989) formulate and estimate for India an aggregate production function. The rationalefor the formulation is argued from the importance of working capital funds in organizingproduction, and how the supply of money or the lack thereof, may constrain itsavailability in a financially underdeveloped economy characterized by imperfect capitalmarkets Specific research studies exclusively on the impact of WCM on profitability ofmanufacturing firms are scanty especially for the case of India. India is attractingsignificant attention as an attractive location for manufacturing industries in recent times.As an important sector in the overall economic growth, manufacturing sector requires indepth analysis at industry as well as firm level. Keeping this in view and the widerrecognition of the potential contribution of the manufacturing sector to the economy ofdeveloping countries, the following objectives have been made for the study • To make a panel data analysis of the manufacturing firms listed in Centre for Monitoring Indian Economy (CMIE) for a period of 5 years • To study the variables affecting the profitability of the Indian firms • To establish a relationship between the profitability and the variables affecting the manufacturing firms • To find out the relationship between profitability and size of the firm • To find out the contribution of debtors days to the profitability of the firm The inferences of the study are expected to be beneficial to the Indianmanufacturing firms in understanding the variables that influence their profitability. Thepanel data analysis and group-wise weighted least squares analysis made in Indiancontext are the contributions to the literature by the authors. The regression results areable to predict the percentage improvement in profit that can be obtained by controllingthe number of days of debtors, number of days of inventory and creditors’ days and cashconversion cycle. The rest of the paper is organized as follows: Section 2 looks at the relevant literature.The data and the variables are explained in section 3. Section 4 explains the empiricalanalysis and its interpretation, by providing the results of descriptive statistics, correlationand regression analysis. Conclusions are given in section5. 122
  • 3. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –6510(Online), Volume 4, Issue 1, January- February (2013)2. LITERATURE REVIEW Smith (1973) identified eight major approaches taken towards the management of theworking capital. Efficiency of working capital management is based on the principle ofspeeding up collections as much as possible and slowing down disbursements as much aspossible. This working management principle, based on the traditional concepts of the cashconversion cycle introduced by Richards and Laughlin (1980), is a powerful performancemeasure for assisting how well a company is managing its working capital. Gentry, Vaidyanthan and Lee(1990) develop a weighted cash conversion cycle, whichscales the timing by the amount of funds in each step of the cycle. Larger inventory reducesthe risk of a stock out. Trade credit may stimulate sales because it allows customers to assessproduct quality before paying (Deloof & Jegers, 1996; Long, Malitz, & Ravid, 1993). As a part of a study of the fortune 500s financial management practices, Gilbert andReichert (1995) found that account receivable management models are used in 59% of thesefirms to improve WCM projects, while, inventory management models were used in 60% ofthe companies. Shamsud and James (1996) analyze the content and process of turnaroundstrategies in smaller manufacturing firms. Weinraub and Visscher (1998) observe a tendencyof firms with low levels of current ratios to have low levels of current liabilities. Shin andSonen (1998) found a strong negative relation between the cash conversion cycle andcorporate profitability for a large sample of listed American firms for the 1975-1994 periods.Howorth and Westhead (2003) examined working capital management routines of a largerandom sample of small companies in the UK. Deloof (2003) investigates the relation between WCM and corporate profitability of1,009 large Belgian non financial firms. From the studies conducted to identify the trends inWCM and its impact on Mauritian small manufacturing firms, Pandachi, (2006) identify thatthe working capital needs of an organization changes over time as does its internal cashgeneration rate. Raheman and Nasr (2007) conducted a study to analyze the relationshipbetween WCM and profitability in case of Pakistani firms. The result shows that, there is astrong negative relationship between variables of WCM and profitability of the firm. Thefirms can increase their profitability by reducing investment on accounts receivable andinventories to a reasonable minimum, indicated by the benchmarks for their industry (Teruel& Solano, 2007). The above discussion clearly implies the importance of working capital management(WCM) in determining the firm’s success. The present work tries to identify the variousfactors of working capital management influencing profitability of manufacturing firms inIndia.3. DATA AND VARIABLES This study uses financial statements of executive summary, assets and liabilitystatements of manufacturing firms listed in Centre for Monitoring Indian Economy (CMIE)for a period of 5 years (i.e. 2005-06 to 2009-10). The data was collected for 1211 firms andthe firms with the 1% outlying values for Debtors Days (DTRDAYS), Inventory Days(INVDAYS), and Creditors Days (CTRDAYS) were left out. Thus the samples size consistsof a balanced panel set of 5990 firm year observations of 1198 firms.Profit beforedepreciation tax accounts (PBDTA), the dependent variable, is taken as a proxy forprofitability. Table 1 presents the independent and control variables, notations and itscalculation methods used in the analysis. 123
  • 4. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –6510(Online), Volume 4, Issue 1, January- February (2013) Table 1. Variables Used in the AnalysisNo Variable Notations Calculation Method1 Debtors Days DTRDAYS [accounts receivable * 365] / Sales.(It is taken from executive summary of the firms)2 Inventory Days INVDAYS [Inventories*365] / Cost of goods sold3 Creditors Days CTRDAYS [accounts payable*365]/ Cost of goods sold. (It is taken from executive summary of the firms)4 Cash Conversion Cycle CCC DTRDAYS + INVDAYS – CTRDAYS5 Current Ratio CR Current Assets / Current Liability6 Ratio of Current Liability CLTOTA Current Liability/ Total Assets to Total Assets.7 Financial Assets to Total FATOTA Financial Assets / Total Assets Assets8 Size SIZE Natural Logarithm of Total Assets9 Assets Turnover Ratio ATR Sales/total asset Debtors Days, Inventory Days, Creditors Days and Cash Conversion Cycle are usedas independent variables. CCC is used as the comprehensive measure of working capital as itshows the time lag between expenditure for the purchase of raw materials and the collectionof sales of finished goods. CR, CLTOTA, FATOTA, SIZE and ATR are used as the controlvariables.4. DATA ANALYSIS AND INTERPRETATION4.1 Descriptive Statistics Table 2 shows the descriptive statistics. The manufacturing industry is having on anaverage 22 % of profit for its sales and most of the firms included in the analysis show profitof a 9%. The average CCC is 57.63 days (median is 49.45days), it shows two month’s timefor the cash conversion cycle. The firm receives the payment on sales after an average of65.49days with median 51.26 days. It takes on average 81.83days (median is 58.27) toconvert the raw materials and sell the finished goods inventory and firms take on an average105.21days (with median 64.20days) to pay purchases. The analysis of Indian manufacturingfirms shows that the firms less credit period to the customers in comparison with what theyare enjoying. 124
  • 5. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 – 6510(Online), Volume 4, Issue 1, January- February (2013) Table2. Descriptive Statistics Variables Mean Minimum Median Maximum Std. Dev DTRDAYS 65.49 0.00 51.26 3174.30 90.00 INVDAYS 73.97 0.00 58.27 4584.80 100.26 CTRDAYS 81.83 0.00 64.20 4610.60 122.44 CCC 57.63 -2567.40 49.45 4498.50 130.85 CR 3.76 -1.28 2.26 1080.00 17.24 CLTOTA 0.31 -0.20 0.21 144.22 1.98 FATOTA .03513 -.00044 0.00053 56.27060 0.76002 SIZE 5.13 -3.22 5.07 12.43 1.71 ATR 1.30 0.00 1.02 384.74 5.29 PBDTA 131.59 -252.19 13.600 31057. 927.93 CR is the traditional measure of liquidity. It indicates the availability of current assets in rupees for every one rupee of current liability. The industry has a high liquidity with the average current ratio being 3.76. For manufacturing firms, the current liabilities are 31% of total assets. Sales are 1.3 times the total assets employed. Financial assets employed in the firm are only an average of 3.5% of the total assets. The size of the company is calculated as the logarithm of total assets. The mean value of ATR shows that sales of the firms are on an average of 1.3 times of the total assets. For most of the manufacturing firms in India, the sales are equal to the total assets employed by the firm. 4.2 Correlation Analysis Table3 presents correlation coefficients at 5% critical value (two tailed) (=0.0253) for all variables considered. There is a negative relation between PBDTA and measures of working capital management such as DTRDAYS, INVDAYS, CTRDAYS and CCC. This is consistent with the view that, when we consider the variables independently, the time lag between the expenditure for the purchase of raw materials and the collection of sales of finished goods can be too long, and that decreasing this time lag increases profitability. PBDTA shows a negative correlation with current ratio implies that profitability and liquidity are inversely related. PBDTA shows a positive correlation with CLTOTA, FATOTA SIZE and ATR. So, these variables have high influence on the return on assets. Size of the firm shows a negative correlation with PBDTA. Table3. Correlation Matrix DTRDAYS INVDAYS CTRDAYS CCC CR CLTOTA FATOTA SIZETA ATR PBDTADTRDAYS 1.0000 0.0305 0.5610 0.1863 0.0133 0.0041 -0.0022 -0.0997 -0.0429 -0.0582INVDAYS 1.0000 0.1715 0.6268 0.0347 -0.0148 -0.0066 0.0072 -0.0355 -0.0249CTRDAYS 1.0000 -0.4185 -0.0532 0.0362 0.0065 0.0219 -0.0289 -0.0077CCC 1.0000 0.0855 -0.0424 -0.0126 -0.0836 -0.0297 -0.0519CR 1.0000 -0.0176 -0.0024 -0.0810 -0.0082 -0.0153CLTOTA 1.0000 0.8990 -0.0372 0.9156 0.1056FATOTA 1.0000 -0.0033 0.8954 0.1080SIZE 1.0000 -0.0464 0.3355ATR 1.0000 0.1058PBDTA 1.0000 125
  • 6. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –6510(Online), Volume 4, Issue 1, January- February (2013)4.3 Regression Analysis Regression analysis is used to estimate the causal relationship between profitabilityand the other chosen variables. The determinants of corporate profitability are estimated byusing group wise weighted least squares. This study uses panel data regression analysis ofcross-sectional and time series data. The specific forms of the models used for the linearregression analysis are as follows:ܲ‫ܣܶܦܤ‬௜௧ ൌ ߚ଴ ൅ ߚଵ ‫ܻܵܣܦܴܶܦ‬௜௧ ൅ ߚଶ ‫ܴܥ‬௜௧ ൅ ߚଷ ‫ܣܱܶܶܮܥ‬௜௧ ൅ ߚସ ‫ܣܱܶܶܣܨ‬௜௧ ൅ ߚହ ܵ‫ܧܼܫ‬௜௧ ൅ ߚ଺ ‫ܴܶܣ‬௜௧ ൅ ߝ௜௧ … … … … … … … … … … … … … … … ሺ1ሻܲ‫ܣܶܦܤ‬௜௧ ൌ ߚ଴ ൅ ߚଵ ‫ܻܵܣܦܸܰܫ‬௜௧ ൅ ߚଶ ‫ܴܥ‬௜௧ ൅ ߚଷ ‫ܣܱܶܶܮܥ‬௜௧ ൅ ߚସ ‫ܣܱܶܶܣܨ‬௜௧ ൅ ߚହ ܵ‫ܧܼܫ‬௜௧ ൅ ߚ଺ ‫ܴܶܣ‬௜௧ ൅ ߝ௜௧ … … … … … … … … … … … … … … … ሺ2ሻܲ‫ܣܶܦܤ‬௜௧ ൌ ߚ଴ ൅ ߚଵ ‫ܻܵܣܦܴܶܥ‬௜௧ ൅ ߚଶ ‫ܴܥ‬௜௧ ൅ ߚଷ ‫ܣܱܶܶܮܥ‬௜௧ ൅ ߚସ ‫ܣܱܶܶܣܨ‬௜௧ ൅ ߚହ ܵ‫ܧܼܫ‬௜௧ ൅ ߚ଺ ‫ܴܶܣ‬௜௧ ൅ ߝ௜௧ … … … … … … … … … … … … … … … … ሺ3ሻܲ‫ܣܶܦܤ‬௜௧ ൌ ߚ଴ ൅ ߚଵ ‫ܥܥܥ‬௜௧ ൅ ߚଶ ‫ܴܥ‬௜௧ ൅ ߚଷ ‫ܣܱܶܶܮܥ‬௜௧ ൅ ߚସ ‫ܣܱܶܶܣܨ‬௜௧ ൅ ߚହ ܵ‫ܧܼܫ‬௜௧ ൅ ߚ଺ ‫ܴܶܣ‬௜௧ ൅ ߝ௜௧ … … … … … … … … … … … … … … … … … … … … … . … ሺ4ሻWhereߚ଴ = Intercept of the equationߚଵ , ߚଶ , … . . ߚ଺ = Coefficient of the variablesε = Error temi = Number of firms, 1 to 1198.t = Time period, 1 to 5.Four regression models are modeled to study the impact of the independent variablesindividually. The pooled ordinary least squares (OLS) regression model shows heteroskedasticity.Because of heteroskedasticity, t–test and F – test fail. To counter this problem, it isrecommended, the analysis is conducted by using Weighted Least Squares (Wooldridge,2004). It is a generalized least squares technique using weight. We conduct group wiseweighted least squares. Weights are based on per unit error variance. The analyses areconducted using Gretl software. Table 4 shows the results of regression analysis. Regression model (1) is estimated with DTRDAYS being considered as independentvariable. The coefficient of DTRDAYS is negative and implies that a decrease in the numberof days of accounts receivable by one day is associated with an increase of profitability by3.195% and it is significant with 99 percentage level of significance. 126
  • 7. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –6510(Online), Volume 4, Issue 1, January- February (2013) Table 4. Group wise Weighted Least SquaresVariable Regression model Regression Regression Regression (1) model(2) model(3) model(4)Constant -227.959 -224.610 -229.190 -227.531 (0.0000***) (0.0000***) (0.0000***) (0.0000***)DTRDAYS -0.0319540 ------ ------- ------- (0.0015***)INVDAYS ------- -0.0800197 ------- ------- (1.73e-014***)CTRDAYS ------- --------- -0.0284547 -------- (0.0002***)CCC ------- ------- -------- -0.0279363 (0.0004***)CR 0.128708 0.243744 0.0952759 0.186840 (0.0003***) (0.0002***) (0.0775*) (0.0019***)CLTOTA 17.2429 16.7878 19.2520 15.7647 (8.79e-013***) (3.35e-013 ***) (6.58e-014***) (1.57e-011***)FATOTA -4.25933 -3.13904 -4.04744 -3.75683 (0.0282**) (0.1400) (0.0537*) (0.0604*)SIZE 51.2588 51.3723 51.5616 51.0355 (0.0000***) (0.0000***) (0.0000***) (0.0000***)ATR 5.59321 4.78390 5.10953 5.60711 (1.72e-024***) (1.18e-019***) (1.51e-023***) (1.36e-029***)R2 Value 0.349020 0.349369 0.349801 0.347296Adj. R2 0.348367 0.348717 0.349149 0.346641F test 534.6262 535.4481 536.4675 530.5806(Sig.) (0.000000) (0.000000) (0.000000) (0.000000)*** The variable with 99 percentage level of significance.** The variable with 95 percentage level of significance.* The variable with 90 percentage level of significance. In regression model (2), profitability and number of days of inventories (INVDAYS)has negative relationship with 99 percentage level of significance. This means that theincrease in number of days of inventory will lead to increase in profitability and vice versa. The regression model (3) shows that CTRDAYS is inversely related to profitability.The relationship is also found significant at 99 percentage. Long number of days of accountspayable led the firm to a low level of profitability and vice versa. An alternate explanation isless profitable firms wait longer to pay their bills. The cash conversion cycle (CCC) is takenas an independent variable in regression model (4). The coefficient of CCC is negativelyrelated to profitability with 99 percentage level of significance. A decrease in the cashconversion cycle by one day is associated with an increase of profit by 2.79%. In all regression models PBDTA is positively related with CR, CLTOTA, SIZE andATR with 99 percentage significance. FATOTA is negatively related to PBDTA at 95%significance level. The results of regression models (1) to (4) suggests that the managers canincrease the corporate profitability by decreasing Debtors Days (DTRDAYS), Creditors Days(CTRDAYS), Inventory Days (INVDAYS), and Cash Conversion Cycle (CCC). 127
  • 8. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –6510(Online), Volume 4, Issue 1, January- February (2013)The natural logarithm of the total assets is taken as a proxy for the size of the firm. The sizeof the firm is found to be significantly positively related to profitability of the firm. Thisindicates that higher the size of the firm the profitability increases. A 1% increase in SIZE isassociated with an average 0.51% increase in profitability. The F test proves that there is no possibility of getting zero values for all regressioncoefficients of variables or there is a possibility that at least one regression coefficient willget more than a zero value. The F test shows that the model has the possibility of predictingPBDTA with a high significant level since the p value is (0.00). The adjusted R2 of theregressions are 35%, means that 35% of variability in variances are explained by the model.5. CONCLUSION Most firms have a large amount of cash invested in working capital. It can thereforebe expected that the way in which working capital is managed will have significant impact onthe profitability of firms. The descriptive and regression analyses have identified criticalmanagement practices and are expected to assist mangers in identifying areas where theymight improve the financial performance of their operation. The study has been conducted on manufacturing industries, irrespective of thebusiness differences. The findings of the analysis show a significant negative relationshipbetween profitability and debtors’ days, inventory days, creditors’ days and cash conversioncycle. The results suggest that the managers can create value for their share holders byreducing cash conversion cycle. The negative relationship between creditors days andprofitability suggest that long number of days of accounts payable leads the firm to a lowlevel of profitability and vice versa.REFERENCES 1. Abdul Raheman and Mohamed Nasr (2007), “Working Capital Management and Profitability-Case of Pakistani firms”, International Review of Business Research Papers, Vol. 3, pp. 279-300. 2. Carole Howorth and Paul Westhead. (2003), “The Focus of Working Capital Management in UK Small Firms”, Management Accounting Research, Vol. 14, pp. 94-111. 3. Deloof M. and Jeger M. (1996), “Trade Credit, Product Quality, and Intra group Trade: Some Empirical Evidence”, Financial Management, Vol. 25, Issue 3, pp. 945- 68. 4. Deloof, D. (2003), “Does Working Capital Management affect Profitability of Belgian Firms?”, Journal of Business Finance and Accounting, Vol. 30 Issue (3&4), pp. 573-587. 5. Gentry J.A., Vaidyanthan R., and Lee H.W. (1990), “A Weighted Cash Conversion Cycle”, Financial Management, Vol. 19, Issue 1, pp.90-99. 6. Gilbert E. and Reichert A. (1995), “The Practice of Financial Management among Large United States Corporations”, Financial Practice and Education, Vol. 5, Issue 1, pp. 16-23. 7. Greg Filbeck and Kruger M. Thomas (2005), “An analysis of working capital management results across industries”, Mid American Journal of Business Vol. 20, Issue 2, pp. 11-18. 128
  • 9. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –6510(Online), Volume 4, Issue 1, January- February (2013) 8. KessevanPandachi (2006), “Trends in Working Capital Management and Its Impact on Firm’s Performance: An analysis of Mauritian Small Manufacturing Firms”, International Review of Business research papers, Vol.2, Issue 2, pp. 45-58. 9. Kumar R. C. and Tayyab A. (1989), “Money, working capital and production in a developing economy: A disequilibrium econometric model of production for India 1950–1980”, Empirical Economics, Vol.14, Issue 3, pp. 229-39. 10. Long M.S., Malitz, I.B., and Ravid, S.A. (1993), “Trade Credit, Quality Guarantees, and Product Marketability”, Financial Management, Vol. 22, Issue 4, pp. 117-27. 11. Pedro Juan Garcia-Teruel and Pedro Martinez-Solano (2007), “Effects of Working Capital Management on SME Profitability”, International Journal of managerial finance, Vol. 3, Issue2, pp. 164-177. 12. Rao R. K. S. (1989), “Fundamentals of financial management”, Macmillan Publishers. 13. Richard V.D and Laughlin E.J. (1980), “A Cash Conversion Cycle Approach to Liquidity Analysis”, Financial Management, Vol. 9, Issue 1, pp.32-38. 14. Shamsud D. Chowdhury and James R. Lang. (1996), “Turnaround in Small Firms: An Assessment of Efficiency Strategies”, Journal of Business Research, Vol. 36 Issue 2, pp.169-178. 15. Shin H. H. and Soenen L. (1998), “Efficiency of Working Capital and Corporate Profitability”, Financial Practice and Education, Vol. 8, Issue 2, pp.37-45. 16. Smith K. V. (1973), “State of the art of working capital management” Financial Management Autumn, pp.50-55 17. Weinraub H. and Visscher S. (1998), “Industry Practice Related to Aggressive / Conservative Working Capital Policies”, Journal of Financial and Strategic Decisions, Vol.11, Issue 2, pp. 39-46. 18. Leandro Torres Di Gregorio and Carlos Alberto Pereira Soares, “Use Of The Mix-Based Costing – Mixbc – To Determine Product Profitability, Level Of Attractiveness and Synergy of The Production Mix” International Journal of Management (IJM), Volume 3, Issue 2, 2012, pp. 1 - 12, Published by IAEME. 129