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:
[email protected] ** Mr Wan Muhd Faez Wan Ibrahim, Department of Finance, UniversitI Teknologi MARA, Malaysia. Email:
[email protected] *** Mrs Nurul Syaqirah Zulqernain, Department of Marketing, UniversitI Teknologi MARA, Malaysia. Email:
[email protected]
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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 17 - 18 February, 2014, Hotel Istana, Kuala Lumpur, Malaysia, ISBN: 978-1-922069-44-3 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 17 - 18 February, 2014, Hotel Istana, Kuala Lumpur, Malaysia, ISBN: 978-1-922069-44-3 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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