Proceedings of 5th Asia-Pacific Business Research Conference
17 - 18 February, 2014, Hotel Istana, Kuala Lumpur, Malaysia, ISBN: 978-1-922069-44-3
The Determinants of Profitability: Evidence from Malaysian
Construction Companies
Noor Azila Mohd Zaid *, Wan Muhd Faez Wan Ibrahim ** and Nurul Syaqirah
Zulqernain***
This study attempts to examine the determinants public based constructions companies'
profitability in Malaysia from 2000-2012. This study used the return on equity (ROE) to
measure profitability of company; debt-equity ratio to measure capital structure; quick
ratio to measure liquidity; sales used to measure the size of company and term premium
to measure the economic cycle. Overall, the result showed that the liquidity and size have
significant relationship with profitability. The negatively insignificant relationship is found
between capital structure with profitability. We also attempt macro aspect variables of
term premium, interest rate and Gross Domestic Product (GDP) and found nonsignificant relationship of all macro aspect variables.
Keywords: Firm Profitability, Construction, Malaysia
Field of research: Finance
1. Introduction
The Malaysian Economy continues to be resilient showing encouraging growth due to the Ninth
Malaysian Plan (9MP, 2006 – 2010). The growth of the construction sector recovered after
experiencing three consecutive years of decline, recording a growth of 4.6% (2006: -0.5%). Only
construction sector recorded positive growth during every quarter of 2009.
The civil engineering sub-sector was the major contributor as a result of the 9 th Malaysia Plan
(9MP) projects executed expediently in 2007. Federal Government development expenditure
increased to RM 40.6 billion due to the funding of building and improvements of infrastructure such
as, schools, hospitals, and government living quarters. The growth of non-residential segment also
increased stimulated by the increase in demand for office and retail space. Activities in the
residential sub-sector continued to remain positive, supported by residential property transactions
with foreign citizens which increased year by year until 2012. This was due to the Government’s
efforts in liberalising property purchases, Property Gains Tax exemptions and relaxation of
residential property borrowings allowing foreigners to also obtain loans for the purpose.
The original allocation for 9MP, totaled RM220 billion promised an improved performance for
Malaysia's economy. Currently, Malaysia is at the edge of implementing its 10th Malaysia Plan
(10MP). The plan is the benchmark of setting stage of major national structural transformation for
* Mrs Noor Azila Mohd Zaid, Department of Finance, UniversitI Teknologi MARA, Malaysia. Email:
azila37@kelantan.uitm.edu.my
** Mr Wan Muhd Faez Wan Ibrahim, Department of Finance, UniversitI Teknologi MARA, Malaysia. Email:
wanfaez@kelantan.uitm.edu.my
*** Mrs Nurul Syaqirah Zulqernain, Department of Marketing, UniversitI Teknologi MARA, Malaysia. Email:
syaqirah@kelantan.uitm.edu.my
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Proceedings of 5th Asia-Pacific Business Research Conference
17 - 18 February, 2014, Hotel Istana, Kuala Lumpur, Malaysia, ISBN: 978-1-922069-44-3
high income economy (Dato’ Shaziman Abu Mansor1, 2010). The plan covers from 2011 till 2015,
which will have potential impact on construction sector, proven by its current contribution to GDP
from 2010 to 2013 (refer to table 1).
In 2010, the construction sector demonstrated consistent growth as a result of active
implementation of projects under the 10MP. Huge allocation of government budget to the
construction sector were set since its implementation, which constitute RM230 Billion development
allocation and RM20 Billion facilitation funds. These abundant resources are potentially attracting
high portion of private investment and also cause positive multiplier effect on chain industries. The
value of construction projects awarded in 2010 increased significantly to RM 380 billion i.e. an
increase of 6.5 times as compared to the total awarded in 2006 (RM58.96 billion). This increase
was a result of the high increase in government projects and Public-Private Partnership (PPP)
initiatives. Among the projects are Tolled Highways, Coal electricity plants, land development and
aluminium smelters amounting RM47 Billion in total.
Apart form that, the contribution toward country’ GDP is rather minimal as compare to other
industries (i.e Manufacturing around 25 percent for 2011-2013). The contribution towards country’s
Gross Domestic Product (GDP) demonstrated a small growth since 2011. The country GDP
therefore constitutes averagely 3 to 3.5 percent toward Malaysia’s GDP. Post recession of 1998
indicates that year 2004 has the highest contribution of 7.1 percent is a boundary that hard to
cross.
Table 1: The Gross Domestic Product (GDP) of Construction (2011-2013)
(At constant price of 2005)
Year
2011
2012
2013
Share of GDP (%)
3.0
3.3
3.5 (forecasted)
Change (%)
4.6
15.5
11.2
Source: Dept. Of Statistics and Ministry of Finance Malaysia/ Economic Report 2012/2013
The growth rate of construction industry result is also an average during this period if relatively
compared with the boom period of the 90’s, in which, the extreme limit is as high as 21.1 percent
(1995). The demand factor is the major problem in the construction industry, especially in Malaysia
as it driven by government initiative. The construction initiatives plans, stimulus packages and
process have come under intense scrutiny in recent times. Furthermore, construction industry does
not create its own demand, but also depending on other industry demand for construction. The
demand for construction is highly sensitive to development of other sectors in the economy,
whether public or private sectors and it involves long term investment and long term risks. During
recession, it will be the first to be suspended, and last to be revived during economic upturn
(Sundaraj, 2005).
1
Minister of Works Malaysia 2010
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Proceedings of 5th Asia-Pacific Business Research Conference
17 - 18 February, 2014, Hotel Istana, Kuala Lumpur, Malaysia, ISBN: 978-1-922069-44-3
In support to understand this, the failure rate and bankruptcy of construction firm in Malaysia is
high (CIDB, 2008b). The construction industry is competitive easy to enter, but sore to survive,
especially for small contractors. Local project is inadequate to sustain small contractors in which,
57 percent of contractors are grade G1 (smallest company grade) amounting 78,000 contractors
(CIDB, 2008b).
For the trend of local contractors undertake overseas project also explained the problem of under
performing in the industry. Since the year 2006, the number of projects and project value of abroad
projects undertaken by Malaysian contractors shows declining trend. Only in year 2007 the project
value is significantly higher (95 percent increase) although the project number is lower (Refer to
table 2).
Table 2: Number and Value of Projects undertaken by Malaysian Contractors in Global Market
(2006-2010)
Project (value in RM
Year
Project (amount)
Million)
2006
558
10,189.88
2007
69
19,551.31
2008
55
9,467.37
2009
26
3,666.77
2010
11
1,491.03
Source: Construction Industry Development Board Malaysia (CIDB)
The main component of construction industry performance is driven by corporations in sums
performance. Brush et al. (1999) find that corporation and industry influence business unit
profitability, but corporation has the larger influence. We argue that corporations’ earnings could
contribute to corporation growth as well as the contribution toward GDP. One of the key criteria, at
least for short term objective for corporation is profitability. Profitability refers to income less
expenses before taxes, or net operating income, is also a key performance indicator for
construction company performance measurement. The corporation profit should contribute to the
longer term of objective for corporation performance, which is measured by corporation growth as
well as contribution to GDP.
All the economic key factors contribution and government initiatives plans for construction industry
should be a good benchmark for other industries to adhere certain factor that lead to good
profitability indicators. Therefore, align with this direction; we attempt to examine the factors of
constructions firm's profitability to understand the better significant independent variables for this
scenario.
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Proceedings of 5th Asia-Pacific Business Research Conference
17 - 18 February, 2014, Hotel Istana, Kuala Lumpur, Malaysia, ISBN: 978-1-922069-44-3
2. Data and methodology
Table 3: Variables and measurement
Variables
Profitability
variable)
Capital structure
Liquidity
Size
Economic cycle
GDP
Interest rate
Measurement
(dependent Return on equity (ROE)
Natural Log of Debt to equity
ratio
Quick ratio
Natural logarithm of total sales
Term premium
Natural Logarithm of Gross
Domestic Product in Ringgit
Annualized T.Bill rate
Symbol
PROFIT
CS
LIQ
SIZE
TP
GDP
IR
2.1 Profitability
According to Dietrich and Wanzenreid (2011), bank profitability is usually measured by the return
on average assets and is expressed as a function of internal and external determinants. The
internal determinants include bank specific variables. The external variables reflect environmental
variables that are expected to affect the profitability of financial institutions. The return on equity
ratio (ROE) is also used as an index for firm profitability in a study done by Basil Al-Najjar and
Taylor, 2008. Carsten (2002) said that profitability can be measured by a number of indicators. The
first indicator is profit per equity. Profit is the residual of sales revenue once all costs, including
interest payments on debt, have been deducted; it thus constitutes the return to equity holders.
(‘‘Profit’’ is the aggregate profit of profitable enterprises minus the losses of loss-making
enterprises).
2.2 Capital structure
Capital structure, which is defined as total debt to total assets at book value, influences both the
profitability and riskiness of the firm (Bos and Fetherston, 1993). There are several commonly used
debt ratios in studies on capital structure. In Muhammad (2003), the main issue of investigation is
laid out on the premise of the static trade off theory, which, in simple terms states that some
amount of debt is desirable, but too much of it brings in financial distress. He is concerned with the
total amount of debt used by a firm to finance its entire operation and firm’s ability to service the
loans. Therefore he is concerned with total debt and total liability of the firms. He also studied the
behaviour of long-term debt because it traditionally forms an important component of capital. In his
study he used three leverage measures which are (1) Total liability (non-equity) to total asset ratio
(TLA), (2) Total debt to equity (TDE) and (3) Long-term debt to capital.
Capital structure may also affects firms’ performance. In Ventoura (2002), he used debt to equity
ratio as a proxy for capital structure is. In the present study debt-to-equity ratio proved to have a
negative impact on firm’s profitability. In previous studies, the financial indication has either a
negative or a positive impact. According to the theory, if high debt-to-equity ratio shows greater
uncertainty then higher risk may lead to higher profit margins.
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Proceedings of 5th Asia-Pacific Business Research Conference
17 - 18 February, 2014, Hotel Istana, Kuala Lumpur, Malaysia, ISBN: 978-1-922069-44-3
2.3 Liquidity
Liquidity management is important in good times and it takes further importance in troubled times.
The efficient management of the broader measure of liquidity, working capital, and its narrower
measure, cash, are both important for a company’s profitability and well being. In the words of
Fraser (1998), "there may be no more financial discipline that is more important, more
misunderstood, and more often overlooked than cash management." However, as argued vividly
by Nicholas (1991), companies usually do not think about improving liquidity management before
reaching crisis conditions or becoming on the verge of bankruptcy. According to Abuzar (2004), he
found that a significant negative relationship between profitability and liquidity.
2.4 Firm size
The size of a firm plays an important role in determining the kind of relationship the firm enjoys
within and outside its operating environment. The larger a firm is, the greater the influence it has on
its stakeholders. The growing influences of conglomerates and multinational corporations in today’s
global economy (and in local economies where they operate) are indicative of what role size plays
within the corporate environment (Ezeoha, 2008). Punnose (2008) shows positive relationship
between firm size and profitability. According to Nguyen (1985), profitability is largely independent
of variations in firm size, although large foreign-owned firms generally earn higher profits than large
domestic firms. Goddard et al. (2004) demonstrate that the relationship between the capital-assets
ratio and profitability is positive in six major European banking sectors for the period 1992–1998.
However, Goddard et al. (2010) explore that a negative relationship between the capital ratio and
profitability reflects the standardized risk-return payoff for eight European Union member countries
between 1992 and 2007.
2.5 Economic cycle
A macroeconomic model developed by Kangari (1998) to predict business failure in the
construction industry found that the majority of these variables are significant in relation to the
failure rate. Russell and Zhai (1996) developed a failure prediction model using economic and
financial variables. Considering all those literature, economic cycle may have a significant effect on
profitability. This variable was included in this study to account for the economic effect on
profitability. Thus, economic cycle also can be considered as one of the factors that affect
companies' profitability especially in construction industry. There was evidence that bank profit
behaves pro-cyclically and that this co-movement is especially strong during severe recessions.
Among the different profit components, loan-loss provisioning is found to be the driver of this
asymmetry (bolt et. al, 2012). Based on Athanasoglou et al. (2008) macroeconomic control
variables, such as inflation and cyclical output, clearly affect the performance of the banking sector.
The effect of the business cycle is asymmetric since it is positively correlated to profitability only
when output is above its trend. M. Curak et al. (2012) also found that macroeconomic policies that
contribute to the growth would have positive effects on the profitability of the Macedonian banking
sector.
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Proceedings of 5th Asia-Pacific Business Research Conference
17 - 18 February, 2014, Hotel Istana, Kuala Lumpur, Malaysia, ISBN: 978-1-922069-44-3
2.6 GDP
GDP, which is used as a macroeconomic determinant of bank profitability, measures total
economic activity within a country whereas the GDP growth reflects its annual change. GDP
growth is expected to have a positive effect on bank profitability according to the literature on the
relationship between economic growth and financial sector profitability (Athanasoglou, Brissimis &
Delis, 2008; Demirguc Kunt & Huizinga, 1999). GDP growth controls for cyclical output effects
(Flamini, McDonald & Schumacher, 2009) and is expected to affect numerous factors related to
supply and demand for loans and deposits. For example, during cyclical upswing, the demand for
lending increases and the positive impact on bank profitability is expected. On the other hand, in
unfavourable macroeconomic conditions, such as those in the recent crisis, banks may suffer from
increasing share of nonperforming loans and consequently deterioration in profits. According to
Leornado (2008) stated that bank profits are pro-cyclical: GDP influences both net interest income
(via lending activity) and loan loss provisions (via credit portfolio quality). That was the evidence
that proved that each percentage point of a contraction in real GDP during severe recessions leads
to quarter of percentage point decrease in return on bank assets. (Bolt et. al, 2012)
2.7 Interest rate
Maisel and Jacobson (1978) found that changes in bank income, in terms of book earnings plus
capital gains and losses from changes in asset market value, may be large and move rapidly with
interest rate increases for banks with above average maturity mismatches. G. A. Hanweck and T.
E. Kilcollin (1984) found that rising interest rates may in fact be the periods of the greatest
increases in net interest margins for small banks, both absolutely and relative to large banks.
Based on research done by Everlyne Atieno (2012), there are positive relationship between
interest rate changes and profitability. As interest rate increases, profits also increase. Thus, we
took interest rate as one of the variable to determine the profitability of construction companies in
Malaysia. Bolt et al. (2012) stated that long-term interest rates in previous years are found to be
important determinants, especially when economic growth (and, hence, lending activity) was
relatively high at the time.
3. Hypothesis Statements and Analysis
Based on literature discussion of explanatory variables studied, we therefore hypothesized these
five relationship statements for next analysis.
: There is a relationship between capital structure (CS) and profitability.
: There is a relationship between liquidity (LIQ) and profitability.
: There is a relationship between sales and profitability.
: There is a relationship between term premium and profitability.
: There is a relationship between interest rate and profitability.
: There is a relationship between GDP and profitability.
We used Eview7 for our analysis. The analysis consists of 610 initial observations excluding the
NA data. We estimated panel data for our multivariate analysis, to cope the unbalanced data.
Below (Table 4) is the descriptive statistic of independent variables.
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Proceedings of 5th Asia-Pacific Business Research Conference
17 - 18 February, 2014, Hotel Istana, Kuala Lumpur, Malaysia, ISBN: 978-1-922069-44-3
Table 4: Descriptive statistic of independent variables
CS
Mean
-1.0917
Median
-0.8840
Maximum
1.0973
Minimum
-4.6785
Std. Dev.
0.8973
Skewness -0.8992
Kurtosis
3.7587
Jarque-Bera 96.852
Probability
0.0000
Observations 610
LIQ
1.3432
1.0300
12.560
0.0000
1.2304
3.5717
21.683
10169.01
0.0000
610
SIZE
367052.6
174158.0
6168891
0.0000
627116.7
4.4593
28.913
19089.94
0.0000
610
TP
0.3404
0.1100
1.8100
-0.0200
0.4861
2.4132
7.6076
1131.7
0.0000
610
IR
2.9503
2.9500
3.5000
2.4500
0.3126
0.1118
2.0384
24.772
0.0000
610
GDP
4.5258
5.0960
5.4592
1.9674
1.2537
-1.2755
2.7255
167.31
0.0000
610
Table 5: Result of Correlation Matrix
Covariance
Correlation
t-Statistic
CS
CS
0.8039
1.0000
-----
LIQ
SIZE
TP
IR
LIQ
-0.4686
-0.4251
-11.581
1.5115
1.0000
-----
SIZE
67019.18
0.1192
2.9625
-36913.24
-0.0479
-1.1828
3.93E+11
1.0000
-----
TP
-0.0043
-0.0099
-0.2452
-0.0256
-0.0428
-1.0580
19890.73
0.0653
1.6148
0.2359
1.0000
-----
IR
-0.0089
-0.0318
-0.7855
-0.0042
-0.0110
-0.2726
5217.3
0.0266
0.6575
-0.0134
-0.0888
-2.1997
0.0975
1.0000
-----
GDP
-0.0112
-0.0099
-0.2464
0.0227
0.0147
0.3644
-10637.04
-0.0135
-0.3341
0.0942
0.1549
3.8664
0.0161
0.0413
1.0210
GDP
1.5691
1.0000
-----
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Proceedings of 5th Asia-Pacific Business Research Conference
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Table 5 reports the covariance and correlation matrix of independent variables studied. No serious
correlation detected. We estimate the spearman rank order correlation as for our independent
variables. The result shows that there are no multicolinearity problem among independent
variables as all the dual-relationship correlation shows value of below 0.7 (Sekaran& Bungie,
2010).
Table 6: Estimated Generalized Least Squared Models for Estimation
Model
Adj. R-squared
Durbin Watson
Standard
10.18
1.107
AR(1)
41.92
1.999
AR(2)
18.75
1.18
After testing for multicolinearity problem, we found a heteroskedasticity problem exist in our panel
data set. Therefore, we rectify the problem by estimating generalize least square (EGLS) with 3
models; the standard, first order and higher order of autoregressive to choose best model which is
absent of autocorrelation. We found that first order at auto regressive model of EGLS is sufficient
for the model selection. Table 6 justify the model selection and Table 7 is the model we chose for
our final estimation.
Table 7: Estimated Generalized Least Squares Model of Profitability of Construction Firm
Variable
Coefficient Std. Error
t-Statistic
Prob.
CS
LIQ
SIZE
TP
IR
GDP
C
AR(1)
-0.3020
1.6795
2.95E-06
-0.6174
-0.7622
-0.2238
3.1880
0.4982
-0.6378
3.6507
3.7755
-1.0935
-0.6855
-0.8240
0.7823
17.081
0.5239
0.0003***
0.0002***
0.2747
0.4933
0.4103
0.4344
0.0000***
0.4735
0.4600
7.82E-07
0.5646
1.1119
0.2716
4.0750
0.0291
Weighted Statistics
R-squared
Adjusted R-squared
S.E. of regression
F-statistic
Prob(F-statistic)
0.4278
0.4192
17.670
49.988
0.0000
Mean dependent var
S.D. dependent var
Sum squared resid
Durbin-Watson stat
9.0124
25.442
146126.6
1.9989
*** Significant at 1% level
Table 7 shows the EGLS of PROFIT (dependent variables) with unbalance panel estimation. The
observation of estimation reduced to 476 from 610 as a result of higher order autoregressive
estimation (AR2). The result of Panel EGLS shows LIQ and SIZE are the significant variables
determined the PROFIT (the construction firms’ profitability). Both LIQ and SIZE are positively
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Proceedings of 5th Asia-Pacific Business Research Conference
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significant at 1 percent level. Other IVs shows negative non-significant relationships. Overall, the
model explained only 42 percent variation of PROFIT, and fit at 1 percent level.
4. Conclusion
This study attempts to identify the determinants of construction firms’ profitability. Sales and higher
liquidity level play roles in paying higher return on equity for construction firms in Malaysia. Low
variation of regression result suggested that more IVs, whether firms specific factors or macro
aspect factors, should be tested and could be a significant factor for firms’ profitability.
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