IMPACT OF PROFITABILITY ON THE …apjor.com/files/1394947512.pdfIMPACT OF PROFITABILITY ON THE...

Asia Pacific Journal of Research Vol: I Issue XI, March 2014

ISSN: 2320-5504, E-ISSN-2347-4793

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IMPACT OF PROFITABILITY ON THE DETERMINANTS OF WORKING

CAPITAL: AN EVIDENT STUDY OF LARGE STEEL MANUFACTURING

COMPANIES IN INDIA

C.Srinivas Yadav **and Sai Shiva Kumar S.B *

** Asst.Professor, Dept.of commerce, Sri Sathya Sai Institute of Higher Learning, Brindavan

Campus,Kadugodi,Bangalore,560067.

*Student (II MFM Class), Dept.of commerce,Sri Sathya Sai Institute of Higher Learning,

Brindavan Campus,Kadugodi,Bangalore,560067.

ABSTRACT

The present research shows the relationship between working capital management determinants

on profitability. Profitability is a dependent variable where as determinants of working capital are

independent variables such as Average Collection Period, Inventory Turnover in days, Average

Payment Period, Cash Conversion Cycle, and Net Trading Cycle were used to assess working

capital management, and Return On Total Assets.

The study has considered sample of the size of ten large scale steel manufacturing companies in

India over a ten year period from 2003 to 2013. The analysis done by using OLS Regression ,

show whether there is a significant relationship between this variables .

Keywords: Working capital management, Return on Total Assets, Average collection period,

Inventory turnover in days, Average payment period.

Introduction:

Working Capital management is important function of finance, takes care of the financing

activities, liquidity position and profitability position of the firm.

Deloof (2003), investigated the effect of working capital management on corporate profitability in

Belgium. He studied a sample of 1009 firms that were essentially non-financial. The study was

conducted for a period of four years i.e. 1992-1996. The variables studied were gross profit margin as a

proxy for profitability, Number of days accounts receivable is calculated as [account

receivable×365]/sales. Number of days inventories is [inventories ×365]/cost of sales. Number of day’s

accounts payable is [accounts payable×365]/purchases. The cash conversion cycle, defined as number

of days accounts receivable + number of days inventory number of days accounts payable was used as a


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comprehensive measure of working capital management. The author used In addition, size, sales

growth, the financial debt ratio (financial debt/total assets), and the ratio of fixed financial assets to total

assets as control variables in the regressions. The methodology adopted was a descriptive statistical

analysis. Based on the studies, the author concluded that the coefficient of the accounts receivable

variable is negative and highly significant. Also regression shows a very significant negative relation

between gross operating income and number of day’s accounts payable confirming the negative

correlation between operating income and number of day’s accounts payable.

Bhunia, Khan (2011) analyzed the association of liquidity management, in other words, working capital

management and profitability of steel companies in the private sector in India. The main objectives of

the authors were to study the overall efficiency of management of short term liquidity and its relation to

profitability. The authors studied a sample of 230 steel companies in the private sector in the Indian

industry and studied them over a period of eight years i.e. 2002-2010. The dependent variable is defined

as the profitability of the sample firms. The independent variables used were the following Current

Ratio (CR), Liquid Ratio (LR), Absolute Liquid Ratio (ALR), Debt-Equity Ratio (DER), Interest

Coverage Ratio (ICR), Inventory Turnover Ratio (ITR), Debtors Turnover Ratio (DTR), and Creditors

Turnover Ratio (CTR). After doing multiple regression analysis, Correlation analysis and Descriptive

statistics, the authors concluded that liquidity and solvency position in terms of debt is very satisfactory

and relatively efficient liquidity management is found but liquidity position has no impact on

profitability.

Pimplapuri, Kulkarni (2011) investigated the twin objectives of liquidity and profitability of the firm by

analyzing the effect of working capital management on profitability. The objectives of the authors were

to study effect of working capital management on profitability of Bharat Petroleum Corporation Ltd and

also study the working capital leverage effect on profitability. The period of study was for five years

vis., 2005-2010. The data was essentially collected from the Annual reports published by the company

from time to time. The ratios which have been applied for highlighting the efficiency of working capital

management are, Current Ratio(CR), Quick Ratio(QR) , Current Assets to Total Assets Ratio(CATAR),

Current Assets to Sales Ratio(CASR),Working Capital Turnover Ratio(WCTR), Inventory Turnover

Ratio(ITR ), Debtors Turnover Ratio (DTR) and Cash Turnover Ratio (CTR) and the measure of

profitability that has been selected is Return on Investment ( ROI). The tools that were used are mainly

the Karl Pearson’s Correlation and Multiple Correlation. The effect of leverage used was measured

through the working capital leverage has been used. The authors concluded that there is a negative

association between the profitability and the current ratio of the company and the correlation coefficient

is found to be statistically significant. Also they found that there is a negative correlation between the

profitability of the company and the ratio of current assets to total assets and hence had a significant

impact on the profitability of the firm.

Khwaja et al investigated the dependence of profitability on the management of working capital. The

objectives of the was to study the impact of working capital components on the profitability of the firm

on the companies in Asia. For the purpose of the study, a sample of 332 listed manufacturing companies

was taken for a period of 5 years i.e. 2006-2010. The methodology used was the panel data analysis in

which the descriptive statistics are used. The independent variables used were, Cash Conversion Cycle

defined as time in days between when firms pay their payables and receive receivables. It is average

number of days to convert raw materials into finished products and then selling them to customers.

Inventory period is calculated by dividing average inventory by average sales per day. Then we have the

control variables, Short Term Liquidity affects profitability of firms, to keep its effect neutral current


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ratio was used as the control variable. Size of firm affects profitability; to keep size as constant factor,

natural logarithm of sales as control variable was used. Short Term financial assets are used to obtain

short term profits, these assets vary company to company. In order to keep its effect neutral, financial

assets to company’s total asset ratio was used as control variable. To keep the debt utilization effect

constant debt to asset ratio is taken as control variable.

Mittal et al (2010), studied the examined the trends in working capital management taking the case of

the cement industry in India as their sample. He studied a sample of two companies namely Gujarat

Ambuja Cements Ltd (GAC Ltd) and Associated Cement Companies Limited (ACC Ltd), considered

the market leaders and principal competitors. The period of study was for four years, 2006-2009. The

data used was the secondary data that was collected from the company balance sheets. The objectives of

the study were to understand the size of the working capital in the cement industry in India and find

trends in the working capital management in the industry. The different variables studied were sales,

total assets, and net profit.

This is a descriptive study based on case study methodology of the Indian cement Industry. The various

statistical tools like mean, standard deviation, coefficient of variation, correlation, multiple regression

and multiple correlation were used. Based on the detailed study and the comparison of both the firms, it

was concluded that there was a insignificant relation between the size of the working capital of the

firms. The other conclusion made by the authors was that, though there exists a significant relation

between the components of working capital and profitability, the firms did not manage their working

capital well. The last conclusion, based on the comparison was that the cement industry in India was not

maintaining adequate amounts of working capital.

Bhagchi, Kamrui (2012) studied the effect of working capital management on profitability on the

FMCG sector in India. The study was conducted on a sample of ten FMCG firms over a period of ten

years 2000-01 to 2009-10. The main objective of the authors was to understand the impact of working

capital management on profitability and to see the impact of various components of working capital

management on profitability. After conducting normality tests, Pearson’s Correlation and panel data

regression, the authors concluded that there was a significant negative relation between working capital

management and firm profitability.

Research Methodology

Research Gap:

Working capital management was found to have an impact on almost all the industries. The literature

review consists of various industries and across all the industries. The literature reviewed also shows

that there is consistency across the findings of various authors across various countries. However, there

has not been any study except by (Amalendu Bhunia, 2011), which has studied the impact of working

capital management on the steel industry a panel sample but these study deals with each large

manufacturing company India, individual and correlate data with the industry.

Statement of the problem:

The study shows the relationship between working capital management determinants on profitability.

Profitability is a dependent variable where as determinants of working capital are independent variables

such as Average Collection Period, Inventory Turnover in days, Average Payment Period, Cash


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Conversion Cycle, and Net Trading Cycle were used to assess working capital management, and Return

On Total Assets, company by company in order get desired results.

Need and significance of the study:

The study helps especially for the creditors and other stake holders to known the

short term liquidity position of the company and profitability relationship in the steel

manufacturing companies in India.

SCOPE OF THE STUDY: The scope of study is limited to ten large steel manufacturing in India and historical data of ten

years 2003-2013.

Objectives:

1. To study the working capital components of the steel industry in India.

2. To study the impact of working capital management components on profitability of the

steel industry in India.

Hypotheses:

This study tries to prove the following hypotheses:

H1- Cash Conversion Cycle does not have effect on profitability of the firm.

H2- Net Trading Cycle has no effect on profitability of the firm.

H3- Gross Working Capital Turnover Ratio does not have effect on profitability of the firm.

H4- Current Asset to Total Asset Ratio does not have effect profitability of the firm.

H5- Current Liabilities to Total Asset Ratio does not have effect profitability of the firm.

H6- Current Ratio Does not have effect on profitability of the firm.

H7- Financial Debt Ratio does not have effect profitability of the firm.

Sample:

The following is the sample used for the study.

1. Bhushan Power and Steel Ltd.

2. Bhushan Steel Ltd.

3. Essar Steel Ltd.

4. Jindal Saw Ltd.

5. Jindal Stainless Steel Ltd.

6. JSW Ltd.

7. Rashtriya Ispat Nigam Ltd.


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8. Steel Authority of India Ltd.

9. Tata Steel Ltd.

10. Uttam Galva Ltd.

Data sources:

Data is collected from various steel manufacturing company website for annual reports and capital

line database websites. Other sources of data is collected from ebscohost websites, text books,

magazines, journals and other library sources (online and offline).

STATISTICAL TECHNIQUES The data has been analysed essentially using ratio analysis. OLS Regression is used to identify

relationship between the variables and state hypothesis, statistically significant at a confidence level of

95%.

The independent variables studied in this are Cash Conversion Cycle (CCC) defined as (No. of Days

Accounts Receivables + No. of Days Inventory) – No. of Days Accounts Payable, Interest Coverage

Ratio (ICR) calculated as PBIT/ Interest, Debt-Equity Ratio (DER) given as External Equities or

debts/Equity capital, Age of Inventory (AI) given by (Average Inventory/Average Cost of Sales) x 365

days, Age of Debtors (AD) defined as (Average Debtors/Average Annual Credit Sales) x 365 days, Age

of Creditors (AC) calculated as (Average Creditors/Average Cost of Sales) x 365 days.

The dependent variable is Return on Assets (ROA) given as PBIT/ Total assets.

NOP it = β0 + β1 (CCC it) + β2 (NTC it) + β3 (GWCTR it) + β4 (CATAR it) + β5 (CLTAR it)

+ β6 (FDR) + β8 (CR it) + ε it

PERIOD OF THE STUDY The ratios were calculated based on the figures provided in the balance sheet and profit and loss

statements of the companies for a period of ten years, viz., 2003- 2013.

LIMITATION OF THE STUDY:

The study consists of only ten years of data that might not be sufficient to establish the relation

in a very significant manner.

The sample size of ten companies, all from the large steel manufacturing companies might not

be adequate to represent the entire Industry.

There might be some data that is not publicly available, that could affect the analysis in a

significant manner.

The study considers only secondary data but not primary (i.e., interaction with the executives

in finance department would close picture and management style etc is not considered).


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Data analysis and interpretation:

Table 1. Table Showing Regression Analysis for the Industry.

Coefficients Standard

Error

t Stat P-value

Intercept 1.093 0.507 2.1558 0.1639

CCC 0.002 0.001 1.4609 0.2815

NTC -0.001 0.000 -1.2901 0.3261

GWCTR -0.124 0.095 -1.3116 0.3200

CATAR -0.115 0.344 -0.3329 0.7709

CLTAR -0.218 0.547 -0.3981 0.7290

FDR -0.408 0.130 -3.1317 0.0886

CR -0.182 0.137 -1.3282 0.3154

From the above table that summaries the regression analysis, it can be seen that there is a negative

relation explained by the negative sign between the Net Trading Cycle, Gross Working Capital

Turnover Ratio, Current Assets to Total Asset Ratio, Current Liabilities to Total Assets Ratio,

Financial Debt Ratio, Current Ratio and Return on Total Assets. This means, a decrease in any of

the variables would lead to an increase in the Return on Total Asset. This finding is consistent with

the findings of (Ray, 2012), (Sachin Mittal, 2012), (Hyder Ali Khawaja, 2012) and many others.

But another important thing to be kept in mind is that the p-values suggest that these variables are

not statistically significant at a 95% confidence level. These findings are again consistent with the

findings of (Amalendu Bhunia, 2011), who studied select companies in the steel industry in India

Company Wise Analysis:

Bhushan Power and Steel Ltd:

Table 2. Table Showing Regression Analysis for Bhushan Power and Steel Ltd


Error

t Stat P-value

Intercept 0.4834 0.5184 0.9326 0.4495

CCC 0.0002 0.0009 0.2275 0.8412

NTC -0.0002 0.0004 -0.4984 0.6676

GWCTR 0.0101 0.0593 0.1710 0.8800

CATAR 0.6351 0.6319 1.0052 0.4207

CLTAR -1.0287 1.3889 -0.7407 0.5360

FDR -0.4570 0.3836 -1.1913 0.3557

CR -0.0466 0.0849 -0.5484 0.6384


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The above table is the analysis of Bhushan Power and Steel Ltd. This has an r-square of 98.47%.

This table however differs from the findings in the Industry. This table shows that there is a

negative relation between Return on Total Assets and Net trading Cycle, Current Liabilities to

Total Asset Ratio, the Financial Debt Ratio and the current ratio. This means that a drop in these

ratios can increase the profitability. The other ratios have a positive relation with profitability,

which means an increase in these ratios could increase in profitability.

At a confidence level of 95% however, these variables are not statistically significant. Significance

can be defined as any p-value lower than 0.05.

Bhushan Steel Ltd:

The following is the table summarising the regression analysis of Bhushan Steel.

Table 3. Table Showing Regression Analysis For Bhushan Steel Ltd

Coefficients Standard Error

t Stat P-value

Intercept -0.292 0.127 -2.299 0.148

CCC 0.000 0.001 -0.163 0.886

NTC -0.001 0.001 -0.735 0.539

GWCTR 0.061 0.014 4.528 0.045

CATAR 0.593 0.401 1.481 0.277

CLTAR -0.630 0.828 -0.761 0.526

FDR 0.228 0.182 1.248 0.338

CR 0.030 0.064 0.472 0.684

In this case, it can be seen that only the Net Trading Cycle and Current Liabilities have a negative

relation between the dependent variable and independent variable. At 95% confidence level, the p-

value of Gross working capital ratio has statistical significance and a positive relation. This means

that an increase in the gross working capital ratio can lead to increase in profitability.

Essar Steel:

The following is the table summarising the regression analysis of Essar Steel Ltd.

Table 4. Table Showing Regression Analysis for Essar Steel Ltd

Coefficients

Standard

Error t Stat P-value

Intercept -0.6873 0.1675 -4.1029 0.0546

CCC 0.0031 0.0011 2.7425 0.1112

NTC -0.0027 0.0010 -2.8293 0.1055

GWCTR -0.0227 0.0338 -0.6719 0.5708

CATAR 0.8216 0.5192 1.5824 0.2544

CLTAR 1.2072 0.6180 1.9534 0.1900

FDR 0.0036 0.0780 0.0462 0.9673

CR 0.1817 0.0857 2.1214 0.1679


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This table shows the negative relation between Net Trading Cycle and Gross Working capital

ratio. This means, an inverse relation between dependent variable and the independent variables.

However, at 95% confidence level, none of the ratios are statistically significant.

Jindal Saw Ltd:

The following table shows the regression analysis of Jindal Saw Ltd

Table 5. Table Showing Regression Analysis for Jindal Saw Ltd


Error

t Stat P-value

Intercept -0.3424 0.6288 -0.5444 0.6407

CCC 0.0003 0.0044 0.0620 0.9562

NTC -0.0005 0.0044 -0.1105 0.9221

GWCTR 0.1448 0.0948 1.5267 0.2664

CATAR 0.3163 0.5031 0.6288 0.5937

CLTAR 0.1210 1.0902 0.1110 0.9218

FDR -0.2913 0.4051 -0.7191 0.5467

CR 0.0515 0.1867 0.2757 0.8086

The table shows that there is a negative relationship between the net trading cycle and the financial

debt ratio. This means, a drop in either of the variables would increase returns. Again, with a 95%

confidence level, there is no variable that is significant

Jindal Stainless Steel Ltd:

Given below is the summary table that shows the regression analysis of Jindal Stainless Steel Ltd.

Table 6. Table Showing Regression Analysis of Jindal Stainless Steel Ltd.


Error

t Stat P-value

Intercept 0.5902 0.7680 0.7685 0.5225

CCC 0.0068 0.0032 2.1233 0.1677

NTC -0.0085 0.0045 -1.8756 0.2015

GWCTR -0.0052 0.0183 -0.2849 0.8025

CATAR 1.8646 2.6100 0.7144 0.5491

CLTAR -1.7670 3.3274 -0.5310 0.6485

FDR -0.7502 0.8836 -0.8490 0.4853

CR -0.1380 0.8459 -0.1631 0.8854


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This shows that there exists a negative relation between the net trading cycle, gross working

capital ratio, current liabilities to total asset ratio, the financial debt ratio and the current ratio.

However, these ratios do not have a p-value of less than 0.05, at a confidence level of

JSW Ltd:

The following is the summary of regression analysis of JSW Ltd.

Table 7. Table Showing Regression Analysis of JSW Ltd


Error

t Stat P-value

Intercept -0.1052 1.0930 -0.0962 0.9321

CCC 0.0013 0.0020 0.6582 0.5780

NTC -0.0005 0.0022 -0.2534 0.8236

GWCTR 0.0700 0.0974 0.7191 0.5468

CATAR -0.5005 1.9581 -0.2556 0.8221

CLTAR -0.0028 2.1247 -0.0013 0.9991

FDR -0.0493 0.6731 -0.0732 0.9483

CR 0.2925 0.7644 0.3826 0.7388

The variables that have negative relationship with profit are, the net trading cycle, Current Asset to

Total Asset Ratio, Current Liabilities to Total Asset Ratio and the financial debt ratio. The p-

values again show that there is no statistically significant ratios that are present.

Rashtriya Ispat Nigam Ltd:

Given below is the regression analysis summary of Rashtriya Ispat Nigam Ltd

Table 8.Table Showing Regression Analysis of Rashtriya Ispat Nigam Ltd


Error

t Stat P-value

Intercept -2.3059 1.9393 -1.1891 0.3564

CCC -0.0009 0.0035 -0.2549 0.8226

NTC 0.0009 0.0030 0.2995 0.7928

GWCTR 0.5430 0.3619 1.5003 0.2723

CATAR -0.2576 0.7356 -0.3501 0.7597

CLTAR 4.6707 4.0434 1.1551 0.3674

FDR -1.2625 0.5111 -2.4701 0.1322

CR 0.3307 0.2139 1.5459 0.2622


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The variables that have negative relation here are, the cash conversion cycle, the Current Asset to

Total Assets Ratio and the financial debt ratio. The analysis shows that there are no statistically

significant ratios that can affect profitability.

Steel Authority of India Ltd:

The following is the regression analysis of Steel Authority of India.

Table 9. Table Showing Regression Analysis of Steel Authority of India Ltd


Error

t Stat P-value

Intercept -2.2398 1.7463 -1.2826 0.3282

CCC 0.0105 0.0057 1.8327 0.2083

NTC -0.0072 0.0048 -1.4996 0.2725

GWCTR 0.4213 0.3340 1.2615 0.3343

CATAR 0.8129 1.5043 0.5404 0.6431

CLTAR 0.6701 2.0407 0.3284 0.7738

FDR 0.5809 1.3489 0.4306 0.7087

CR 0.3082 0.3756 0.8204 0.4982

Here only the net trading cycle has a negative relation. But there are no statistically significant

ratios.

Tata Steel Ltd:

The table that is presented shows the summary of regression of Tata Steel Ltd.

Table 10Table Showing Regression Analysis of Tata Steel Ltd


Error

t Stat P-value

Intercept 0.5261 0.0136 38.7861 0.0007

CCC -0.0001 0.0000 -2.1540 0.1641

NTC 0.0005 0.0000 15.7790 0.0040

GWCTR -0.0473 0.0057 -8.3557 0.0140

CATAR 0.3460 0.0535 6.4671 0.0231

CLTAR 1.0436 0.0614 16.9856 0.0034

FDR -0.7348 0.0189 -38.9211 0.0007

CR -0.0780 0.0071 -10.9568 0.0082

Here the relation between cash conversion cycle, the gross working capital ratio, the financial debt

ratio and the current ratio with Returns on Total Asset is negative. This means, an inverse relation

between the dependent and independent variables. In this company however, the all the ratios

except cash conversion cycle are statistically significant at a confidence level of 95%.


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Uttam Galva Ltd:

The following table shows the regression analysis of Uttam Galva Ltd.

Table 11Table Showing Regression Analysis of Uttam Galva Ltd


Error

t Stat P-value

Intercept -1.2672 1.3902 -0.9115 0.4582

CCC 0.0020 0.0033 0.5998 0.6096

NTC -0.0039 0.0035 -1.1240 0.3778

GWCTR 0.1174 0.0503 2.3360 0.1446

CATAR 0.0656 1.5028 0.0437 0.9691

CLTAR 0.9088 1.9651 0.4625 0.6892

FDR -0.0731 0.8924 -0.0819 0.9422

CR 0.5324 0.9680 0.5500 0.6375

Here the variables that have negative relation with return on total assets are the Net Trading

Cycle and the Financial Debt Ratio. This is subject to the fact that, at a 95% confidence level, the

variables are not statistically significant.

Conclusion:

After a careful analysis of the data, it was found that none of the values were statistically significant.

This means, the null hypotheses of the six variables get accepted. On analysis of the individual sample

companies, we see the same pattern, which only goes on to prove the study on the industry. This only

shows that, in the steel industry, the working capital management does not have effect on profitability.

These studies are consistent with the findings of (Amalendu Bhunia, 2011), Ching et al (2011).

However, this goes against the findings of many researchers like Shin & Soenen (1998), Padacchi

(2006). But the difference being, while the former studied firms across several industries with several

samples, the latter studied the impact on micro and small industries in Mauritius.

However, we can see that there is one exception in the sample, Tata Steel Ltd. On careful analysis of

the sample, we find that all the ratios except the cash conversion cycle are significant. This shows that,

when taken individually, working capital management has an impact on profitability of the firm. It is

also important to note that Tata Steel Ltd is the lowest cost steel producer in the world1. When these

facts are taken together, we conclude that efficient management of working capital does impact the

cost and therefore profitability.

From the study, though it is evident that working capital management does not have a significant

impact on profitability, the importance of working capital management cannot be ignored. The steel

companies must ensure that there is a balance maintained between liquidity and profitability of the

firms. Some companies like Uttam Galve Ltd can reduce their current asset to total asset ratio that

might help it to maintain liquidity position. Tata Steel Ltd and Essar Steel Ltd have a negative cash

1 Source: equitymaster.com


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conversion cycle, which might be desirable for any firm. Uttam Galva Ltd has the most balanced cash

conversion cycle, Tata Steel and Essar Steel have a positively unbalanced cycle, leading to a negative

cash conversion cycle. It is often a strategy that is employed by firms to delay payables as long as

possible and take collections as fast as possible. But, this must be done with caution as creditworthiness

can be impacted.

Scope of further study:

This study can be improved further by:

Identifying a larger sample (i.e., small and large manufacturing company) from the steel

industry.

Taking the regression of individual components of the cash conversion cycle on returns figure.

The can be extended by taking data over a four to five decades period of time.

Incorporating studies of the impact of non-quantitative data like supply chain management on

working capital management that can effect profitability.

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