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    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

    Variable Coefficient Std. Error t-Statistic Prob.

    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

    F-statistic 0.455884 Probability 0.835985 Log likelihood ratio 3.515156 Probability 0.741952

    Chow Breakpoint Test: 30

    F-statistic 1.882665 Probability 0.110724 Log likelihood ratio 13.09951 Probability 0.041483

  • 12

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

    F-statistic 1.961596 Probability 0.106782 Log likelihood ratio 11.02111 Probability 0.050963

    Chow Breakpoint Test: 20

    F-statistic 0.859429 Probability 0.517084 Log likelihood ratio 5.142404 Probability 0.398750

    Chow Breakpoint Test: 30

    F-statistic 1.147415 Probability 0.352656 Log likelihood ratio 6.749277 Probability 0.239965

    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

    Variable Coefficient Std. Error t-Statistic Prob.

    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

  • 13

    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

    Variable Coefficient Std. Error t-Statistic Prob.

    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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  • 17

    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