A study of selected manufacturing companies listed on colombo stock exchange in sri lanka

European Journal of Business and Management
ISSN 2222-1905 (Paper) ISSN 2222-2839 (Online)
Vol.5, No.27, 2013

www.iiste.org

Factors determining Profitability: A Study of Selected
Manufacturing Companies listed on Colombo Stock Exchange in
Sri Lanka
N.Sivathaasan
Asst. Librarian, University of Jaffna, Sri Lanka
Email: sivathas27@gmail.com
R.Tharanika
University of Jaffna, Sri Lanka
Email: rajatharanika@yahoo.com
M.Sinthuja
Email: sinthujamuthulingam@gmail.com
V.Hanitha
Email: hanithavijeyaratnam@yahoo.com
Abstract
This paper aims to investigate whether factors such as capital structure, working capital, firm size, non-debt tax
shield and growth rate, determining profitability have any impact on profitability of selected manufacturing
companies listed on Colombo stock exchange, Sri Lanka over a period of five years from 2008 to 2012. This
study employs multiple regression analysis to measure relationship among variables, individual and overall
impact on profitability and to test the operational hypotheses. The results revealed that whereas all independent
variables explain 76.6% and 84.7% of the variance on ROA and ROE respectively where significant is at 5%
levels, the overall model has a significant impact on profitability at the rate of 80.5 % (Adjusted R2 = 80.5%,
P<.0.05). Further, while capital structure (+) and non-debt tax shield (+) have statistically significant impact on
profitability (P < 0.05), the remaining working capital (+), growth rate (-) and firm size (+) have no significant
effect on the profitability (P > 0.05).
Keywords: Profitability, Determinants of Profitability, Colombo Stock Exchange
1.
Introduction
Profitability has been given considerable importance in the finance and accounting literatures. According
to Hifza Malik, (2011), Profitability is one of the most important objectives of financial management since one
goal of financial management is to maximize the owners’ wealth, and, profitability is very important determinant
of performance. A business that is not profitable cannot survive. Conversely, a business that is highly profitable
has the ability to reward its owners with a large return on their investment. Hence, the ultimate goal of a
business entity is to earn profit in order to make sure the sustainability of the business in prevailing
market conditions. Pandey (1980) defined the profitability as the ability of a business, whereas it interprets
the term profit in relation to other elements. It is necessary to examine the determinants of profitability to
understand how companies finance their operations. A financial benefit is realized when the amount of revenue
gained from a business activity exceeds the expenses, costs and taxes needed to sustain the activity. Profitability
analysis classifies measures and assesses the performance of the company in terms of the profits it earns either in
relation to the share holders investment or capital employed in the business or in relation to sales, profit, (or
loss). Given that most entrepreneurs invest in order to make a return, the profit earned by a business can be used
to measure the success of that investment. Hermanson (1989) defines that profitability is the organizations’
ability to generate income and its inability to generate income is a loss. He further asserts that if the income
generated is greater than the input cost, that is simply profitability but if the incomes are less than the input cost,
it reflects poor performance.
The study addresses the research question “to what extent do the factors affect profitability of manufacturing
companies in Sri Lanka” and tries to find out the factors which determine profitability of the selected
manufacturing firms listed on Colombo stock exchange. More specifically, the study investigates on the
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relationship between the factors determining profitability and profitability and the impact of those factors on
profitability of the companies. Section 2 of the study reviews previous studies with the development of
hypotheses and section 3 presents the research methodology. Section 4 discusses the empirical results of the
study. Finally, section 5 provides a summary of the findings with main conclusion.
2.
Review of Literature and Development of Hypotheses
Modern literature provides two schools of competing models of firm profitability, such as structureconduct performance (SCP) and firm effect model (Andreas, 2009). In the SCP model, the market
structure determines firm behavior and profitability, whereas in the effect model, market structure is the
results of the distribution of firms and firms profits.
Previous studies have generated many results that
attempt to explain the determinants of profitability of several institutions. There exist a number of determinants
of profitability, out of which this study focuses on capital structure, working capital management, firm size,
growth rate and non-debt tax shield.
Modigliani and Miller (1958) were the first ones to landmark the topic of capital structure. This theory put
forward by Modigliani and Miller (MM) explains the impact of taxation, bankruptcy costs, and agency costs on
the determination of an optimal capital structure. Four theoretical approaches can be distinguished namely the
irrelevance theory such as Modigliani and Miller (1958) , the trade off theory (Bradley et al., 1984), agency cost
theory (Jensen and Meckling, 1976) and pecking order theory (Myers and Majluf, 1984). The three conflicting
theories of capital structure such as trade-off theory, agency cost theory and pecking order theories have been
developed after the establishment of Modigliani and Miller’s theory. Whereas the trade-off, signaling, and
agency theories expect a positive relationship between profitability and leverage, the pecking order theory
predicts a negative one. Most empirical studies observe a negative relationship between leverage and
profitability. The Pecking Order Theory of capital structure (Myers 1984) suggested an inverse relationship
between leverage and profitability. The findings of Kester (1986), Titman and Wessels (1988), Rajan and
Zingales (1995) and Booth et al. (2001), empirically confirm an inverse relation between the leverage ratio and
profitability. Lalith (1999) examines the use and determinants of leverage in a cross section of quoted companies
in Sri Lanka and stated that profitability is reliably negatively correlated to leverage suggesting that more
profitable firms tend to use less leverage. On the other hand, the trade-off, signaling, and agency theories expect
a positive relationship between profitability and leverage. The free cash flow theory (Jensen, 1986) suggested
that debt reduces the agency cost of free cash flow. This theory implies a positive association between leverage
and profitability. In a study carried out by Sangeetha and Sivathaasan (2013), a significant strong and positive
relationship between profitability and leverage (r = 0.569, P<0.01) has been found. Frank and Goyal (2004)
experienced a positive relationship between profitability and leverage in some models. Moreover, various studies
identified the determinants of profitability (Velnampy, 2005 & 2005, 2013).
Working capital management involves the relationship between a firm's short-term assets and its short-term
liabilities. The basic goal of working capital management is to ensure that a firm is able to continue its
operations and that it has sufficient ability to satisfy both maturing short-term debt and upcoming operational
expenses. In relation to the relationship between working capital and profitability, there are mixed results from
different scholars across the world. In the study conducted by KessevanPadachi (2006) on the trend in working
capital management and its impact on firm’s performance, it was found that high investment in inventories and
receivables is associated with lower profitability. A case study conducted by Abdul Kahman and Mohamed
Nasir (2007) on working capital management and profitability of Pakistan firms, a strong negative relationship
between variables of working capital management and profitability of the firm has been observed. The empirical
results found by carpenter and Johnson (1983) revealed that there is a negative relationship between working
capital policies and profitability. While Amir shanh and Sana (2006) pointed out a negative relationship between
working capital and profitability of the firm, Bradley and Michal Rubach (2002) on trade credit and small
business failures suggested that there is a relationship between poor working capital management and
organizational failure. Anupchowdhury and Md. Muntasir Amin (2007) conducted a research on working capital
management practiced in pharmaceutical companies listed on share stock exchange. A positive correlation had
been found in the mathematical model, between current assets management and financial performance of
pharmaceutical firms.
Empirical evidence has given varying results relating to the relationship between firm size and profitability. In
this view, Velnampy and Nimalathasan (2010) examined about firm size on profitability between Bank of
Ceylon and Commercial Bank of Ceylon in Sri Lanka during ten year period from 1997 to 2006 and found that
there is a positive relationship between firm size and profitability in Commercial Bank of Ceylon Ltd, but there
is no relationship between firm size and profitability in Bank of Ceylon. Demsetz (1973) offers an alternative
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explanation for the relationship between firm size and profitability, arguing that the greater profits of large firms
have little or nothing to do with conventional scale economies. Using Internal Revenue Service data, he observes
that large firms earn higher profits in highly concentrated markets while smaller firms earn a normal return. On
contrast, Managerial utility maximization thus provides a conceptual framework for a negative relationship
between ﬁrm size and profitability (Amato and Wilder 1985). Hall and Weiss (1967) who reported that size did
tend to be associated with higher profit rates, however, reached the opposite conclusion. While Marcus (1969)
found either a weak negative relationship or none at all, Hall & Weiss (1967) observed through their studies a
positive association that disappears or reverses itself among the firms with the largest assets.
Trau (1996),
Sutton (1997), and Hart (2000) have reviewed the theoretical and empirical literature on firm growth. In the early
empirical literature, a number of manufacturing studies find either no relationship or a positive relationship
between firm sizes and growth rates. MacMillan and Day (1987) considered that rapid growth could lead to
higher profitability based on evidence that new firms become more profitable when they enter markets quickly
and on a large scale. On the other hand, Hoy (1992) concluded that the pursuit of high growth may be minimally
or even negatively correlated with firm profitability. Keith (1988) examined the relationship between company
characteristics, profitability and growth using accounts data for a sample of 38 small manufacturing firms and his
research revealed that size, age, location, and industry group are found to be limited value in explaining
profitability. The use of growth as a measure of firm performance is generally based on the belief that growth is
a precursor to the attainment of sustainable competitive advantages and profitability (Markman, 2002). In
addition, larger firms have higher rates of survival (Aldrich 1986), and may have the benefits of associated
economies of scale. While growth has been considered the most important measure in small firms, it has also
been argued that financial performance is multidimensional in nature and that measures such as financial
performance and growth are different aspects of performance that need to be considered (Wiklund, 1999)
However, larger firms are found to grow faster than smaller, and younger firms are found to grow faster than
older. In terms of non-debt tax shield, DeAngelo and Masulis (1980) say that non-debt tax shield can be
substitutes for the tax benefits of debt financing and a firm with larger non-debt tax shield is expected to use less
debt. The study conducted by Shah and Khan (2007) stated that size and tangibility has a positive and significant
relationship with Leverage while profitability and non-debt tax shield has significant and negative relationship
with leverage.
This study has tested the following hypotheses in relation to the determinants of profitability of selected
manufacturing companies quoted on Colombo Stock Exchange. Figure 1 clearly presents the relationship
between dependent and independent variables taken in this research.
H1: There is a positive relationship between capital structure and profitability.
H2: There is a positive relationship between working capital management and profitability.
H3: There is a positive relationship between firm size and profitability.
H4: There is a positive relationship between growth rate and profitability.
H5: There is a positive relationship between non-debt Tax shield and profitability.
H6: There is a significant impact of above determinants on profitability.

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Capital Structure
(H1)

Working Capital
(H2)

Determinants of
Firm Size
(H3)

Profitability

Growth
(H4)

Profitability
(ROA & ROE)

Non-Debt Tax
Shield (H5)

Figure 1: Research Model
3.
Research Methodology
This study analyzes the factors determining profitability of selected manufacturing companies quoted on
Colombo Stock Exchange (CSE).
3.1. Sampling Technique
The Colombo Stock Exchange (CSE) has 287 companies representing 20 business sectors as at 01st June 2013;
with a Market Capitalization of Rs. 2,481.8 Bn. This study considers only 11 manufacturing companies out of
listed manufacturing companies. While Titman and Wesseles (1988) adopted three year averages, Rajan and
Zingales (1995) used five year averages. Following Rajan and Zingales, this study used five year averages. The
necessary data was collected from the annual reports of the selected companies for the financial year ended
2008, 2009, 2010, 2011, and 2012 through the link available in the website of CSE.
3.2. Variables in the Study
The most important variable used in this study is the profitability which is the dependent factor. This variable is
broken into two and average is used to find the overall impact of the study. Return on equity (ROE) (Demsetz &
Villalonga, 2001; Gugler et al., 2004a) and return on assets (ROA) (Denis & Denis 1994; Lehmann & Weigand,
2000) are used as tools determining financial performance. Hence, these variables are taken to measure the
profitability.
Variables such as capital Structure, working capital, firm size, non-debt tax shield and growth rate are taken as
determinants of profitability. Table 1 (a & b) clearly shows the design of variables and their measurements
proposed to use.

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Table 1(a): Design of Independent Variable(s)
Variable

Measurement
Used by

Proxy

Capital
Structure

(Sivathaasan,
2013b)

Total Debt/Total equity

Working
Capital

(Pandy,2005)

Current asset:
liabilities

Firm size

(Tanveer
&
Safdar, 2013)

The log of Total Assets

Non debt
tax shield

(Tanveer
&
Safdar, 2013)

Growth
rate

Independent
Variables

Indicator

(Lujie, 2007)

Current

(EAIT + Depreciation) /
average tax rate / total
assets
(Current year’s sales-Last
year’s sales) / Last year’
sales X 100

Table 1 (b): Design of Dependent Variable
(Denis
&
Denis,
1994; Lehmann &
Weigand, 2000)
(Demsetz
&
Villalonga,
2001;
Gugler et al., 2004a)

Return
on
asset(ROA)
Profitability

Return
equity
(ROE)

on

Ratio of profit
before interest and
tax to total assets
Ratio of profit
before interest and
tax to equity

3.3. Model Specification and Mode of Analysis
To find out the impact of the above independent variables on profitability, the following ordinary least squire
(OLS) regression model is used.
Profitability i,t = β0+ β1CS i,t + β2 WC i,t + β3 NDTX i,t + β4 FS i,t + β5 GR i,t +ε
Where;
CS = Capital Structure
WC = Working Capital
NDTX = Non-Debt Tax Shield
FS = Firm Size
GR= Growth rate
β0, β1, β2, β3, β4 β5- Model coefficients
Ε = Error term.
i,t = for firm i in period t
In this study, multiple regression analysis has been employed to analyze the data collected from the companies
listed on CSE. A well known statistical package called “SPSS” (Statistical Package for Social Science) version
16 has been used to analyze the data the researcher collected. The upper level of statistical significance for
hypotheses testing was set at 5%. All statistical test results were computed at the 2-tailed level of significance.
4. Empirical Results
4.1. Analysis of Multi –Collinearity
Before conducting regression analysis, multi-collinearity needs to be checked. There are two major methods
utilized in this study, in order to determine the presence of multi collinearity among independent variables.
These methodologies involved calculation of both a Tolerance test and Variance Inflation Factor (VIF)
(Kleinbaum et.al, 1988, Sivathaasan, 2013a). Menard (1955) suggested that a tolerance value less than 0.1
almost certainly indicates a serious collinearity problem. Furthermore, Myers (1990) also suggested that a VIF
value greater than 10 calls for concern. As per the data shown in table 2, none of the Tolerance level is less than
0.01 and VIF value is well below 10. Therefore, independent variables used in this study do not suggest multicollinearity problem.

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Table 2: Collinearity Statistics
Independent Variables

Tolerance

VIF

Capital structure

.306

3.266

Working capital

.584

1.712

Firm size

.577

1.732

Non debt tax shield

.269

3.711

Growth rate

.772

1.295

4.2.
Multiple Regression Analysis
The purpose of regression analysis is to relate a dependent variable to a set of independent variables (Mendenhal
and Sincich, 1993; Nilufar et al., 2009). Table 3(a & b) provides the impact of all independent variables on ROA
and ROE separately. All independent variables explain 76.6 % and 84.7 & of the variance on ROA and ROE
respectively, where significant is at 5% levels (P < 0.05).
Table 3(a): Regression Results (Individual) on ROA

2

Variables

R


.883

a.
b.

Adjusted R

2

P-Value

7.559

.766

F value

.022

Predictors: (Constant), Firm Size, Growth rate, Capital structure, Working capital, Non debt tax
shield
Dependent Variable: ROA

Table 3(b): Regression Results (Individual) on ROE
2

Variables

R


.923

a.
b.

Adjusted R
.847

2

F value

P-Value

12.059

.008

Predictors: (Constant), Firm Size, Growth rate, Capital structure, Working capital, Non debt tax
shield
Dependent Variable: ROE

Table 4 presents the results of predictors of profitability of selected manufacturing companies in Sri Lanka. In
addition, data can be analyzed using multiple linear regressions to test the hypotheses (Hair et al., 1998, Nilufar
et al., 2009). The regression coefficient of determinants on profitability has been estimated. As shown, the
overall model is significant at the 5% level (t =9.272, P < 0.05). Of the independent variables, capital structure
(+) and non-debt tax shield (+) have statistically significant impact on profitability. The remaining working
capital (+), growth rate (-) and firm size (+) have no significant effect on the profitability.
Table 4: Regression Results (Overall) on Profitability
Independent

Variables

Capital structure
Working capital
Non debt tax shield
Growth rate
Firm Size
2
R
2
Adjusted R
P-Value
Model F-Test

Coefficients
(Beta)
.260
.305
.152
-.049
.125
.903
.805
.014
9.272

104

t-statistics

P-Value

4.997
1.672
4.285
-.309
.678

.004
.155
.008
.770
.528

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The results of the study shows that the association between capital structure and profitability is significant with β
= 0.260 (P < 0.05). Hence, H1 is accepted. Several studies have highlighted positive association between these
two variables (Goyal, 2004; Sangeetha & Sivathaasan, 2013). Mixed results have been given through scholars in
relation to the relationship between working capital and profitability. This study provides positive link with
profitability (β = 0.305, P > 0.05) insignificantly. This supports to the acceptance of hypothesis of H2.
Hypothesis (H3) is in line with the results found by Velnampy and Nimalathasan (2010). As per the above table,
positive relationship is shown between firm size and profitability (P > 0.05), which supported to H3. Hypothesis
(H4) reflecting positive connection between growth rate and profitability is not in agreement with the findings of
Hoy (1992), as negative correlation is presented. Therefore, H4 is rejected. With regard non-debt tax shield, the
study found a positive and significant relationship between non-debt tax shield and profitability (β = 0.152, P<
0.05). Further, Five independent variables incorporated in this model explain 80.5 % (Adjusted R2) of the
variance in the profitability. This supports to the hypothesis H6 that there is a significant impact on profitability.
As the overall model is statistically significant, the model adequately describes the data.
5.
Conclusion
This paper empirically examined the relationship between the factors determining profitability and profitability
of selected manufacturing companies in Sri Lanka for the period of 2008-2012 by using the multiple (individual,
overall) regression analysis. Regression results (individual) on ROA and ROE were 76.6% and 84.7% (Adjusted
R2) respectively where significant is at 5% levels (P< 0.05). Further, these results revealed that overall model has
an impact on profitability at the rate of 80.5% (Adjusted R2) which was measured by capital structure, working
capital, firm size, non debt tax shield and growth rate. Based on the regression analysis, the capital structure and
non-debt tax shield have statistically significant and positive impact on profitability. Though other variables
such as working capital and firm size have shown positive relationship, they are not significant at 5 % levels.
Moreover, the growth rate has shown the negative insignificant relationship on the profitability. While the study
is limited to the sample of selected manufacturing companies quoted on the Colombo stock exchange, the
findings from this research could be generalized to the companies similar to this category.
6.

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