The amount of fixed cost that is being incurred by an organization in its business operations is basically determining the operating leverage of the firm. The degree of operating leverage is higher for the firms that have a higher fixed cost compared to lower variable cost and vice versa.
Financial leverage is the present in the case when a firm opts for debt for its financing needs and has to pay the cost of debt in the form of interest rate rather than equity. Higher is the debt's proportion and higher is the financial leverage.
Large fixed cost of firms makes them more risky due to higher breakeven points than the firms having lower fixed costs. Operating profits of leveraged firms increase faster than un-leveraged firms with increase in sales, while rapid decrease can be seen when sales decline.
This thesis report will examine the effect of operating leverage, financial leverage, liquidity and size of the firm on return on assets and return on equity. The empirical calculations and regression analysis is performed on a panel set of 8 pharmaceutical companies of Pakistan for the period of 2004 to 2009.
The organization of the remaining report is as follows. Section II provides information on the Pakistan's pharmaceutical sector profile. Section III is carrying out a theoretical discussion of some capital structure theories. Section IV contains review of literature. Section V discusses the description of the variables, the sample and data used, hypotheses and methodology to test the hypotheses. Section VI shares and describes the empirical findings. Section VII contains the concluding remarks for this study.
THE PHARMACEUTICAL SECTOR OF PAKISTAN
The $1.62 billion pharmaceutical market of Pakistan stands as 10th largest in Asia Pacific as compared to the 9th largest $2.58 billion Philippine market and 11th largest Vietnam of $1.53 billion. This sector is actually being dominated by multinational organizations. GSK is the market leader in Pakistan's pharmaceutical industry and is the largest drug maker operating in Pakistan. Among the significant local companies are Highnoon laboratories, Ferozsons and Searle Pakistan which are also listed on the stock market. This sector achieved combined sales of $400 million in fiscal year 2008. Business environment is challenging and is characterized by high level of business risk. Pakistan was rated as 15th in regional ranking just ahead of Cambodia by BMI (Business Monitor International).
Ministry of health is the main regulating authority and all products to be sold in the market or exported have to be authorized by MoH. The 1976 Drug Act has been and is still used for regulatory purposes. Pricing of pharmaceutical products is also controlled by Ministry of health.
Spending on health care has been low in the country. Pakistan's spending on health care in 2008 was 2.9% of GDP which was lower than the regional (5.1%) as well as the global (6.7%) average.
In Pakistan's pharmaceutical sector the top 50 companies (that include 30 multinationals and 20 domestic companies have a market share of 83.5%, while top 100 companies have 94% of the sector. Total number of companies operating in Pakistan was 664 in 2008 out of which 405 are registered units for manufacturing including 31 multinationals. Large number of firms operating in the market is seen by Government as a source of creating price competition and therefore lead to consumer benefit. 80% of the sales are through retail sector while the rest is carried out through health care institutions. Local companies usually work to produce generics and R & D is not very much significant in the sector. Only 5% of locally produced drugs are exported. The local industry is still vulnerable to foreign imports as the technical capacity of these local organizations is not very promising. The shortage of financial resources as well as unavailability of raw material can also be added to the reasons in addition to technical backwardness.
Future forecast for Pakistani pharmaceutical sector suggests that medicine sales will be increasing from PKR 132 billion ($1.62 billion) to PKR 196 billion ($ 1.78 billion) in 2014, while the market is expected to grow up to PKR 290 billion ($ 2.12 billion).
THEORETICAL DISCUSSION: CAPITAL STUCTURE THEORIES
There are different theories of capital structure proposed by subject matter experts over the time.
David Durand (1952) announced the capital structure by net income approach. Solomon (1963) described the capital structure by intermediate approach. This theory defines the increase in the value of the firm to a certain level of debt capital, after which it tends to remain constant with a moderate use of debt capital, causing value of the firm to decrease (Solomon 1963).
The irrelevance proposition of Modigliani and Miller actually resulted in a lot of research work and much debate on theories of capital structure of firms.
Myers (1984) divided the capital structure concept into two theories. First one was the Static Trade off Theory (STT) of capital structure which tells that the optimal debt ratio of a firm is determined on the basis of cost and benefits of borrowing. This analysis of trade off actually includes cost of financial distress, taxes and agency costs. Two important empirical of this theory are:
Companies having more tangible assets and growth opportunities have a higher
debt. This higher debt helps them to get a bigger tax shield.
Companies having high business risk have less debt as they are uncertain of
generating enough income to utilize the tax shield advantage.
Second theory by Myers (1984) describes the relationship between debt-equity ratio and profitability of a firm to be negative. This actually tells that the firms are giving first priority to internal finance i.e. through retained earnings for financing their projects. If they still require additional finance they first go for bank loan and give least priority to going for public debt. This theory was called Pecking Order Theory (POT) whereby firms actually follow a hierarchy of financial decisions in establishing their capital structures. This theory therefore implies that more profitable firms will have less debt as they can arrange their financing needs internally and vice versa.
LITERATURE REVIEW
Modigliani and Miller (1958) described the effect of capital structure on earnings, market value of the firm. With some presumptions they concluded that a leveraged and un-leveraged firm has the same market value. Risk of business was to be measured by standard deviation of operating income. Capital markets were assumed to be perfect while debts were assumed to be risk less. Thus, debts were given on risk free rate of interest.
Baxter (1967) found a negative relation between variance of operating earnings and leverage. He described that business with stable income stream are more likely to be heavily leveraged as compared to business with risky income stream.
Mseddi and Abid (2004) reported a significant positive impact of financial and operating leverage on the firm's profitability and consequently value of the firm by investigating the relationship between firm's value and risk using panel data to estimate operating and financial leverage for 403 USA firms over the period from 1995 to 1999.
James M. Gahlon and James A. Gentry (1982) developed a model that showed that degree of operating leverage and degree of financial leverage were the explicit determinants of systematic risk (beta).
Gardner et al. (1986), Weinraub and Visscher (1998) highlighted the fact in their studies that more aggressive working capital policies as compared to conservative working capital policies lead to higher returns but in addition to this higher risk also.
Smith (1980) also described the importance of working capital because of its effects on profitability, risk and thus value of the firm.
Lancaster et al. (1999), Farris and Hutchison (2002) and Moss and Stine (1993) saw corporate liquidity being examined from either a static or dynamic view. The static view is basically using the ratio calculated from balance sheet data e.g current ratio. Static view gives the liquidity at a given point in time. The dynamic view provides the liquidity status ongoing from the firm's operations. It measures liquidity dynamically i.e. from the cash outflow to cash inflow which is measured by cash conversion cycle.
Jose et al. (1996) and Eljelly (2004) studied the empirical relationship between profitability and liquidity measured by current ratio and found a significant negative relation between them.
The size of the firm is also found to be significant. Remmers (1974) said that large firms are more diversified. Pinches and Mingo (1973) claimed larger firms to have easier capital market access as well as having higher credit rating that leads them to lower interest payments on debt.
EXPLANATION OF VARIABLES, HYPOTHESES AND SAMPLE
Variables used in the econometric model are described in this section along with their measurement methods. The null hypotheses are also included as well as methodology to test these hypotheses. At the end of the section the sample size and data source are discussed.
Explaining the Variables
Return on Assets and Return on equity are taken as two dependent variables while size of the firm, liquidity, operating leverage, financial leverage are taken as the independent variables in the econometric model being tested. The relative impact of these independent variables on the dependent variables is tested and analyzed.
Return on Assets and Return on Equity (Dependent variables)
Return on Asset basically tells the returns earned on investing the assets in a business. This has been measured by dividing Net income by total assets deployed. Return on equity tells the returns earned from the shareholder's equity. It is calculated by dividing net income by shareholder equity.
ROA and ROE are taken as dependent variables in multivariate regression analysis in order to find out the impact of the independent variables discussed later in this section on both of these variables.
Size of the firm
Size of the firm can affect the returns earned by a firm. Large companies are able to buy inventories in large quantities which is not possible for smaller sized firms because of associated cost which are unbearable for them. Also, large firms buy large quantities and thus have a higher negotiation power and are able to get discounts on their purchases. Large firms are usually able to get more favorable credit terms from their suppliers in terms of longer credit periods. For this study, we have measured the size of firms by taking natural log of sales (which smoothes the variation over the time period taken into consideration).
Degree of Operating Leverage
A firm with a higher fixed cost as compared to variable cost is said to have a higher operating leverage. A firm with higher degree of operating leverage will actually experience rapid increase in returns. This is because of the fact that with the increase in sales the variable costs in this case will not increase substantially. In contrast a firm with higher variable cost will face increased costs when sales level will increase and so returns would not increase at a rapid rate in the case of firms with lower degree of operating leverage.
Although operating leverage magnifies returns with increase in sales it also cause a rapid decline with decreasing sales and firm therefore poses high business risk. DOL has been calculated as a ratio of percentage change in EBIT to percentage change in sales.
Degree of Financial Leverage
The extent to which financial fixed costs are being incurred by an organization is measured by degree of financial leverage. These costs are usually the cost of debt taken by the organization to fulfill its financing needs. DFL exposes the firm to financial risk. Operating and Financial leverage normally move in the same direction and increase/decrease the returns expected in a rapid manner pertaining to the changes in sales levels. DFL is measured as a ratio of EBT (Earnings before Taxation) to EBIT (Earnings before Interest and Taxation) for this study.
Liquidity
Liquidity is basically defined as being in cash or easily convertible to cash in a small time period. The direct effect of liquidity is not only on the cash positions but it also affects the returns of the firm directly. Efficient liquidity management requires planning of current assets and current liabilities in a manner that there is no or minimum risk of a firm not being able to meet its short term obligations. Consequently, liquidity actually avoids excessive investments in assets. Due to this a negative relation can be expected between the returns and liquidity. Current ratio is used as a measure of liquidity for this study.
Hypotheses
The main objective of the study is to find the impact of operating leverage, financial leverage, size of the firm and liquidity on return on assets and return on equity of firms in pharmaceutical sector of Pakistan. For this purpose the following hypotheses were tested:
Ho1 = Degree of Financial leverage has a significant impact on return on assets of
pharmaceutical firms of Pakistan
Ho2 = Degree of Operating leverage has a significant impact on return on assets
pharmaceutical firms of Pakistan
Ho3 = Size of the firm has a significant effect on return on asset of the firms
Ho4 = Liquidity of the firm has a significant impact on return of assets of the firms
Ho5 = Degree of Financial leverage has a significant impact on return on equity of
pharmaceutical firms of Pakistan
Ho6 = Degree of Operating leverage has a significant impact on return on equity
of pharmaceutical firms of Pakistan
Ho7 = Size of the firm has a significant effect on return on equity of the firms
Ho8 = Liquidity of the firm has a significant impact on return of equity of the firms
Panel regression was run to test these hypotheses. The econometric models used are:
ROA = α0 + α1 DFL + α2 DOL + α3 SZ + α4 CR + ε - (1)
ROE = β0 + β1 DFL + β2 DOL + β3 SZ + β4 CR + ε - (2)
Where,
ROA = Return on assets
ROE = Return on equity
DFL = Degree of financial leverage
DOL = Degree of operating leverage
SZ = Size of the firm
CR = Liquidity of the firm in terms of current ratio
ε = Error term
Sample size and source of data
This study is focused on the pharmaceutical industry of Pakistan and sample consists for 8 most significant pharmaceutical companies. The financial data for the period 2004 to 2009 used for regression analysis is taken from balance sheet analysis of joint stock companies listed on Karachi stock exchange published by SBP from 2003 to 2008 and annual reports 2009 of the companies present in the selected sample. Thus a total of 48 observations are used.
EMPIRICAL RESULTS
Empirical results of this study are presented in this section. Results include descriptive statistics as well as the regression analysis findings with explanation of the discovered relationships.
Descriptive Statistics
Table 1 shown below shows the descriptive statistics of the variables in the econometric model with all variables other than size (which is taken as natural log of sales) taken in percentage form. It can be seen that degree of operating leverage has a lower mean than degree of financial leverage and is very much volatile as compared to DFL. Both DOL and DFL are negatively skewed which shows a gradual increase and then rapid decrease. High kurtosis values shows that both DOL and DFL are peaked (leptokurtic) relative to normal distribution. Size of the firm shows less deviation, it is nearly normal distribution as the skewness is very close to zero while kurtosis value is also near 3. Current ratio is showing large deviations while mean is also very high so some of the firms from the sample are having very high liquidity while some are on the other extreme.
TABLE 1: DESCRIPTIVE STATISTICS
ROA
DOL
SZ
DFL
CR
ROE
Mean
17.32638
0.705229
7.885813
0.807896
235.6250
26.67673
Median
15.96550
1.094000
7.788500
0.978500
172.3245
27.10000
Maximum
39.40000
16.61700
9.597000
1.134000
512.9000
56.80000
Minimum
-5.980000
-34.96700
6.282000
-1.965000
91.40000
-8.860000
Std. Dev.
11.02729
7.871707
0.880120
0.442205
128.8802
13.34957
Skewness
0.264094
-2.345196
0.133510
-5.282461
0.577419
0.054973
Kurtosis
2.200279
12.11623
2.124538
33.63460
1.919339
2.988453
Jarque-Bera
1.837070
210.2107
1.675468
2100.193
5.002962
0.024443
Probability
0.399103
0.000000
0.432690
0.000000
0.081964
0.987853
Sum
831.6660
33.85100
378.5190
38.77900
11310.00
1280.483
Sum Sq. Dev.
5715.254
2912.297
36.40676
9.190608
780675.5
8375.924
Observations
48
48
48
48
48
48
ROA and ROE both are having skewness very close to zero that shows they are almost following a normal distribution. ROE is much closely skewed to zero than ROA. Kurtosis results also confirm this observation especially ROE is very close to 3.
Results of Regression Analysis
In order to find the determinants of returns of firms operating in the pharmaceutical sector of Pakistan regression of return on assets and return on equity were ran separately on the degree of financial leverage, degree of operational leverage, size of firms and liquidity. The size of firm is taken as natural log of sales all other variables were already in percentage form and there was no need to change to log form. Table 2 shows the regression analysis while taking return on assets as dependent variable and DOL, DFL, size of the firm and liquidity as independent variables. Table 3 shows regression results taking ROE as dependent variable.
TABLE 2: REGRESSION ANALYSIS RESULTS (ROA as dependent variable)
Dependent Variable: ROA
Method: Least Squares
Date: 09/11/10 Time: 07:04
Sample: 1 48
Included observations: 48
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
5.311095
8.815325
0.602484
0.5500
DOL
0.234263
0.119233
1.964752
0.0559
SZ
-1.256102
1.169587
-1.073970
0.2888
DFL
11.17007
2.137303
5.226245
0.0000
CR
0.054032
0.008215
6.576850
0.0000
R-squared
0.707662
Mean dependent var
17.32638
Adjusted R-squared
0.680467
S.D. dependent var
11.02729
S.E. of regression
6.233422
Akaike info criterion
6.596060
Sum squared resid
1670.789
Schwarz criterion
6.790977
Log likelihood
-153.3055
F-statistic
26.02244
Durbin-Watson stat
1.455610
Prob(F-statistic)
0.000000
From Table 2 we can see the estimated value of R-squared showing that 70.7 percent variation in return on assets is being jointly determined by the four independent variables chosen for this study. The durbin-watson statistics show a value of approximately 1.5 which shows a positive serial correlation which is the most commonly observed form of dependence. F-statistics value is also fairly high.
T-statistics clearly show the significant relationships of degree of operating leverage, degree of financial leverage and liquidity of the firm to the return on assets of the firm. Size of the firm has no significant impact on returns on assets. The level of significance although varies between DOL, DFL and liquidity. Liquidity of the firm is found having the highest significant relationship by analyzing t-statistics, while degree of financial leverage coming next to it. Degree of financial leverage has the highest coefficient significantly higher then DOL or liquidity. This implies that firms in pharmaceutical sector of Pakistan should go for financial leverage as with every percent increase in degree of financial leverage its effect will be magnified by a high co-efficient (11.17) resulting in a significant increase in return on assets. On the other hand operating leverage has a co-efficient of 0.23 which will not be very beneficial in terms of improving return on asset of the firms.
Regression analysis of the second econometric model are shown in table 3 keeping ROE as the dependent variable and studying the effect of degree of operating leverage, degree of financial leverage, size of the firm and liquidity.
TABLE 3: REGRESSION ANALYSIS RESULTS (ROE as dependent variable)
Dependent Variable: ROE
Method: Least Squares
Date: 09/11/10 Time: 07:07
Sample: 1 48
Included observations: 48
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
19.60603
13.74412
1.426503
0.1609
DOL
0.473502
0.185898
2.547111
0.0145
SZ
-1.788394
1.823523
-0.980736
0.3322
DFL
17.00566
3.332306
5.103272
0.0000
CR
0.030136
0.012809
2.352793
0.0233
R-squared
0.515107
Mean dependent var
26.67673
Adjusted R-squared
0.470000
S.D. dependent var
13.34957
S.E. of regression
9.718634
Akaike info criterion
7.484300
Sum squared resid
4061.430
Schwarz criterion
7.679216
Log likelihood
-174.6232
F-statistic
11.41982
Durbin-Watson stat
1.370929
Prob(F-statistic)
0.000002
Regression analysis results show a highly significant impact of degree of liquidity, financial leverage and degree of operating leverage on return on equity. Size of the firm has a t-statistics value of less than 2 implying a non-significant relationship between it and return on equity. R-Squared value is showing that approximately 51.5 percent variations in the values of return on equity is jointly being derived from the independent variables selected for this econometric model. The durbin-watson statistics show a value of approximately 1.4 which shows a positive serial correlation.
Like in the previous case degree of financial leverage here also have the largest co-efficient. This again lead to the fact that every percent increase in degree of financial leverage will result in a magnified increase in return on equity due to the high co-efficient (17.0) of DFL. Thus, pharmaceutical firms should go for financial leverage and thus use more debts than equity in their capital structure to achieve higher returns on equity as well as assets.
Stability Test
To test the stability of these models CUSUM test was carried out which is based on the cumulative sum of the recursive residuals. This test plots the cumulative sum together with the 5% critical lines and finds parameter instability if the cumulative sum goes outside the area between the two critical lines. It can be clearly seen from the results of this test that both the models are stable as there is no movement present outside the critical lines that could suggest co-efficient instability.
CUSUM Test for ROA
CUSUM Test for ROE
CONCLUSION
A sample of 8 firms operating in the pharmaceutical industry of Pakistan was taken for this study and regression analysis was used to determine the controlling factors of return on assets and return on equity of the firms. The data used for this study belonged to the year 2004 to 2009. Keeping in view the empirical results the hypotheses Ho1, Ho2, Ho4, Ho5, Ho6, and Ho8 are accepted while hypotheses Ho3 and Ho7 are rejected. Thus, strong evidence is gathered to reach to following conclusions:
Financial leverage has a statistically significant positive impact on return on assets as well as return on equity of the firms.
Operating leverage significantly affects the return on assets and return on equity of the firms.
Size of the firm has no statistically significant influence over return on asset and return on equity of the firms.
Liquidity of the firm has a significant relationship with return on assets and return on equity of the firms.
The CUSUM stability test was successful and therefore provided evidence on stability of the findings. The findings of the study may be useful to determine the optimum level of operating and financial leverage for a firm as well as the liquidity levels to increase the efficiency of the firms and consequently improve the returns earned on assets and equity employed by the firm.
