1. science
2. mathematics
3. statistics

Journal of Renewable Natural Resources Bhutan
ISSN: 1608-4330
The Relationship between Cash Conversion Cycle and Profitability of
Companies listed in Tehran Stock Exchange (with Emphasis on the Type of
Industry)
Mahmoud Aghajani1, Dr.Amir Mahmoudian*1 , Dr.Ali Zabihi1
1
Department of Management, Sari Branch, Islamic Azad University, Sari, Iran
ABSTRACT
Working capital management is one of the main tasks of the financial manager of the company and has
an important role in achieving the objectives, policies and achievements of the company and increasing
shareholders' wealth. Cash conversion cycle is a measure of working capital management and
controlling this cycle can affect a company's profitability. The relationship between the cash conversion
cycle and profitability of companies listed in Tehran Stock Exchange (with emphasis on the type of
industry) was investigated in this study. The criterion of earnings per share was used to measure the
profitability. The effects of three variables such as firm size, debt ratio and sales growth that were
expected to affect the relationship between the dependent and independent variables were controlled.
The study population is companies listed in Tehran Stock Exchange; three industries of automotive with
16 companies, cement industry with 17 companies and the pharmaceutical industry with 20 companies
were selected from among the entire industries as companies and industries under investigation that
were selected using systematic elimination method. Time period to test the hypothesis include a period
of eleven years based on financial statements from 2002 to 2012. The study has one hypothesis in which
the relationship between the cash conversion cycle and profitability has been tested in three industries
separately. Multivariate linear regression analysis in the combination (firm-year) form with the help of
Spss software has been used to test the hypothesis. Overall, the results indicated a significant inverse
relationship between the cash conversion cycle and profitability in the automotive and cement
industries, but a significant relationship was not observed between cash conversion cycle and
profitability in the pharmaceutical industry.
Keywords: cash conversion cycle, profitability, return on assets, return on equity, earnings per share
Introduction
One of the most important goals of commercial units is maximizing brand equity and increasing
company's stock value and shareholders and management of companies prefer policies that will increase
the company's stock market value. Stock value depends on company's profits. From the perspective of
investors, beneficiaries, creditors and customers also the profitability of the company is considered as
one of the factors affecting decision making (Teruel and Solano, 2006). Profit is considered as one of
the important information in economic decisions. Studies and researches conducted on profit are one of
the most voluminous and most research efforts in the history of accounting. Profit, as dividend payment
guidelines, management effectiveness assessment tool and decision predicting and evaluating tool, has
often been used by investors, managers and financial analysts (Saghafy, 1994). Accordingly, many
researchers have tried to identify the factors affecting the profitability of the company. Considering the
place and importance of capital in the organizational process, its management is of utmost importance.
Meanwhile, the working capital in general allocates a large part of the organization's capital to itself in
all organizations, and its management based on the mechanism of supply chain management is also of
great importance. Working capital management is the optimal combination of working capital items in
a manner that will maximize shareholder's wealth. Managers of business units must choose appropriate
strategies for the management of working capital of their own unit in different situations according to
external and internal factors of the unit and according to the risk and return. However, active working
capital management is a basic requirement of organization's ability to adapt in a challenging economy
and aims to establish a delicate balance between maintaining liquidity to support the daily operations
and maximizing short-term investment opportunities (Hawtis, 2003). In this challenging economic
environment that international organizations seek new ways to grow and to improve financial
performance and to reduce risk, working capital is considered as an important source of profitability.
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2. Statement of the problem
In the current challenging economic environment with increasing environmental pressures and limited
external sources, current assets and liabilities that is working capital of economic firms is of great
importance and optimal management of working capital of firms can be considered as a competitive
advantage for them. Working capital is the total of amounts that is invested in current assets and working
capital management is to determine the size and composition of the sources and uses of working capital
so as to increase shareholders' wealth (Yosefi Talarami, 2012).
Working capital management is one of the most important areas of financial management and
organizations' management, because it directly affects the liquidity and profitability of the company.
There is the probability of bankruptcy for firms that are subject to improper management of working
capital even with positive profitability. Working capital management is concerned with current assets
and liabilities. A significant portion of the company's total assets is current assets. Excessive levels of
current assets can lead to achieve investment returns lower than normal. However, firms that have low
current assets will have shortages and problems in the ordinary course of operations (Rahman and Nasr,
2007).
Any change in the environmental factors of the organization affects working capital immediately. By
medium programming, the possibility of creating balance against changes in environmental factors
should be predicted and balancing the accounts associated with it is very sensitive. Deciding on the
amount of needed inventory, granting business credit to buy or receiving trade credit from suppliers of
raw materials are among the issues that could affect the cash conversion cycle and ultimately affect the
company's profitability (Delft and Mark, 2005). A common criterion for assessing the working capital
management of cash conversion cycle is the time interval between the expenditure for the purchase of
raw materials and receiving the money of goods sold. The longer the interval, the greater investment in
working capital is done. Long cash cycle may increase profitability, because it leads to the increase in
sales. However, if the costs of investing more in the working capital exceed the benefits from keeping
more inventories or granting more trade credit, profitability of the business unit may reduce by
increasing the cash cycle (Panighrahi, 2013). The traditional relationship between the cash conversion
cycle and corporate profitability is such that reducing the cash conversion cycle increases the
profitability of the company. On the other hand, the decrease in the cash conversion cycle can harm the
production operations and product quality of the company and reduce profitability. Identification of the
optimal level of inventory, accounts receivable and payables (the constituent elements of cash
conversion cycle) which minimizes maintenance costs and opportunity costs and recalculating the cash
conversion cycle under the optimum conditions can provide complete and accurate insights into the
efficiency of the management of working capital, which ultimately reduces the cash conversion cycle
and increase profitability. Therefore, in this study it is investigated that whether cash conversion cycle
fluctuations can affect the company's profitability?
3. Research background
Sonen and Shine (1995) investigated the relationship between a measure of the cash conversion cycle
and profitability of the company for a large sample of US firms for the period of 1975-1994. They found
a significant negative relationship between them, which suggests that managers can create value for
shareholders by reducing the cash conversion cycle to a reasonable minimum.
Mark Delft (2003) investigated the relationship between working capital management and profitability
of the company for a sample of 1009 Belgian companies during the period of 1992-1996 and used the
number of days of receiving receipts, inventories and payable accounts as commercial credit criteria
and good inventory procedures. The cash conversion cycle was also used as a comprehensive measure
for working capital management. The results showed that managers can increase the profitability of the
business units through reducing processes of incoming deferred accounts and inventories and similarly,
reducing the cash conversion cycle will increase the company's profitability.
Teruel and Solano (2007) investigated 8872 Spanish small- and medium-sized enterprises during 1996
to 2002 and tested the relationship between working capital management and profitability of small- and
medium-sized enterprises. Research results showed that management can create value for the company
by reducing the number of days in working accounts receivable and inventory of materials and goods.
Shortening the cash conversion cycle also leads to improved profitability.
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Panighrahi (2013) studied the relationship between the cash conversion cycle and Hindi firms'
profitability. His study was had a 10-year period from 2001 to 2010. Corporate profitability has been
measured using two criteria of return on equity and return on assets. His study shows that the cash
conversion cycle has a negative significant relationship with return on assets and return on equity. In
other words, increasing the cash conversion cycle reduces the return on assets and return on equity.
Yaghob Nezhad et al. (2010) examined the relationship between working capital management and
profitability of listed firms in Tehran Stock Exchange. For this purpose, 86 companies were selected
during the period of 2002 - 2007. The results showed an inverse relationship between profitability and
working capital management variables. The results showed that if the collection of receivables period,
the debt-cycle inventory and cash conversion cycle increase, profitability of companies will reduce and
managers can reduce the collection of receivables period, the debt-cycle inventory and cash conversion
cycle to a minimum level and thus create a positive value for shareholders.
Hasani Tabatabai (2011) examined the effects of working capital management on the profitability of
small and medium companies listed in Tehran Stock Exchange. The information of 181 small and
medium companies listed in Tehran Stock Exchange during the period 2005-2009 has been used.
Research findings indicate that there is no significant correlation between the collection of receivables
period, debt paying period, inventory turnover period, and cash conversion period of small companies.
There is no significant correlation between the collection of receivables period, debt paying period,
inventory turnover period, and cash conversion period of medium-sized companies. The findings also
showed that with the introduction of control variables of sales growth and firm size in small and medium
enterprises, correlation coefficients between all variables and profitable were increased. In most cases,
there was no significant relationship between profitability and leverage ratios.
Vaez et al. (2013) conducted a study regarding the factors affecting working capital management in
companies listed in Tehran Stock Exchange. In this study, the affecting factors were profitability,
leverage, capital expenditure and gross domestic product. The results showed that there is a negative
significant relationship between profitability, leverage and capital expenditures with cash conversion
cycle and there is a positive significant relationship between gross domestic product and the cash
conversion cycle.
4. Research objectives, hypotheses and model
The overall objective of this research seeks to explain the relationship between the cash conversion
cycle and profitability of firms listed in Tehran Stock Exchange by industry and the main objective of
the research that is consistent with research title can be expressed as follows:
1. Explaining the relationship between the cash conversion cycle and earnings per share.
Research hypotheses
1. There is a relationship between the cash conversion cycle and earnings per share.
Conceptual model of research variables and regression model for hypotheses testing
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The conceptual model of research variables is as follows:
Earnings per
share
Profita
bility
Cash
conversion
cycle
Firm size
Sales
growth
Debt ratio
Research hypothesis is tested using the following regression model:
EPS i ,t  o  1 CCC i ,t  2  Size i ,t  3  Debt i ,t  4 Growt i ,t   i ,t
In which:
EPS i ,t : Earnings per share for i company in year t
CCC i ,t : Cash conversion cycle for i company in year t
Size i ,t : Size of the for i company in year t
Debt i ,t : Debt ratio of for i company in year t
Growt i ,t : Sales growth of for i company in year t
 i ,t : Residuals of the regression model
Independent variable
Cash Conversion Cycle (CCC) (independent variable):
CCC = debt paying period – (collection of receivables period + inventories turnover period)
Inventory turnover period:
×
Inventory turnover period =
Collection of receivables period:
Collection of receivables period =
Debt paying period:
×
Debt paying period =
×
Dependent variable
Earnings per share (EPS) (dependent variable):
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EPS =
Control variables
Firm size (Size) (control variable):
Size ј,t = Ln (Sale ј,t)
Debt ratio (Debt) (control variable):
Debt i ,t  TLi ,t /TAi ,t
In which:
TL : Total liabilities
TA : total assets
Sales growth (Growth) (control variable):
S i ,t  S i ,t 1
S
i ,t 1
Growth i,t =
Growth i,t = sales growth of i company in year t
S i,t = Net sales of i company in year t
S i,t-1 = Net sales of i company in year t-1
5. Research methodology
This study is applied in terms of its objective and is causal post hoc in terms of its nature and method.
In this study, population includes firms listed in Tehran Stock Exchange from 2002 until the end of
2012. With regard to the duration of the study, the samples shall be selected in a way that they have an
active market during this time in order to be able to test the hypothesis. For this purpose, the following
scheme is used to select samples:
1) Companies should be among automotive, cement and pharmaceutical industries.
2) Company should be listed in Tehran Stock Exchange from the beginning of 2002 to 2012.
3) Fiscal year of the company should end in the final day of the year and company should have not
changed its fiscal year during the years under study and also companies should be among active
companies in Stock Exchange or at least active in days under investigation.
4) Companies should not be among banks and financial institutions (investment companies, financial
intermediation, holding companies, banks and leasing).
5) The information needed to calculate the variables should be available in the years studied.
To determine the sample size, the systematic elimination method has been used; i.e. all the companies
listed in Tehran Stock Exchange were first determined and then the above limitations were applied and
the remaining companies were used as sample. Thus, according to the mentioned limitations, 16
companies from automotive industry, 17 companies from cement industry and 20 companies from
pharmaceutical industry were selected as research sample. In the theoretical foundations' part, method
of collecting data is library method and in the practical part also financial data is used from Tehran
Stock Exchange. For data analysis and hypothesis testing, multivariate linear regression analysis was
used; i.e. first information needed for testing was calculated and then correlation between variables was
investigated using correlation coefficient and then the multivariate linear regression tests was done to
investigate the relationship between cash conversion cycle and companies' profitability.
The Kolmogorov-Smirnov (KS) test was used for normality of data distribution, Durbin-Watson test
for independence of errors and co-linearity test was used for the lack of correlations between the
independent variables.
To test the hypotheses and the analysis of data, Excel software and statistical software of SPSS16 were
used.
6. Research findings
6.1. Descriptive statistics
Descriptive statistics of variables according to the industry are presented in Table (1):
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Industry
CCC
EPS
Size
Debt
Growth
Automotive
Table 1: Descriptive statistics of variables by industry
176
141.6
126.8
1.6a
83.3
1.6
176
611.3
461.2
-692.8a
613.7
-692.8
176
13.58
12.91
12.30
2.05
10.41
176
0.70
0.71
0.73
0.15
0.34
176
0.25
0.16
0.24
0.65
-0.56
352.9
2872.5
18.49
1.07
7.68
CCC
EPS
Size
Debt
Growth
187
183.7
176.9
207.5
89.0
16.7
187
1422.5
993.6
988.2
1244.3
-81.1
187
12.89
12.87
12.32a
0.69
11.09
187
0.60
0.62
0.71
0.15
0.30
187
0.24
0.17
0.14
0.51
-0.69
652.0
5921.1
14.82
0.91
5.21
CCC
EPS
Size
Debt
Growth
220
256.0
255.0
234.9a
78.9
53.7
220
1211.8
1069.1
71.9a
710.0
71.9
220
12.57
12.59
11.70
0.86
10.42
220
0.64
0.65
.65a
0.13
0.33
220
0.26
0.20
0.19
0.47
-0.76
585.2
2987.1
14.46
0.91
4.69
cement
Industry
Industry
pharmaceutical
Statistical
index
Numbers
Observations
Mean
Median
Mode
SD
Minimum
Maximum
Statistical
index
Numbers
Observations
Mean
Median
Mode
SD
Minimum
Maximum
Statistical
index
Numbers
Observations
Mean
Median
Mode
SD
Minimum
Maximum
As can be seen the mean of cash conversion cycle (CCC) is 141.6 for automotive industry, which is less
than the other two industries indicating that this industry can cover its required cash from its sales more
quickly. Earnings per share's (EPS) figure is 1422.5, which is more than the other two industries in the
cement industry. Firm size (Size) mean is 13.58 is more than the other two industries in automotive
industry as expected; debt ratio (Debt) with figure 0.70 is also higher than the rest of the industries in
this industry. Sales growth (Growth) with figure 0.26 is in the first place in pharmaceutical industry and
then with figure 0.25 is in the second place in automotive industry and ultimately in cement industry
with figure 0.24 is in third place.
6.2. Testing normality of data distribution
Kolmogorov-Smirnov (K-S) test has been used to test normality of data.
Automotive
Table 2: K-S test results by industry
industry
Number
Mean
SD
Absolute value
The highest
Positive
deviation
Negative
K-S (Z)
Sig. Level
190
EPS
176
611
613
0.14
0.14
-0.10
1.34
0.056
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Industry
Cement
Number
Mean
SD
Absolute value
The highest
Positive
deviation
Negative
K-S (Z)
Sig. Level
Industry
pharmaceutical
Number
Mean
SD
Absolute value
The highest
Positive
deviation
Negative
K-S (Z)
Sig. Level
EPS
187
1422
1244
0.18
0.18
-0.13
1.17
0.132
EPS
220
1211.80
6710.00
0.09
0.09
-0.05
0.80
0.553
K-S test results show that in automotive industry sig level of EPS variable (0.056) is greater than 5%,
thus the null hypothesis is confirmed; in other words this variable is normally distributed in the
automotive industry. In cement industry sig level of EPS variable (0.056) is greater than 5%, thus the
null hypothesis is confirmed; in other words this variable is normally distributed in the cement industry.
In pharmaceutical industry sig level of EPS variable (0.553) is greater than 5%, thus the null hypothesis
is confirmed; in other words, this variable is normally distributed in the pharmaceutical industry.
6.3. Hypothesis testing
Since this hypothesis is investigated in three automotive, cement and pharmaceutical industries, thus,
this hypothesis is tested for three industries separately.
Statistical hypothesis in the form of null hypothesis (H0) and the opposite hypothesis (H1) are as
follows:
H0: There is no relationship between cash conversion cycle and earnings per share.
H1: There is a relationship between cash conversion cycle and earnings per share.
For hypothesis testing, cash conversion cycle as independent variable and EPS as the dependent variable
and firm size, financial leverage and liquidity ratio as control variables are entered into the multivariate
linear regression equation according to the following model.
EPS i ,t  o  1  CCC i ,t  2  Size i ,t   3  Debt i ,t   4  Growt i ,t   i ,t
6.3.1. Hypothesis testing in automotive industry
Independent variables co-linearity test in automotive industry
Independent variables co-linearity test is given in table (3):
Model
1
Table 3: co-linearity test of the first hypothesis in automotive industry
Dimension
Eigenvalue
Condition index
Constant (Bo )
3.876
1.000
CCC
.867
2.115
Size
.421
4.184
Debt
.219
11.464
Growth
.106
14.953
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As can be seen, eigenvalues are between zero and one indicating the possibility of internal correlation
of predictions, but since they are not so close to zero, it indicates lack of severe problem in using
regression.
Durbin-Watson Test
Investigating errors' independence using Durbin-Watson test and hypothesis testing
Table 4: Durbin-Watson test results and third hypothesis testing in automotive industry
Statistical model: multivariate linear regression model
Industry, Companies and year: Automotive, 16 companies and 11 year
Variable entering method: Enter
Confidence level: 95%
Dependent variable: EPS
Significance of the whole
model of Anova
R square
Durbin-Watson
Adjusted R square
F statistics
Sig.
.192
0.210
1.559
11.374
.000
Significance of each variable
T statistics
Sig
3.415
.001
Independent
variables
Constant value
1456.6
CCC
-1.68
-2.766
.006
Firm size
27.21
1.127
.261
Debt ratio
-468.4
-1.617
.108
Sales growth
355.6
5.397
.000
Beta
According to this table, Durbin-Watson value is 1.559 and regression can be used.
Adjusted coefficient of determination is 0.192 indicating the degree of relationship between
independent and dependent variables.
Significance of the whole model indicates variance analysis between cash conversion cycle and control
variables with EPS. According to the results of multivariate linear regression, H1 is confirmed in
automotive industry with 95% confidence and 5% error probability.
6.3.2. Hypothesis testing in cement industry
First hypothesis co-linearity test in cement industry
Independent variables co-linearity test is given in table (5):
Model
1
Table 5: co-linearity test of the first hypothesis in cement industry
Dimension
Eigenvalue
Condition index
Constant (Bo)
4.016
1.000
CCC
.803
2.236
Size
.436
5.441
Debt
.245
9.495
Growth
.145
12.880
As can be seen, eigenvalues are between zero and one indicating the possibility of internal correlation
of predictions, but since they are not so close to zero, it indicates lack of severe problem in using
regression.
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Durbin-Watson Test
Multivariate linear regression test results are given in table (6).
Table 6: Durbin-Watson test results and third hypothesis testing in cement industry
Statistical model: multivariate linear regression model
Industry, Companies and year: cement, 17 companies and 11 year
Variable entering method: Enter
Confidence level: 95%
Dependent variable: EPS
Significance of the whole
model of Anova
R square
Durbin-Watson
Adjusted R square
F statistics
Sig.
0.69
.089
2.037
4.457
.002
Significance of each variable
T statistics
Sig
4.308
.000
Independent
variables
Constant value
9578.2
CCC
-2.864
-2.522
.013
Firm size
594.8
3.874
.000
Debt ratio
-76.86
-.124
.901
Sales growth
35.49
.197
.844
Beta
According to this table, Durbin-Watson value is 2.037, which shows that errors are independent of each
other and there is no auto-correlation among errors and the correlation hypothesis between errors is
rejected and regression can be used.
Significance of the whole model indicates variance analysis between cash conversion cycle and control
variables with EPS. Since sig. of Anova (.002) with F-statistics (4.457) is less that the acceptable error
level (5%), the linearity hypothesis between the two variables is confirmed.
According to the results of multivariate linear regression, H1 is confirmed in cement industry with 95%
confidence and 5% error probability.
6.3.3. Third hypothesis testing in pharmaceutical industry
First hypothesis co-linearity test in pharmaceutical industry
Independent variables co-linearity test is given in table (7):
Model
1
Dimension
Constant (Bo)
CCC
Size
Debt
Growth
Eigenvalue
4.174
.729
.771
.224
.102
Condition index
1.000
2.393
7.661
11.112
14.663
As can be seen, eigenvalues are between zero and one indicating the possibility of internal correlation
of predictions, but since they are not so close to zero, it indicates lack of severe problem in using
regression.
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Durbin-Watson Test
Multivariate linear regression test results are given in table (8).
Table 8: Durbin-Watson test results and third hypothesis testing in pharmaceutical industry
Statistical model: multivariate linear regression model
Industry, Companies and year: pharmaceutical, 20 companies and 11 year
Variable entering method: Enter
Confidence level: 95%
Dependent variable: EPS
Significance of the whole
model of Anova
R square
Durbin-Watson
Adjusted R square
F statistics
Sig.
.113
0.130
1.872
7.999
.000
Significance of each variable
T statistics
Sig
-2.015
.045
Independent
variables
Constant value
1456.6
CCC
-1.68
-.412
.680
Firm size
27.21
4.846
.000
Debt ratio
-468.4
-1.974
.050
Sales growth
355.6
1.152
.251
Beta
According to this table, Durbin-Watson value is 1.872, which shows that errors are independent of each
other and there is no auto-correlation among errors and the correlation hypothesis between errors is
rejected and regression can be used. Significance of the whole model indicates variance analysis
between cash conversion cycle and control variables with EPS. According to the results of multivariate
linear regression, H1 is confirmed in pharmaceutical industry with 95% confidence and 5% error
probability.
Summary of hypothesis testing
For overall analysis, the results of hypothesis testing are summarized in Table (17-4).
Hypothesis
Table (17-4): summarizing the results of all hypotheses
Hypothesis
Industry
Result
There is a
relationship between
cash conversion
cycle and EPS.
Automotive
Confirmed
Cement
Confirmed
Pharmaceutical
Rejected
Kind of the
relationship
Significant and
inverse
Significant and
inverse
Non-significant
7. Conclusion
The results show that decrease in the cash conversion cycle increases earnings per share in the
automotive and cement industries; in other words, by reducing the cash conversion cycle, automotive
and cement industries' profitability can be increased. Thus, according to the elements of the cash
conversion cycle, it can be concluded that reducing period of receiving accounts receivable, reducing
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conversion cycle and selling good inventory and increasing the time of debt paying leads to increase in
companies' profitability in automotive and cement industries.
Research results show that cash conversion cycle in pharmaceutical industry has no effect on earnings
per share. It should be noted that Iran's pharmaceutical industry is strategically dependent on foreign
raw material, knowledge and technology and is affected by internal and external economic and political
conditions; therefore, it has specific problems and situation. In recent years, cash conversion cycle in
this industry has been so long and the liquidity problem is so fundamental. This industry's performance
is also affected much by international sanctions.
According to the obtained results, it can be said that increasing cash conversion cycle is associated with
decrease in profitability of automotive and cement industries.
Industry type affects companies' cash conversion cycle and causes results in various industries be
different from the expressed theory and given the specific situation of industry, cash conversion cycle
may have no effect on profitability in some industries.
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