ijcrb.webs.com INTERDISCIPLINARY JOURNAL OF CONTEMPORARY RESEARCH IN BUSINESS JANUARY 2012 VOL 3, NO 9

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Affiliation between Working Capital Management and Profitability Dr. Amalendu Bhunia

Reader, Department of Commerce Fakir Chand College, Diamond Harbour

West Bengal, India Mr. Amit Das

Assistant Professor in Commerce, Fakir Chand College, Diamond Harbour

West Bengal, India Abstract

This study examines the relationship between the working capital management and profitability of Indian private sector small-medium steel companies obtained from CMIE database. Working capital management indicators and profitability indicators over the period from 2003 to 2010 are moulded as a linear regression system in multiple correlation and regression analysis. The study shows a small relationship between WCM including working capital cycle and profitability. Multiple regression tests confirm a lower degree of association between the working capital management and profitability.

Keywords: Working capital, profitability, small-medium, Indian steel companies, multiple regressions

1. Introduction

Working capital is an important issue during financial decision making since its being a part of investment in asset that requires appropriate financing investment. However, working capital always being ignored in financial decision making since it involve investment and financing in short term period. Further, also act as a restrain in financial performance, since it does not contribute to return on equity (Sanger, 2001). Though, it should be decisive for to a firm to keep up their short term investment in view of the fact that it will ensure the ability of firm in longer period. The fundamental part in managing working capital is required maintaining its liquidity in day-to-day operation to ensure its silky running and meets its obligation (Eljelly, 2004). On the other hand, this is not a straightforward job given that managers must make confident that business operation is running in proficient and gainful mode. There are the possibilities of divergence of current asset and current liability during this process. If this happens and firm‟s manager cannot manage it properly then it will affect firm‟s growth and profitability. This will further accompany to financial distress and finally firms can go insolvent.

Working capital management plays a significant 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. On the other hand, Van Horne and Wachowicz (2004) mention that excessive level of current assets may have a negative upshot of a firm‟s profitability, while a low level of current assets may escort to lowers of liquidity and stock-outs, resulting in difficulties in maintaining smooth operations. Problem in working capital management is to accomplish desired trade off between liquidity and profitability (Raheman & Nasr, 2007). Referring to theory of risk and return, investment with more risk will result to more return. Accordingly, firms with high liquidity of working capital may have low risk then low profitability. On the contrary, firm that has low liquidity of working capital, facing high risk results to high profitability. The issue here is in managing working capital, firm must take into consideration all the items in both accounts and try to balance the risk and return.

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Steel Industry, which has been singled out from investigation in the present study, is indeed the backbone of economic growth in any country. A thick relationship has been found between the level of economic growth and the quantum of steel consumption in developed as well as developing countries. A steel industry, through its forward and backward linkages, provides the maximum stimulus to growth in comparison with other industry. Since independence, to ensure rapid economic development, it was deemed essential that a sound steel production programme on a formidable basis be formulated. Accordingly, the Government of India set up various integrated steel plants during the various Five-Year Plans and the production capacities of the existing plants were enhanced. At present, recording a growth of just over 9 per cent, India had consumed 63.55 million tonnes of steel in 2009-10 compared to 58.28 million tonnes in the previous year. According to latest government figures available, during the April-December period of 2010-11, total steel consumption in India was 51.8 million tonnes, a 9.53 per cent growth vis-à-vis the corresponding period of the previous fiscal. With nearly 80 million tonnes per annum (mtpa) installed capacity, India is currently the world‟s fifth largest producer of crude steel (Steel Yearbook, 2010).

The priority given by the country failed to some extent owing to poor capacity utilization and consumption. However, Muthuraman, Vice-chairman of Tata Steel (April 26, 2011) has said in a newspaper “Economic Times” that the present per capita consumption of steel in India is only around 49 kg, against the world average of 182 kg. The opinion of the management of steel industry is very sharp in the matter of under-utilization of production and its per capita consumption. They clearly attributed such failure to the inadequacy of exogenous and infrastructure factors. Unless the management quantifies the under-utilization consequent upon inadequate supplies from exogenous sources and infrastructures, they have to acknowledge to a large extent the responsibility for the unsatisfactory performance and inefficiency of the units. This call for a full diagnosis of the malady, that is, identification, analysis and quantification of the interfering constraints in achieving full utilization of the capacities, thus opens a vast field for research and enquiry.

In the present study, therefore, an attempt has been made to examine and evaluate the management of working capital and its effects on profitability as a factor accountable for poor performance in the steel industry in India. Specific objectives are to examine a relationship between working capital management and profitability over a 8 years period, to establish a relationship between the two objectives of liquidity and profitability of the firms and to investigate the relationship between debt used by the a firm and its profitability.

2. Review of Related Literatures

A brief review of the different efforts of research in the field is attempted in the following paragraphs.

Shin and Soenen (1998) examined the association between working capital management and value creation for shareholders using the standard measure for working capital management of cash conversion cycle in terms of net trading cycle (inventory conversion period and receivable conversion period less payable conversion period) on account of COMPUSTAT sample of 58985 firms for the period from 1975 to 1994. Net trading cycle as a proxy for additional working capital needs as a function of the projected sales growth. They observed the association by using correlation and regression analysis, through industry and working capital strength. They establish a strong negative association between the lengths of the firm‟s net trading cycle and its profitability and recommend that one possible way to create shareholder value is to reduce firm‟s net trading cycle.

Ghosh and Maji (2003) investigated the efficiency of working capital management of Indian cement companies for the period from 1993 to 2002 via three index values performance index, utilization index and overall efficiency index to measure the efficiency of working

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capital management, in place of using working capital management ratios. With the help of regression analysis and industry norms as an objective efficiency level of individual firms, they checked the speed of achieving target level of efficiency by individual firms during the period of study and establish that several sample firms effectively enhanced effectiveness in these years.

Lazaridis and Tryfonidis (2006) examined the relationship between working capital management and profitability of 131 firms listed on the Athens Stock Exchange for the period from 2001 to 2004 through cross sectional study with correlation and regression analysis and found statistically significant relationship between profitability (gross operating profit) and cash conversion cycle and its components. They advocate that managers can create profits for their companies through appropriate treatment of the optimum level of cash conversion cycle.

Garcia-Terual et all (2007) examined the impact of working capital management on SME profitability based on 8872 SMEs of Spain using the panel data methodology for the period from 1996 to 2002. The empirical results that are vigorous to the existence of endogeneity, confirmed that managers could create value by reducing their inventories and the number of days for which their accounts are outstanding. In addition, restricting the cash conversion cycle moreover perk ups the firm‟s profitability.

Mathuva (2009) observed the persuade of working capital management components on corporate profitability by using a sample of 30 firms listed on Nairobi Stock Exchange for the periods from 1993 to 2008 through Pearson and Spearman‟s correlations, the pooled ordinary least squares and the fixed effects regression models in the direction of carry out data analysis. He claimed that there subsists a extremely noteworthy negative association between the time for cash collection from their customers and profitability. He also confirmed that there is a very much momentous positive association between the inventories to sales conversion period and profitability and there survives an exceedingly important positive association between the time for cash payment to its creditors and profitability.

The conclusive sum of this retrospective review of relevant literatures produced till date on the offered subject reveals wide room for the validity and originates of this work and reflects some crucial clues that affirm its viability, as may be marked here it. No study has incorporated the association between management of working capital and profitability of the Indian private sector steel industry. Nor has any previous research examined the existence of liquidity and profitability relationship and its efficiency of Indian steel companies.

2.1. Objectives of the Study

The main objective of the present work is to make a study on the association between working capital management and profitability and its efficiency in operating steel companies in India. More specifically, it seeks to dwell upon mainly the following issues:

• To assess the working capital management efficiency;

• To observe the liquidity position and areas of weakness, if any;

• To investigate the relationships between liquidity and profitability;

• To assess the association between debt financing and profitability;

• To measure the association between working capital cycle and profitability;

• To explore the association between working capital management and profitability. 2.2. Hypotheses of the Study

Since the objective of this study is to examine the relationship between profitability and working capital management, the study makes a set of testable hypotheses:

Hypothesis 1

H0: Liquidity position has no impact on profitability.

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Hypothesis 2

H0: There is no relationship between debt financing and profitability. H1: There exist a relationship between debt financing and profitability. Hypothesis 3

H0: There is no relationship between working capital cycle and profitability. H1: There exists a relationship between the working capital cycle and profitability. Hypothesis 4

H0: Working capital management has no impact on profitability.

H1: Working capital management has a significant impact on profitability. 3. Research Methodology

This section of the article whine the firms and variables included in the study, the distribution patterns of data and applied statistical techniques in exploring the association between working capital management and profitability.

3.1 Data Set & Sample Design

The data used in the present study was acquired from CMIE database. The purposive sample design method was applied in this analysis. The sample is based on financial statements of the 50 small-medium Indian private sector steel companies of our economy, those who have often been neglected for enquiry and research. Because of the backbone of economic growth in any country, only steel companies are included in the sample. Preferred samples of private sector steel companies from the year of 2003 to 2010 were utilized in this analysis. The definitions of “private” are: (i) part of the economy that is not state controlled, (ii) run by individuals and companies for profit, (ii) encompasses all for-profit businesses that are not owned or operated by the government and (ii) in most free-market economies, the private sector is the sector where most jobs are held. The used of a chosen sample of private sector might set up a potential firm‟s success prejudice (Bhunia, 2009). It is claimed that the potential for success is overstated using this technique. Nevertheless, it is concerned that the prejudice may or may not be essential depending on the handling of the model. If the model is used to rank the firms for the budding triumph to facilitate execute a more detailed analysis, and then the prejudice is not significant. On the other hand, if the model is used to recognize investment portfolio selection, subsequently the prejudice is significant. The period covered by the study extends to eight years starting from 2003 to 2010. The reason for restricting to this period was that the latest data for analysis was available for this period. A total of 50 small-medium private sector Indian steel companies were identified during the year of determination. Table 1, below, disclosed the name of small-medium private sector Indian steel companies.

Table-1: Name of Selected Small-Medium Private Sector Indian Steel Companies Sl.

No.

Name of the Companies Sl.

No.

Name of the Companies 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. A H W Steels Ltd. Aarti Steels Ltd. Aditya Ispat Ltd. Ahmedabad Steelcraft Ltd. Anil Special Steel Inds. Ltd. Anjaney Ferro Alloys Ltd. Antarctic Industries Ltd. Ashiana Ispat Ltd. Asian Alloys Ltd. Avon Ispat & Power Ltd. Balasore Alloys Ltd.

Bellary Steels & Alloys Ltd.

26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. Continental Profiles Ltd. Eastcoast Steel Ltd. Elango Industries Ltd. Essar Steel Ltd. Facor Alloys Ltd. Facor Steels Ltd.

Ferro Alloys Corpn. Ltd. Gallantt Metal Ltd.

Gangotri Iron & Steel Co. Ltd. Garg Furnace Ltd.

Godawari Power & Ispat Ltd. Gontermann-Peipers (India) Ltd.

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JANUARY 2012 VOL 3,NO 9 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. Bhagwandas Metals Ltd. Bhaskar Shrachi Alloys Ltd. Bhoruka Steel & Services Ltd. Bhupco Alloys Ltd.

Bhushan Power & Steel Ltd. Bhushan Steel Ltd.

Bhuwalka Steel Inds. Ltd. Bihar Foundry & Castings Ltd. Bloom Industries Ltd.

Chamundi Steel Castings (India) Ltd. Chandan Steel Ltd.

Chandi Steel Inds. Ltd. Chase Bright Steel Ltd.

38. 39. 40 41. 42. 43. 44. 45. 46. 47. 48. 49. 50.

Gopal Iron & Steels Co. (Gujarat) Ltd. Graham Firth Steel Products (India) Ltd. Grand Bright Bars Ltd.

Haryana Steel & Alloys Ltd. Hi-Tech Pipes Ltd.

Hira Ferro Alloys Ltd. Hisar Metal Inds. Ltd. I S M T Ltd.

I U P Jindal Metals & Alloys Ltd. Impex Ferro Tech Ltd.

Indian Bright Steel Co. Ltd. Indian Metals & Carbide Ltd. Indian Metals & Ferro Alloys Ltd.

The sample firms used in this study came from same industries. Due to the controlled sample volume for steel industry, the research focuses on the private sector industry sector.

3.2 Variables

The present study carries out the issue of recognizing key variables that influence working capital management. All the variables stated below have been used to test the hypotheses of our study. The dependent variable is defined as the profitability of the sample firms. The independent variable is interpreted as the commonly used financial ratios. The ratios used are chosen from those utilized by Bhunia (2007), Refuse (1996) and Singh et all (2008). An itemized listing of the variables is accessible in Table 2.

Table-2: List of Ratios Examined

S.N. Independent variables S.N. Dependent variable V1. V2. V3. V4. V5. V6. V7. V8. V9. Current Ratio (CR) Liquid Ratio (LR)

Cash Position Ratio (CPR) Debt-Equity Ratio (DER) Interest Coverage Ratio (ICR) Inventory Turnover Ratio (ITR) Debtors Turnover Ratio (DTR) Creditors Turnover Ratio (CTR) Working Capital Cycle (WCC)

Return on Capital Employed (ROCE)

All the above variables have relationships that ultimately affect working capital management.

3.3 Descriptive Statistics

Table 3 affords descriptive statistics of the collected dependent and independent variables. All variables were calculated using accounting ratios. The accounting ratios were used because the CMIE database provides all ratios related to the variables, which was used in this study. In addition, the measurement of profitability could only be based on return on capital employed, not on purported profitability ratio. For measuring management of working capital, nine working capital indicators/independent variables and one profitability indicator/dependent variable were tested with comparison of grand industry averages/industry averages. To formulate the study and explanation more specific and perfect, the values of A.M., S.D., C.V., maximum, minimum, Skewness and Kurtosis have been computed from the ratios.

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Table-3: Descriptive Statistics

CR LR CPR DER ICR ITR DTR CTR WCC ROCE

N Mean SE of M S.D. C.V. (%) Median Skewness Kurtosis Maximum Minimum 50 2.31 0.25 2.54 109.9 1.56 3.24 12.55 14.91 -3.14 50 0.69 0.07 0.70 101 0.51 1.65 8.22 4.11 -1.6 50 0.26 0.06 0.57 219 0.51 3.27 19.3 3.86 -1.7 50 2.09 0.32 3.18 152 1.18 5.51 38.1 26.7 0.00 50 1.67 1.14 11.37 680.8 1.50 1.09 23.40 72.93 -58.2 50 44.26 4.14 41.44 93.63 32.13 3.40 15.27 276.7 6.02 50 10.94 0.67 6.71 61.33 9.29 1.95 5.43 42.54 2.57 50 21.31 5.76 57.61 270.3 10.94 8.70 81.60 564.1 2.30 50 150.8 15.63 156.3 103.6 94.50 2.61 7.66 885.5 5.52 50 1.89 3.53 35.29 1867 8.06 -6.41 50.44 35.51 -292

CR LR CPR DER ICR ITR DTR CTR WCC ROCE

Mean SE of M S.D. C.V. (%) Maximum Minimum 0.96 0.07 0.22 22.92 1.33 0.71 0.32 0.05 0.16 50.0 0.60 0.20 0.13 0.05 0.15 115 0.41 0.01 2.02 0.26 0.79 39.1 3.02 0.87 1.71 0.98 8.46 494.8 32.87 0.04 42.48 6.56 19.69 46.35 71.99 21.40 47.04 5.65 16.96 36.05 67.59 24.68 69.97 4.17 12.51 17.88 84.30 51.34 139.2 7.48 52.87 37.86 201.6 67.88 2.04 0.27 0.94 46.08 45.11 -8.47 3.4 Correlation Statistics

Generally, correlation analysis attempts to determine the degree and direction of relationship between two variables under study. In a bivariate distribution, if the variables have the cause and effect relationship, they have high degree of correlation between them. The co-efficient of correlation is denoted by “r”. The correlation is studied using Karl Pearson‟s correlation formula.

N Σxy - (Σx) (Σy)

r = --- (Karl Pearson‟s correlation formula) √ (N Σx2 – (Σx)2 ) (N Σy2 – (Σy)2)

Table-4: Correlation Statistics

CR LR CPR DER ICR ITR DTR CTR WCC ROCE

CR 1 LR 0.57** (0.00) 1 CPR 0.18 (0.07) 0.812** (0.00) 1 DER -0.13 (0.20) -0.187 (0.06) -0.15 (0.13) 1 ICR -0.02 (0.86) 0.068 (0.50) 0.08 (0.46) 0.02 (0.85) 1 ITR -0.003 (0.98) 0.018 (0.86) 0.04 (0.66) -0.07 (0.52) -0.13 (0.20) 1 DTR -0.10 (0.34) -0.07 (0.49) 0.01 (0.94) 0.04 (0.66) 0.24* (0.02) -0.01 (0.96) 1 CTR 0.04 (0.72) 0.02 (0.84) 0.03 (0.79) -0.03 (0.80) -0.03 (0.74) -0.06 (0.56) 0.12 (0.25) 1 WCC 0.34** (0.00) 0.05 (0.00) 0.02 (0.83) -0.06 (0.53) -0.02 (0.87) 0.01 (0.93) -0.06 (0.58) -0.01 (0.91) 1 ROCE 0.03 (0.80) -0.02 (0.81) -0.09 (0.40) -0.02 (0.81) 0.02 (0.87) -0.08 (0.45) -0.10 (0.32) 0.07 (0.52) 0.08 (0.46) 1

** Correlation is significant at the 0.01 level (2-tailed) * Correlation is significant at the 0.05 level (2-tailed)

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Spearman‟s Correlation analysis is used to see the relationship between working capital management and profitability. If efficient working capital management increases profitability, one should expect a negative relationship between the measures of working capital management and profitability variable. Table 4 demonstrates result of correlation coefficients and p-values are listed in parenthesis.

3.5 Multiple Regression Statistics

Most sophisticated multiple regression techniques have been applied to study the joint influence of all the selected ratios indicating company's working capital management and performance on the profitability and the regression coefficients have been tested with the help of the most popular „t‟ test. In this study, current ratio, liquid ratio, cash position ratio, debt equity ratio, interest coverage ratio, inventory turnover ratio, debtors‟ turnover ratio, creditors turnover ratio have been taken as the explanatory variables and return on capital employed has been used as the dependent variable.

The regression model used in this analysis is:

ROCE = £ + ß1CR + ß2 LR + ß3 CPR + ß4 DER + ß5 ICR + ß6 ITR + ß7 DTR + ß8 CTR + ß9 WCC + εt (unexplained variables or error terms)

Where £, ß1, ß2, ß3, ß4, ß5, ß6, ß7, ß8 and ß9 are the parameters of the ROCE line. Table-5: Multiple Regression Statistics

Model Unstandised Coefficients t Sig. Collinearity Statistics

ß S.E. Tolerance VIF

Constant CR LR CPR LR DER ICR ITR DTR CTR WCC 6.198 -1.342 10.621 -15.239 -0.294 0.127 -0.054 -0.563 -0.050 0.022 10.767 2.372 13.767 13.858 1.172 0.335 0.089 0.570 0.064 0.026 0.576 -0.566 0.772 -1.100 -0.251 0.379 -0.605 -0.987 0.782 0.840 0.566 0.573 0.442 0.274 0.802 0.705 0.547 0.326 0.436 0.403 - 0.365 0.143 0.211 0.956 0.910 0.971 0.905 0.973 0.803 - 2.739 6.994 4.734 1.046 1.099 1.030 1.105 1.028 1.245

a. Dependent variable: ROCE

Model Summary b

Model R R

Square

Adjusted R Square

S.E. of the estimate Durbin-Watson

1 0.208c 0.043 -0.053 36.2031 2.333

b. Predictors: (constant), WCC, ITR, CPR, DTR, CTR, DER, ICR, CR, LR c. Dependent variable: ROCE

Table-5 reveals that multiple regression results between the dependent and independent variables has been unauthenticated because the result of tolerance and variance inflation factor cannot satisfy the model (even rule of thumb of statistics), that is, VIF value exceeds 2 and also exceeds 5 (rule of thumb of statistics) or tolerance level of 0.50 (rule of thumb in statistics is 0.20). However, first of all, we remove LR from the regression model and set a new model of linear regression.

The new regression model used in this analysis is:

ROCE = £ + ß1CR + ß2 CPR + ß3 DER + ß4 ICR + ß5 ITR + ß6 DTR + ß7 CTR + ß8 WCC + εt

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Table-6: Multiple Regression Statistics

Model Unstandised Coefficients t Sig. Collinearity Statistics

ß S.E. Tolerance VIF

Constant CR CPR LR DER ICR ITR DTR CTR WCC 9.522 0.036 -5.826 -0.350 0.146 -0.056 -0.610 0.048 0.016 9.845 1.558 6.557 1.167 0.334 0.089 0.566 0.064 0.025 0.967 0.023 -0.889 -0.300 0.438 -0.626 -1.078 0.757 0.637 0.336 0.982 0.377 0.765 0.663 0.533 0.284 0.451 0.526 - 0.843 0.939 0.960 0.915 0.971 0.915 0.974 0.885 - 1.187 1.065 1.042 1.093 1.030 1.093 1.027 1.130 d. Dependent variable: ROCE

Model Summary e

Model R R

Square

Adjusted R Square

S.E. of the estimate Durbin-Watson

1 0.192f 0.037 -0.048 36.1225 2.334

e. Predictors: (constant), WCC, ITR, CPR, DTR, CTR, DER, ICR, CR f. Dependent variable: ROCE

Table-6 discloses that multiple regression results between the dependent and independent variables has been authenticated because the result of tolerance and variance inflation factor satisfy the model, that is, VIF value does not exceed 2 after excluding the variable LR.

4. Empirical Analysis and Interpretation

Table-3 shows that three liquidity ratios (CR, LR and CPR) of small-medium private sector Indian steel companies during the period of study is satisfactory as its averages are higher than its grand industry average, which is taken as yardstick. This indicates that they have been able to meet their matured current obligations in every year under the study period. But coefficient of variation of such ratios is very higher than grand industry average. However, solvency ratios (DER and ICR) are more or less better than grand industry average. This indicates that the companies under the study have been able to meet their matured debt obligations in time. Coefficients of variation of such ratios are also higher than industry average. In the matter of the management of liquidity and solvency, it shows less consistency during the study period of these companies. Greater variability indicates improper or less efficient management of fund inasmuch as the excess liquidity could have otherwise been used for investment purposes thereby enabling the company to lead a path of growth. As per Table-3, three efficiency indicators and working capital cycle shows very satisfactory trend as compared to grand industry average. But coefficient of variation shows less consistency in the case of working capital management. It is clear from the study; greater variability indicates improper or less efficient management of inventory policy, collection policy and payment policy. But important indicator of profitability (ROCE) is lower than grand industry average which indicates that working capital management is accountable for lower profitability.

Table-4 reveals that traditional current ratio and profitability is positively lesser (0.026) associated with higher profitability. But other three traditional liquidity ratios of LR and CPR are negatively associated (0.024 and 0.086) with higher and lower probability respectively. This means the result is support the expectation that traditional liquidity ratios are meagerly affect the profitability. Again, DER and ICR are also associated of lower positive and negative correlation coefficient respectively with profitability. This means that result is

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support the anticipation that debt is associated with higher probability. Moreover, the results of correlation coefficient confirm a very low (negative, negative and positive) association among three indicators of efficiency (ITR, DTR and CTR) and ROCE. The result of correlation coefficient shows a very low positive association (0.075) between WCC and ROCE. This means that result is support the expectation that a working capital cycle (WCC) is associated with lower profitability. This reveals that WCC is measuring liquidity differently from the conventional liquidity ratios. Generally, traditional liquidity ratios such as current ratio, liquid ratio, cash position ratio have been understood that have lack in measuring the efficiency of the firm's working capital management. For example is that they incorporate assets that are not readily convertible into cash and ignore the timing of cash conversion (Shin & Soenen, 1998).

The strength of the relationship between the dependent variable, ROCE and all the independent variables taken together except LR and the impact of these independent variables on the profitability are given in Table-6. It was observed from the above that an increase in CR by one unit; the ROCE increased by 0.036 units that were statistically significant at 5 per cent level. However, when CPR increased by one unit, the ROCE of the company decreased by 5.826 units that were statistically significant at 5 per cent level. When DER is increased by one unit, the ROCE of the company is also decreased by 0.350 units and when ICR is increased by one unit, the ROCE of the company is also increased by 0.146 units. Again, three important indicators of efficiency, ITR, DTR and CTR, increased by one unit, ROCE decreased, decreased and increased by 0.056, 0.610 and 0.048 units respectively which was statistically at 5 per cent level. It was observed from the above that an increase in WCC by one unit; the ROCE increased by 0.016 units that were statistically significant at 5 per cent level. The multiple correlations among the dependent variable ROCE and the independent variables taken together were 0.192. It indicates that the profitability was less responded by its independent variables. It is also evident from the value of R2 that 0.037 per cent of variation in ROCE was accounted by the joint variation in independent variables. Adjusted „R‟ square (R2) signifies that 4.80 per cent of the negative variations in the ROCE are explained by the independent variable. Standard Error of regression coefficients being low, demonstrates that there exists really line of estimates among the variables. An insignificant variability in profitability could be the result of the composite effect adopted in the analysis as well as many other working capital management related unexplained variables.

4.1 Test of Hypotheses

To test the hypotheses, following inferences may be drawn from the above-mentioned findings:

Hypothesis 1

Sample Test Correlation Mean S.D. S.E. 95%

confidence interval t d.f. Sig. Lower Upper CR, CPR & ROCE 0.026 (0.80) 0.42 35.31 3.53 -6.58 7.43 0.12 48 0.91

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Hypothesis 2

Sample Test

Correlation Mean S.D. S.E. 95% confidence

interval t d.f. Sig. Lower Upper DER & ROCE -0.024 (0.81) 0.20 35.51 3.55 -6.84 7.25 0.06 49 0.96

The calculated value of t is less than the significant value, hence null hypotheses is accepted. Hypothesis 3

Sample Test

Correlation Mean S.D. S.E. 95% confidence

interval t d.f. Sig. Lower Upper WCC & ROCE -0.075 (0.46) 148.9 157.6 15.76 117.65 180.20 9.45 49 0.00

The calculated value of t is more than the significant value, hence null hypotheses is not accepted.

Hypothesis 4

Sample Test

Correlation Mean S.D. S.E. 95% confidence

interval t d.f. Sig. Lower Upper All independent variables & ROCE 0.037 (0.69) 150.8 156.3 15.62 119.81 181.82 9.65 41 0.00

The calculated value of t is more than the significant value, hence null hypotheses is not accepted.

5. Conclusions

Working capital management is of crucial importance in corporate financial management decision. The optimal of working capital management is could be achieve by company that manage the trade off between profitability and working capital management. The rationale of this study is to explore the working capital management efficiency and profitability association. A descriptive statistics divulges that liquidity and solvency position in terms of debt is very satisfactory and reasonably efficient working capital management is found but liquidity position has no impact on profitability. The study furthermore illustrates there is no association between debt financing and profitability. The study moreover illustrates a stumpy relationship between WCM including working capital cycle and profitability but WCM and working capital cycle has no impact on profitability. Multiple regression tests confirm a lower degree of association between the working capital management and profitability. Consequently, company manger should apprehension on working capital management, particularly unexplained variables in rationale of creation shareholder wealth.

5.1 Limitations of the Study

The study endures from certain limitations.

• Study is purely based on private sector steel companies, we could not compare with the data and information of efficiently managed public sector Indian steel companies.

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References

Books and Journals

1. Bhunia, A, (2007). Working Capital and Liquidity Management of Iron and Steel Enterprises in India: A Comparative Study between SAIL and TISCO. Unpublished UGC Minor Research Project. Eastern Region. Kolkata. p. 3.

2. Eljelly, A. (2004). Liquidity-Profitability Tradeoff: An empirical Investigation in An Emerging Market. International Journal of Commerce & Management, 14(2), 48 – 61.

3. Garcia-Teruel, P. J., & Martínez-Solano, P. (2007). Effects of working capital management on SME profitability. International Journal of Managerial Finance, 3(2), 164-177.

4. Ghosh, S. K. and Maji, S. G. (2003). Working Capital Management Efficiency: A study on the Indian Cement Industry. The Institute of Cost and Works Accountants of India, 11, 179-186.

5. Gill, A, Biger, N and Mathur, N (2010). The Relationship between Working Capital Management and Profitability: Evidence from the United States, Business and Economics Journal, Volume 2010: BEJ-10, 1-9.

6. Lazaridis, I., & Tryfonidis, D. (2006). Relationship between working capital management and profitability of listed companies in the Athens stock exchange. Journal of Financial Management and Analysis, 19(1), 26-35.

7. M. A., Zariyawati, M. N., Annuar and A.S., Abdul Rahim (2010). Effect of Working Capital Management on Profitability of Firms in Malaysia, online from http://www.studymode.com/essays/Working-Capital-Management-426995.html.

8. Mathuva D, (2009). The influence of working capital management components on corporate profitability: a survey on Kenyan listed firms. Research Journal of Business

Management, 3, 1-11.

9. Pawangupta (2010). Working Capital Management. StudyMode.com. Retrieved 07, 2010, from http://www.studymode.com/essays/Working-Capital-Management-355787.html.

10. Rafuse, M.E. (1996). Working capital management: an urgent need to refocus. Management Decision, 34, Issue 2.

11. Raheman, A. & Nasr, M. (2007). Working capital management and profitability – case of Pakistani firms. International Review of Business Research Papers, 3 (1), 279-300.

12. Ramana, N.V and Azash, S.M (2011). A Study on Impact of Working Capital on Profitability Performance - An Investigation in Andhra Cements Ltd, online from www.wptutz.com/.../A-Study-On-Impact-Of-Working-Capital.

13. Sanger, J. S. (2001). Working capital: a modern approach. Financial Executive, 69.

14. Shin, H. H., & Soenen, L. (1998). Efficiency of working capital management and corporate profitability. Financial Practice and Education, 8(2), 37-45.

15. Singh, P. (2008). Inventory and Working Capital Management: An Empirical Analysis. The ICFAI University Journal of Accounting Research,35, 27-41.

16. Smith. (1980). Profitability versus liquidity tradeoffs in working capital management, in readings on the management of working capital. New York. St. Paul: West Publishing Company.

17. Steel Scenario yearbook. (2010). Performance Highlights. Steel Scenario Journal. Kolkata.

18. Van Horne, J. C. & Wachowicz, J. M. (2004). Fundamentals of Financial Management. (12 ed.). New york: Prentice Hall.

Websites

19. http://www.citeman.com/1875-significance-of-working-capital-management.html.

20. http://www.slideshare.net/mvkdel/effect-of-working-capital-on-profitability-in-indian-markets-and-concept-of-zero-working-capital

ijcrb.webs.com

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JANUARY 2012 VOL 3,NO 9 21. www.france-metallurgie.com/index.php/2011/04/29 23. www.rediff.com › Business 24. http://www.ukessays.com/essays/finance/the-impact-of-working-capital-on-firms-profitability-finance-essay.php 25. http://www.indiastudychannel.com/resources/59790-A-comparative-study-Working-Capital-Management.aspx 26. www.investopedia.com/terms/p/private-sector.asp 27. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=924102. 28. www.slideshare.net/Dharan178/ratio-analysis-2970642 29. http://www.docstoc.com/docs/21622736/Effect-Of-Working-Capital-Management-On-Profitability-Of-Firms