AN INVESTIGATION OF THE INFLUENCE OF KEY FINANCIAL AND.pdf

Electronic copy available at: http://ssrn.com/abstract=1534186

AN INVESTIGATION OF THE INFLUENCE OF KEY FINANCIAL AND

ECONOMIC INDICATORS ON PROFITABILITY OF CEMENT SECTOR

COMPANIES IN PAKISTAN

H. Jamal Zubairi Institute of Business Management (IoBM), Karachi, Pakistan

ABSTRACT

For any business concern the net profit or bottom line for a particular time period is the end result

of its investing, financing and operating activities. These activities can be visualized as being

influenced by managements decisions and a host of internal and external environmental factors.

This paper investigates how profitability of firms, in the cement manufacturing sector of Pakistan,

is influenced or linked to selected financial and economic indicators. The purpose of the research

is to check whether the linkage of profitability with the selected indicators is in line with the

relevant generally accepted theory. Also, if there appear to be some deviations from the theory,

what the plausible reasons are which can explain these. Furthermore, the conclusions arrived

through data analysis might lead to some useful policy recommendations for Pakistani cement

companies, to better manage their profitability.

JEL classification: C23, G32

Keywords: Profitability, Operating Leverage, Financial Leverage, Liquidity, Gross Domestic

Product (GDP), Return On Assets (ROA), Return on Equity (ROE), Pakistans Cement Industry,

Panel Data

I. INTRODUCTION:

Finance and Economics theories come up with explanations of the likely impact of different

financial and economic factors on the profitability of a business. This paper focuses on studying

the impact of selected variables on profitability of firms in the cement manufacturing sector of

Pakistan. The purpose of the study is to examine the extent to which the linkage of the selected

indicators with profitability conforms to the relevant theory and to provide explanations for any

variations observed. More specifically, the objective is to see whether the cement sector companies

exhibit a commonality in respect of the linkage of the selected variables to profitability. In other

words to what extent can the linkage between a particular variable and profitability be generalized

for the sector as a whole or is there evidence to conclude that the significance of selected variables

differs from company to company.

Data of 12 out of 20 cement companies listed on the Karachi Stock Exchange was analyzed using

an econometric framework over a four year period (financial years 2005 to 2008). Return on

Equity (ROE) and Return on Assets (ROA) are taken as major profitability indicators (dependent

variables) while average share price, current ratio, long term debt to total capitalization, year-on-

year revenue growth and GDP are taken as independent variables. The strength of linkage between

the selected dependent and independent variables was tested by applying pooled regression and

diagnostics like R-squared, F-Statistic and DW-Statistic. Results interestingly indicate that only


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GDP growth (an external factor) has a significant effect on profitability, while none of the other

variables exhibit a statistically significant linkage with profitability during the years 2005-2008.

Following are the details of the findings:

First, out of the chosen independent variables only GDP growth has a significant positive

relationship with profitability. Second, financial leverage as measured by long term debt to total

capitalization has a negative though weak relationship with profitability. The sign of the

relationship supports the Dynamic Tradeoff theory that degree of financial leverage should have a

negative effect on profitability. However, this is contrary to the Static Tradeoff Theory that

postulates a positive relationship between profitability and financial leverage. Third, liquidity as

measured by the current ratio has a weak positive relationship with profitability, which is in

variance with finance theory which stipulates a negative relationship between liquidity and

profitability. Fourth, year-on-year growth in sales revenue has a weak positive impact on the

profitability of the examined firms. This implies inconsistencies in the cost structures of the sample

firms, which means that for a given percentage increase in sales revenue of every firm, the

percentage change in profitability will vary from firm to firm. Fifth, average share price of a firm

has a weak positive relationship with profitability. This shows that share prices of cement firms on

the Karachi Stock Exchange are not a true reflection of their financial performance and there are a

number of other factors impacting on the market price of shares.

The organization of the paper is as follows: Section 2 presents the theoretical basis for the analysis

and main findings of some previous empirical studies in the related area. Section 3 provides a

detailed description of the methodology, operational definitions of the variables, econometric

model and data used in the study. The estimated results are reported in Section 4. Finally, Section

5 concludes the main findings alongwith explanation of the results.

Names of the companies, included in the sample whose Income statement and Balance Sheet

figures were used to extract the data used in this research study, are given at Annexure A.

II. THE THEORY AND THE EMPIRICAL EVIDENCE

The linkage of profitability to capital structure is seen differently by the two theories presented by

Myers in 1984. These are the Static Trade-off theory (STT) and Pecking Order Theory (POT). STT

postulates that a companys capital structure is based on a target debt-equity ratio which is arrived

at by evaluating the costs and benefits linked to levels of debt. The factors evaluated include tax

impact, agency costs, financial distress costs etc. On the other hand POT argues that companies

base their capital structure on a hierarchy of decisions. They would first use internal funds

(retained earnings) for their financing needs. If fund requirements for investment projects can not

be fully met from internal sources, a company would go for debt financing from a bank or other

financial institutions, while issuing equity would be considered as a last option for external

financing. This means that companies operating profitably would generally not resort to debt

financing for their new projects, since they have sufficient internal funds available for the purpose.

On the other hand according to STT, profitable companies would prefer raising debt financing to

avail the benefit of tax shield on borrowed funds. Thus, STT supposes a direct relationship

between profitability and leverage, while POT expects an inverse linkage of profitability with

leverage. Moreover, STT argues that larger size companies would show greater preference for debt

financing due to lower chances of going bankrupt. This is supported by the assumption that larger


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firms are more diversified, which also reduces the bankruptcy probability (see, for details Titman

and Wessels 1988).

Signaling Theory which was first presented by Ross (1977) explains that raising debt can be taken

as a signal to the capital markets that a company is confident that its future net cash flows after

debt servicing, are going to be positive. This is because a company is contractually bound to

service its debt i.e. pay interest and repay principal from its cash flows, otherwise it may be forced

to go into liquidation by its creditors. Thus, higher level of debt reflects the managements as well

as investors positive expectations in respect of future cash flows of the company. With reference

to POT, the issuance of equity by a company, rather than obtaining debt for financing its new

projects, sends a negative signal to the market. This is because managers are expected to have

more and superior information on the company and may therefore be tempted to issue equity when

it is overpriced, thereby hurting the interests of equity investors.

Financial management texts generally postulate an inverse relationship between liquidity and

profitability. Liquidity can be seen in the context of the level of current assets i.e. a high level of

current assets means high liquidity. This translates into a lower level of risk for a firm i.e. it will

have sufficient cash or near cash items, not only to meet its routine needs including payments to

its creditors, but also unexpected cash requirements in the event of an emergency. A similar line of

argument goes for accounts receivables, as these also convert to cash in due course, except for bad

debts which are usually a small percentage of sales. The same reasoning applies to inventory for

products with an established market and satisfactory turnover. However, if inventory constitutes a

relatively high proportion of current assets, this could signal that the demand for the firms

products is declining and in such a situation it would not be prudent to consider inventory to be

liquid. For this reason while liquidity may generally be estimated through the current ratio (current

assets/current liabilities), a stricter measure of liquidity the quick ratio ([current assets minus

inventories]/current liabilities) is more appropriate, when the current assets include a relatively

high percentage of inventories.

Modigliani and Miller (1958) in the first version of their paper tried to identify the effect of capital

structure on earnings and market value. They argued that in an economy without corporate and

personal taxes, capital structure does not matter. In other words, under a restrictive set of

assumptions, an un-leveraged firm has the same market value as a leveraged firm. They later added

corporate taxes to their model and then demonstrated that earnings and market value of the firm

can only be maximized by using 100% debt in their financing mix. Their findings were based on

the assumptions that business risk can be fairly gauged by the standard deviation of operating

income (EBIT); also that all present and prospective investors have homogeneous expectations

about corporate earnings and the riskiness of those earnings. They also assumed that capital

markets in which companies stocks and bonds are traded are perfect. Their most important

assumption was that the debt of firms and individuals was riskless, so the interest rate on debt was

a risk-free rate. Their model with corporate taxes demonstrated that benefits from debt arise

because of tax deductibility of interest payments.

Gahlon and Gentry (1982) came up with a model for estimating beta which is the measure of

riskiness of an asset as compared to the risk of a market portfolio. The variables used in the model

incorporated both operating and financial leverage as measured by DOL (degree of operating

leverage) and DFL (degree of financial leverage). The model focused on the impact of operating

and financing decisions on an assets systematic risk and valuation. The models findings

confirmed that DOL and DFL are representative of asset risk. Also beta was shown to be a

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function of DOL / DFL, the coefficient of variation of companys gross earnings, and correlation

of cash returns to equity holders with the total monetary return on all capital assets or the total

investment. Another study by Mandelker and Rhee (1984) provides evidence about the linkage

between DOL, DFL and beta. They proved empirically that between 38 to 48 percent changes in a

cross-section of data are explained by DOL and DFL.

Mseddi and Abid (2004) explored the connection between company value and risk. They used

panel data to estimate DOL and DFL of 403 non-financial USA firms over the period 1995 to

1999. They reported that both financial and operating risk have a significant positive impact on

company value. They further provide evidence that excess return is a positive and increasing

function of DOL, DFL and systematic risk for all firms in the sample that exhibit a positive

correlation between sales changes and market portfolio returns. Eljelly and Abuzar (2004)

empirically examined the linkage of profitability with liquidity, as indicated by the current ratio

and cash cycle. They studied a sample of Saudi Arabian companies taken from the major economic

sectors, excluding power generation and banking, covering the years 1996 to 2000.The correlation

and regression analysis demonstrated a significant inverse relationship between company

profitability and liquidity, while a direct and strong relationship was seen to exist between

company size and profitability. This relationship, however, was more pronounced within industrial

sectors but not across all companies. Liquidly and size have more influence on profitability of

capital intensive industrial sectors as compared to their impact on service sector organizations.

Amongst the indicators of liquidity, the strongest influence on profitability was of current ratio

(CR), irrespective of industrial sector. However, when sectors were analyzed separately, liquidity

indicated by the cash gap was found to have a more significant impact on profitability. However,

this impact was of lesser significance for service sector or labour intensive companies but more

significant in the case of manufacturing and capital intensive companies. The study also found that

significance of the impact of liquidity on profitability varies from industry to industry. Although

firms may find it necessary to hold a certain minimum level of liquidity, the study shows that on

the one hand firms may lose profit opportunities by having very low liquidity, while on the other

they could be incurring unnecessary costs by carrying excessively high liquidity. In line with these

findings, Kesseven Padachi (2006) also found that low profitability was linked to the tying up of

large amounts of investment in inventories and receivables.

Larry et al. (1995) found an inverse relationship between a companys leverage and its growth rate.

This relationship was more pronounced in case of companies whose true growth potential was not

given due recognition by the capital markets or their perceived value was considered lower than

that needed to override the effect of debt overhang. They also confirmed that leverage did not

adversely affect the growth of companies which were reputed to be highly profitable. For studying

the leverage and growth linkage, data used covered a long time period of 20 years. Employing

regressions of investments on distinct parts of company cash flows, they found that reduction in

operating flows did not adversely affect investment to such an extent as was the case when a

comparable cash outflow was needed for debt servicing. The extent of leverage used depends on

managements own assessment of future growth. Thus, managers anticipating profitable growth

opportunities might feel that raising external funds and subsequent associated cash outflows could

be an impediment to growth. Consequently, an inverse relationship between leverage and

profitability could arise because of high growth companies managements deliberate preference

for a low leverage capital structure. Samuel H. Baker (1973) measured financial leverage inversely

by computing the ratio of equity to assets i.e. the lower this ratio the higher would be the leverage.

In line with finance theory, they found the relationship between profitability to be negative as well

as significant.

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Sudipto Dasgupta and Kunal Sengupta (2002) in their study titled Financial Constraints, Investment and Capital Structure: Implications from a Multi-Period Model examined the

relationship between profitability and leverage. They found that within a dynamic framework

profitability could be directly related to leverage, which is at variance from most studies using one-

period models that show an inverse linkage between leverage and profitability. This result is

however in line with the conclusions of a study by MacKay and Phillips (2001), which found that

within an industry, financial leverage and profitability are directly related. Also Zubairi and Rashid

(2008) found a positive relationship between liquidity and profitability of automobile sector firms

in Pakistan. This was attributed to the sellers market scenario prevailing during the period under

review. In this period automobile firms were routinely getting advance payments from their

buyers, for up to five months before delivery, thereby enjoying substantially high liquidity.

Fama and French (2000) conclude that: The pecking order model predicts that more profitable

firms have less book and market leverage. The leverage regressions support the pecking order

model. Myers (1984) argued that, high profitability firms having access to substantial internal

funds prefer to avoid using the costlier external sources of financing i.e. debt and external equity,

to the maximum extent possible. If the variability of profits from existing projects is estimated to

be high, it would be prudent for companies to borrow presently instead of waiting for profits to

actually materialize. This borrowing would also help in keeping high cash balances, to overcome

possible liquidity constraints that might occur in the future. The level of borrowings can of course

be enhanced by firms, if the profitability of existing projects is high. Thus, we can conclude that, at

variance with the generally accepted theory, under specific conditions, a direct relationship

between leverage and profitability can also exist.

Timothy G. Sullivan (1974) while probing the relationship between company power and use of

leverage found that more powerful firms used relatively less debt in their capital structure. Thus

the common assertion that the higher profitability of these powerful firms could be because of

employing higher financial leverage, is refuted by Timothy G. Sullivans findings.

F. Samiloglu and K. Demirgunes (2008) studied and analyzed the effect of working capital

management on firm profitability. The sample for their study comprised manufacturing firms listed

on the Istanbul Stock Exchange. The data covered the years 1998-2007 and the variables included

for determining firm profitability comprised the various components of the cash conversion cycle.

The study found through multiple regression analysis that during the period under review, average

collection period of accounts receivable, inventory turnover in days and leverage had an inverse

and significant relationship with profitability, while growth in sales impacted positively and

significantly on profitability. However, the variables; cash conversion cycle, firm size and fixed

financial assets did not significantly affect profitability.

III. VARIABLES DESCRIPTION, METHODOLOGY AND SAMPLE

This section presents the measurements of the variables and discussion of different measures of the

variables, null hypothesis and methodology used to test the hypothesis and provides information

about the sources of data and sample size.

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A. Variables:

1) Return on Equity & Return on Assets (as dependent variables)

2) Average Share Price

3) Liquidity (Current Ratio)

4) Financial Leverage (Long Term Debt to Total Capitalization)

5) Year to year growth in revenue

6) Gross Domestic Product (GDP)

Following is a brief introduction of the variables used in the study:

Return on Equity and Return on Assets

These are computed by dividing the net income by total stockholders equity and total assets

respectively. For the purpose of this paper net income before tax has been used to avoid possible

distortion in results due to any special tax treatment position of any firm. The ratios have been

taken in percentage terms.

Average Share Price

For the purpose of this study, the market price of the firms share has been taken from Karachi

Stock Exchanges website and the average price prevailing in a particular year has been taken for

comparison with and analysis of its relationship with profitability indicators of the corresponding

years.

Liquidity

The most common liquidity measures of a firm are its current and quick ratios. The current ratio is

used as an indicator of a companys ability to meet it short term debt obligations through its

current assets. It is measured by: current assets / current liabilities. If inventory comprises a high

percentage of current assets a stricter liquidity measure called quick ratio is considered to be a

more appropriate indicator of liquidity, which is calculated by: (current assets inventories) /

current liabilities. There are no universally good or bad current and quick ratios, since these have

to be seen in the context of the nature of business or the industry to which a firm belongs.

However, in general, the higher these ratios, the safer is a firm from the point of view of short term

creditors. Since these ratios are calculated on a particular balance sheet date, they can also be seen

as measures of a firms ability to pay off its short term creditors out of the cash proceeds from its

current assets, in the extreme case of the firm being liquidated due to its inability to continue

operations as a going concern. In this paper the liquidity measure used is the current ratio,

calculated in the standard way i.e. Current Assets (Cash + Marketable Securities + Accounts

Receivable + Inventories) / Current Liabilities.

Financial Leverage (Long Term Debt to Total Capitalization)

Companies can finance their assets through a combination of debt and equity. Leverage ratios

show the extent of a firms reliance on borrowed funds. Leverage ratios tell us how a firm is

financing its operations and provide some insight into its financial strength. The higher the

proportion of debt in the capital structure of a company, the higher is its default risk because debt

carries a fixed cost which has to be paid irrespective of its operating performance. Thus, a high

proportion of debt makes a firm more vulnerable to default with a slight decline in operating

performance. In practice, there are many variations of this ratio. It is therefore important to be clear

on what figures are being taken from a company's financial statements for computing this ratio. In

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this paper financial leverage has been taken as the proportion of long term debt in the total long

term financing sources, measured by long term debt / (long term debt + equity).

Year-to-Year Growth in Revenue

Year to year growth in revenues has been calculated in percentage terms by comparing the net sale

figure of a firm for a particular year with the net sales of the previous year, measured as follows:

[(Net sales year 2 net sales year 1) / net sales year 2] X 100%.

Gross Domestic Product - GDP

GDP is the monetary measure of all finished goods produced and services provide in a country,

within a time period, usually a financial year. It includes all of private and public consumption,

government outlays, investments and exports less imports that occur within a particular country.

GDP growth rate is one of the most common indicators of a countrys economic performance.

Gross National Product GNP

Another indicator of a countrys economic performance is GNP. It is the total value of the finished

goods and services produced/provided within a country in a year, plus the income of its nationals,

whether working in the country or abroad, less the income of foreign nationals working in that

country. Using GNP as a measure of the economic condition of a country implies that a higher

GNP as compared to a previous year is reflective of an improvement in the standard of living of a

countrys nationals.

For this paper the year-wise GDP growth measured at current and constant prices in percentage has

been used as the indicator for performance of the economic performance of Pakistan. The figures

have been taken from Economic Survey published by Government of Pakistan. GNP was not

considered a relevant variable for the purpose of this paper since cement is a product which uses

local raw materials, mainly limestone and clay.

B. Methodology and Hypotheses

The main object of the study is to know whether the key financial and economic indicators affect

profitability (ROA & ROE) of the firms in the cement sector of Pakistan.

In order to achieve this objective, we tested the following five hypotheses for profitability*:

Ho: Profitability* of the firm is not significantly affected by the average share price of the firms

Ho: Profitability* of the firm is not significantly affected by the liquidity as measured by its

current ratio

Ho: Profitability* of the firm is not significantly affected by financial leverage as calculated by

the Long-Term Debt to Total Capitalization ratio

Ho: Profitability* of the firm is not significantly affected by the YoY Growth in revenues

Ho: Profitability* of the firm is not significantly affected by the growth in GDP

* Includes both profitability measures, i.e., Return on Assets & Return on Equity

We ran panel regressions to test the above hypotheses. Panel data analysis facilitates analysis of

cross-sectional and time series data. The pooled regression, also called the constant coefficients

model, is one where both intercepts and slopes are assumed constant. The cross section company

data and time series data are pooled together in a single column assuming that there is no

significant cross section or inter temporal effects. Specifically, the econometric model is defined as

follows:

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PF = 0 + 1ASP + 2CR + 4LTDTC + 5YoYRG + 6GDP + (Equation I) Where,

PF = Profitability represented by ROA & ROE

ASP = Average Share

CR = Current Ratio

LTDTC = Long Term Debt to Total Capitalization

YoYRG = Year-on-year Growth in Revenues

GDP = Gross Domestic Product

= the error term with zero mean and constant variance

The possible expected effects of the said variables on firms profitability are shown in Table 1.

Table-1: Expected Relationships

Variable Measure (proxy) Expected relationship

with Profitability

Average Share

Price

Av. Price during

financial year Positive

Liquidiy Current Assets / Current

Liablities Negative

Leverage LTD / (LTD + Total

Equity) [%] Positive/Negative

YoY Growth in

Revenues In percentage Positive

GDP Growth In percentage Positive

C. Sample and Sources of Data

The study is limited to performance of the cement sector of Pakistan during 2005 - 2008. Due to

data constraints 12 out of 20 companies listed on the Karachi Stock Exchange were included in the

study. Financial data of these firms over years 2005 to 2008 was used. This translates into 48 firm-

year observations for panel regression, which can reasonably form results. The data has been

obtained from the financial statements of the respective cement companies. Data on GDP growth

has been taken from Economic Survey published by the Government of Pakistan.

IV. EMPIRICAL RESULTS

This section presents the results of the regression analysis. The interpretation and detailed

discussion of the empirical findings are also reported in the section. Finally, a possible explanation,

on the basis of the financial theory, is given to explicate the empirical findings.

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A. Regression Analysis Results

Using the pooled regression technique, we ran the regression of profitability on average share price

of the firm, liquidity (current ratio), leverage (long-term debt to total capitalization), growth in

revenues and GDP growth(GDP) with an aim to investigate whether these five variables have

significant explanatory power or not. The measures of profitability are taken as Return on Assets

and Return on Equity. The Average Share Price is taken in PKR. The remaining four variables

namely, liquidity, leverage, YoY growth in revenues and GDP growth are in percentage form. The

estimated results are reported in Tables 2 & 3.

Table-2: Pooled Regression Results (ROE)

Dependent Variable: ROE Method: Least Squares Sample: 1 48 Included observations: 48

Variable Coefficient Std. Error t-Statistic Prob.

C -0.473635 0.389756 -1.215209 0.2311 AVGPRICE 0.001695 0.001662 1.019602 0.3138 LTDBTCAP -0.339913 0.359477 -0.945576 0.3498

REVGROWTH 0.020546 0.137110 0.149853 0.8816 CR 0.060604 0.103750 0.584140 0.5623

GDPGROWTH 9.046482 4.105570 2.203465 0.0331

R-squared 0.204235 Mean dependent var 0.150290 Adjusted R-squared 0.109501 S.D. dependent var 0.350685 S.E. of regression 0.330928 Akaike info criterion 0.742636 Sum squared resid 4.599555 Schwarz criterion 0.976536 Log likelihood -11.82325 F-statistic 2.155885 Durbin-Watson stat 1.850517 Prob(F-statistic) 0.077344

The diagnostics R-squared and F-statistic (significant at 8%) are low as is usually remains the case

cross-section analysis, while DW statistics shows no auto-correlation problem. Moreover, all the

coefficients have the expected signs. Moreover low mean and high standard deviation of ROE

show that there is no consistency in profitability across companies in the cement sector.

The results are very interesting, as none of the firm specific factors is significantly linked to ROE.

The only factor, out of the variables considered, which has a significant effect on ROE is GDP

growth, as shown by t-statistic (significant at 3.3%). Since the demand for cement is closely

related to the real sector of the economy, the significant impact of GDP growth is not surprising.

Also, since cement pricing in Pakistan is usually controlled by a cartel of manufacturers, an

increase in demand resulting from growth in the real sector is usually accompanied by a rise in

cement prices and consequent increase in profitability (ROE). Similarly, the ROE would tend to go

down in the event of a slump in the economy.

Average share price is showing a weak positive linkage with ROE. This is understandable, as the

period under study witnessed some turbulent times in the equity markets. Moreover, this is

evidence, which shows that the share prices on KSE are driven more by speculation rather than

company or sector fundamentals like ROE.

Liquidity (current ratio) is found to have an insignificant positive relationship with ROE. This is in

slight variation with finance theory, although previous studies show that in certain peculiar

industry situations, a strong positive association of liquidity with profitability may also be

observed. In the case of the cement industry, there are slim chances of firms carrying excessive

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cash balances, since unlike most other industries they do not need cash to buy raw materials on a

regular basis. This is because a long-term investment by way of lease rights on deposits of lime

stone and clay, the main raw materials, has already been done while setting up the plant. As for

finished goods inventory, relatively high levels may result either from over estimation of demand

or the desire to maintain a high plant capacity utilization, in order to keep the per unit cost of

product on the lower side. Thus, a weak to moderate positive relationship between current ratio

and ROE would not be unreasonable to expect.

Leverage (long-term debt to total capitalization) is exhibiting a weak negative relationship with

ROE. While the sign of the relationship is consistent with most studies which find a strong

negative relationship between leverage and profitability, the weak linkage in our study needs to be

explained. In contrast to most other sectors, the cement sector firms are not competing for

purchasing raw materials since the main raw materials (lime stone and clay) have already been

procured through leasehold rights. The price paid for leasehold rights and depletion rate determine

the cost charged for these raw materials on the incomer statement. Due to differences in the price

of leasehold rights, and depletion rates, the per unit production cost is likely to be inconsistent

across the companies. A similar inconsistency in the depreciation charge per unit can be expected

because of the differences in plant procurement cost and the difference in the ages of the new and

old cement plants. Raw materials and depreciation costs, being a significant proportion of cost of

sales, the gross profit and operating profit per unit of production are bound to exhibit

inconsistencies across firms. Thus leverage alone cannot reasonably be expected to show a strong

relationship with ROE.

Year to year growth in revenues exhibits a weak positive linkage to ROE. Again a plausible reason

for this result is because of inconsistencies in gross profit and operating profit margins across

firms. In other words, a similar percentage increase in sales revenues in all the firms will result in

varying degrees of change in profitability (ROE) across the firms.

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Table-3: Pooled Regression Results (ROA) Dependent Variable: ROA Method: Least Squares Date: 03/28/09 Time: 16:12 Sample: 1 48 Included observations: 48


C 0.005019 0.098658 0.050875 0.9597 CR 0.002639 0.026262 0.100492 0.9204

GDPGROWTH 2.043024 1.039235 1.965893 0.0559 LTDBTCAP -0.254559 0.090994 -2.797541 0.0077

REVGROWTH 0.002023 0.034706 0.058280 0.9538 AVGPRICE 0.000923 0.000421 2.194870 0.0338

R-squared 0.396077 Mean dependent var 0.078496 Adjusted R-squared 0.324181 S.D. dependent var 0.101896 S.E. of regression 0.083767 Akaike info criterion -2.005084 Sum squared resid 0.294711 Schwarz criterion -1.771184 Log likelihood 54.12202 F-statistic 5.509057 Durbin-Watson stat 1.364606 Prob(F-statistic) 0.000548

We have taken the same firm specific external factor as determinants of ROA; another measure of

profitability. The diagnostics like R-squared, F-statistic (significant at 1%) and DW-statistic

(which lies close to the indecision zone) have no serious statistical problem and results are fine.

Again, low mean and relatively high standard deviation of ROA show that there is no consistency

in profitability across firms in the cement industry.

The results are broadly the same for ROA as were for ROE. Again, GDP growth is the only factor

that has a significant effect on cement sector ROA (significant at 6%). The probable explanation

for these results is the same as that for ROE. However, the results reaffirm that the firm level

factors are not consistently strongly linked to profitability across companies and the cement sector

as a whole primarily relies on robust real sector performance for its growth and profitability.

Moreover, all the coefficients have the expected signs thus reflecting well on estimation results.

To check whether the values of estimated parameters of model remain consistent through the

examined time period, we ran the Chows test on an overlapping sample by sequentially adding ten

points of data and computing the F-statistic and log likelihood to test the null hypothesis, that is,

parameters of the estimation model are stable over time. The estimated results are reported in

Tables 4 & 5.

Table-4: Estimates of Chows Test (Under ROE) Chow Breakpoint Test: 10

F-statistic 0.306383 Probability 0.929502 Log likelihood ratio 2.390535 Probability 0.880512

Chow Breakpoint Test: 20




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Table-5: Estimates of Chows Test (Under ROA) Chow Breakpoint Test: 10






Based on the calculated statistics both F-statistic and log likelihood ratio, the study is unable to

reject the null hypothesis that estimated parameters are consistent over time except for

insignificant log likelihood ratio at 30 break point in the case of ROE only. It implies that there is

no structural break in the model. Another way to check the reliability of the model is to determine

whether the estimated residuals are white noise. To proceed with this, we applied the augmented

Dickey-Fuller (ADF) test to the estimated residual from the model (presented in equation-I). The

results are presented in Tables 6 & 7.

Table-6: Augmented Dickey-Fuller Test Results (Under ROE) ADF Test Statistic -6.556299 1% Critical Value* -3.5778

5% Critical Value -2.9256 10% Critical Value -2.6005

*MacKinnon critical values for rejection of hypothesis of a unit root.

Augmented Dickey-Fuller Test Equation Dependent Variable: D(RESIDROE) Method: Least Squares Date: 03/28/09 Time: 16:42 Sample(adjusted): 3 48 Included observations: 46 after adjusting endpoints


RESIDROE(-1) -1.259511 0.192107 -6.556299 0.0000 D(RESIDROE(-1)) 0.370984 0.141712 2.617867 0.0122

C -0.010073 0.045391 -0.221919 0.8254

R-squared 0.533610 Mean dependent var -0.000678 Adjusted R-squared 0.511918 S.D. dependent var 0.440370 S.E. of regression 0.307655 Akaike info criterion 0.543320 Sum squared resid 4.070027 Schwarz criterion 0.662579 Log likelihood -9.496360 F-statistic 24.59879

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Table-7: Augmented Dickey-Fuller Test Results (Under ROA) ADF Test Statistic -5.923451 1% Critical Value* -3.5778

5% Critical Value -2.9256 10% Critical Value -2.6005

*MacKinnon critical values for rejection of hypothesis of a unit root.

Augmented Dickey-Fuller Test Equation Dependent Variable: D(RESIDROA) Method: Least Squares Date: 03/28/09 Time: 16:46 Sample(adjusted): 3 48 Included observations: 46 after adjusting endpoints


RESIDROA(-1) -0.967057 0.163259 -5.923451 0.0000 D(RESIDROA(-1)) 0.413539 0.139164 2.971597 0.0048

C -0.001157 0.010474 -0.110505 0.9125

R-squared 0.452501 Mean dependent var -0.000109 Adjusted R-squared 0.427036 S.D. dependent var 0.093797 S.E. of regression 0.070999 Akaike info criterion -2.389315 Sum squared resid 0.216756 Schwarz criterion -2.270055 Log likelihood 57.95424 F-statistic 17.76946 Durbin-Watson stat 1.852838 Prob(F-statistic) 0.000002

The ADF test results provide strong evidence to reject the null hypothesis that the estimated

residual has a unit root. This implies that the residuals are stationary. It means that the mean and

variance of the residuals do not vary much with time.

V. CONCLUSIONS AND POLICY IMPLICATIONS

In this study, data of a sample of 12 firms in the cement sector was analyzed through a pooled

regression model to ascertain whether the selected independent variables could serve as true

determinants of profitability. The data used in this study covered four financial years i.e. 2005 to

2008 or 48 firm-year observations for panel regression. The data analysis provides strong evidence

to come to the following conclusions:

1) GDP growth has a significant positive impact on profitability. 2) Average market price of share has a weak positive linkage with firm profitability 3) Liquidity has a weak positive association with profitability. 4) Financial leverage has a negative but insignificant influence on profitability in terms of

ROE but a more significant (at 10%) linkage with ROA.

5) Year on year growth in revenue has a positive but statistically insignificant linkage with profitability when measured as ROE. However year on year growth has a significant

positive linkage with profitability in terms of ROA.

The direction of relationship of chosen independent variables with profitability corroborates

conventional theory and is also generally supported by previous studies. However, the linkage of

profitability (both ROA and ROE) is significant only in the case of GDP growth. This means that

the selected firm specific factors are not important determinants of profitability (both ROA and

ROE) of cement sector firms in Pakistan. Thus, the results of the study cannot form the basis for

14

formulating any industry-wide policy guidelines for optimizing firm profitability. In other words it

may be concluded that firm specific factors impact on the profitability of individual firms in

different ways. Two plausible reasons for this are:

(i) Differences in firm-wide per unit production cost of raw materials, as leasehold rights for limestone and clay deposits, the main raw materials, may have been obtained by different

firms at widely varying rates.

(ii) Disparity in firm-wide plant depreciation cost per unit of production, due to wide fluctuations in book value of plant and machinery of individual firms resulting from

differences in age and procurement price of the cement plants of the companies included in

the sample.

However, for a firm confirmation of the above plausible reasons, further research would be

required. Also, since all possible independent variables have not been covered in this study, future

studies can probe into whether there are some other firm level factors which have a commonality

with respect to their impact on profitability of cement sector firms in Pakistan. Most importantly,

the impact of firm level factors may also be inconsistent due to wide discrepancies and errors in

the reported profitability figures. The discrepancies may result from the financial statements being

influenced by certain differences in accounting policies amongst firms and possible doctoring of

accounts in some cases.

15

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Annexure A

Names of Pakistans Cement Sector Firms Included in the Research

1. ATTOCK CEMENT PAKISTAN LIMITED 2. BESTWAY CEMENT LIMITED 3. DEWAN CEMENT LIMITED 4. DADABHOY CEMENT 5. MAPLE LEAF CEMENT 6. D.G. KHAN CEMENT COMPANY LIMITED 7. FAUJI CEMENT LIMITED 8. GHARIBWAL CEMENT 9. KOHAT CEMENT LIMITED 10. LUCKY CEMENT LIMITED 11. PIONEER CEMENT 12. CHERAT CEMENT

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