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Business Management DynamicsVol.1, No.7, Jan 2012, pp.12-21

Society for Business and Management Dynamics

Performance Analysis of Manufacturing Companies in PakistanMehran Ali Memon1* and Izah Mohd Tahir2*

Abstract

The objective of this paper is to examine the performance of fourteenmanufacturing companies in Pakistan using financial accounting ratios. Data

was collected from OSIRIS database sorted by total assets. The variables thatwill be used are: total assets; expenses; sales; profit before tax; and return onassets. Each variable will be compared and analysed during the 5 year-period,2006-2010. ENGRO being the largest company by total assets over 3 years(2006, 2007 and 2008) had more expenses, low sales, low profit before tax andreturn on assets compared to the other thirteen companies. FCC being thesecond largest company by assets showed high sales, profit before tax and returnon asset in 5 years (2006-2010). On the other hand, NRL being the fourthlargest company showed the highest sales in five years, lowest expenditures in2010 as compared to other thirteen listed companies but it has decreasing profitbefore tax and return on asset over the years; lowest profit before tax in 2010.The financial position of other listed companies is mixed. Correlation analysisshowed that total assets, sales and profit before tax are positively related

indicating economies of scale; large firms are able to take advantage of their size.Finally, this study concluded that higher expenses were the results of either theExpense Preference Behaviour Theory or slow growth rate of investment.

Key words: Performance,manufacturing companies, Pakistan

Available onlinewww.bmdynamics.comISSN: 2047-7031

INTRODUCTIONA business entity nowadays, has to be efficient in order to perform and stay in business. Many expertsdefine performance in different ways. Watkins (2007) defined performance as valuable results,accomplishments or contributions of an individual/team or an organization, regardless of preferred ormandated processes. Enos (2007) defined performance as achievement of tangible, specific, measurable,worthwhile and personally meaningful goals. Efficiency measurement is one aspect of a companysperformance. Efficiency can be measured with respect to maximization of output, minimization of cost ormaximization of profits. A company is regarded as technically efficient if it is able to obtain maximum

outputs from given inputs or minimise inputs used in the production of given outputs. The objective ofproducers is to avoid waste. Various studies have been carried out to examine the performance ofcompanies. Many studies have used financial ratios such as sales (Wang, 2003), return on assets (Lin et al.2005; Naser and Mokhtar, 2004), return on equity (Ponnu and Ramthandin, 2008), and return on investedcapital (Hsu and Liu, 2008).Measuring the efficiency is essential for this purpose as efficiency is an important characteristic oforganizational performance. In order to compete with other firms in international market, businessorganizations such as manufacturing companies, banks, private companies whether big or smallorganizations must reach to their optimal performance. Therefore, one of the major objectives in todaysworld of business is to improve the performance (Mohamad and Said, 2010b). Every country needs to seetheir organizations performing well with maximum efficiency and productivity. Hence, it is the focus ofall organizations to achieve this target in order to meet their goals.If we take an example of Pakistani manufacturing sector, as the study relates to this, it has significantcontribution in economic growth of Pakistan (Shah, 2011). It is one of the major sectors of Pakistan withshares in gross domestic product (GDP) about 18.7% in 2010-2011(Hassan, 2011). Public and privateinvestments are the main sources to boost growth rate. Investment in 2011 in manufacturing showed thatit has declined to 11% which is due to heavy decline in private investment. Furthermore, investment of

1 [email protected]@unisza.edu.my* Faculty of Business Management & Accountancy, University Sultan Zainal Abidin, Malaysia, Gong Badak Campus 21300Kuala Terengganu, Terengganu, MALAYSIA
http://www.bmdynamics.com/http://www.bmdynamics.com/mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]://www.bmdynamics.com/

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large scale manufacturing (LSM) showed a decline of 26.7% during the fiscal year of 2010-2011 (Shah,2011).Bouton and Sumlinski (2000) reported that higher income countries tend to have higher privateinvestment ratios than lower income countries. A report from Trade Development Authority of Pakistan(TDAP) in 2011 shows the total exports from June 2010 to January 2011 is about USD13.23 billion whichhas improved than last years corresponding period with USD10.78 billion. This year exports are; Textile

sector with 25.9% growth (USD7.450 billion) as compared to only USD5.918 billion in 2010; Food groupwith 13%; Petroleum & Coal with 26.4%; and Other Manufacturing with 7%, respectively. Moreover,SMEs are often called as backbone of economic growth of developing countries (OECD, 2002). InPakistan, its significance can be seen by GDP in the year of 2009-2010 in which real GDP grew by 3.8%(Shah, 2011).The objective of this study is to analyse the performance of manufacturing companies in Pakistan usingfinancial ratios. This paper is an exploratory study, therefore understanding the fundamental issues onperformance are important because this will lead to an extended research in the area.

PERFORMANCE ANALYSISPerformance is a quality of any company or firm which can be achieved by valuable results. For example,a firm having high return on assets (ROA) is said to be performing well. But having high ROA is not a

sign of good performance: there are some other variables to be considered such as sales, profit andexpenses which we will highlight in this paper.Performance can be analyzed by various methods, such as parametric (Stochastic Frontier Approach) andnon-parametric (such as Data Envelopment Approach). The focus of this paper is on accounting ratio, thenon-parametric method. Many researchers have focussed on this aspect. Milost (2003) assessedperformance by non-financial ratios. Pratheepkanth (2011) evaluated interrelation between capitalstructure and financial performance by ratio analysis. Marimuthu (2010) evaluated performance ofBumiputra-controlled companies by non-parametric financial ratios using five measures: return on assets(ROA); return on equity (ROE); price per earnings ratio (PE); debt per equity ratio (D/E); and earningsper share (EPS). Moreover, financial ratios have been used as mixed methods, for example, Bayyurt (2008)used financial ratios with DEA and Tran (2008) used it in multidisciplinary approach for performanceevaluation of companies.Financial ratios are very helpful and easy tool to identify weakness and strength (performance) of anycompany by looking at their financial statements. Through this, we can identify the ratios across time forexample the 5-year period which we have taken for this study and we can compare the ratios to othercompanies in different time periods. Beside its advantages, the disadvantage is that performance analysiscannot be done only by financial ratios which are based on traditional accounting data as they can nolonger meet information needs.Performance of any organization is directly related to efficiency. In other words, performance will behigher, if it achieves desired output with minimum consumption of input and time. A company istechnically efficient when it produces a certain level of output by using a minimum level of physicalinputs Wober (2002). There are numerous studies available which have been done on performanceevaluation and efficiency analysis to support and guide the business organizations to achieve theirobjectives. Farell empirically measured the efficiency for first time (Charnes et al. 1978) and developed anew tool called Data Envelopment Analysis (DEA) by generalizing the concept of single input, single

output technical efficiency measure of Farrell to the multiple inputs and multiple output case. Since then,during the three decades, studies related to performance evaluation has been done on profit and non-profit organizations such as Mohamad and Said (2010a) on Malaysian Food industries, Zheng (2010)onChinese Real Estate Firms, Sharma (2008) on Cement companies, Ar and Baki (2007) on Turkish glasscompany, Wu and Ho (2007) on Taiwanese integrated circuit industry, Wu et al. (2006) on RetailingIndustry, Jajri and Ismail (2006) on Malaysian Manufacturing Companies and Lin et al. (2005) onefficiency of shipping industry.Yusof et al. (2010) did a valuable work toevaluate operation performance of 14 Malaysian public listedcompanies for the period 2004- 2008 using DEA approach and by drawing the Performance Matrix which

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is based on the DEA Efficiency and Profitability index (return on assets). Inputs were taken as totalexpenses (financial and operating expenses) and total assets, and output taken was sales. The studyfound that the overall efficiency was around 50% and only one company was consistently efficient in 5years. Using the Performance Matrix, it was found that only three companies appeared as having highestaverage ROA among 14 companies and out of this only one company appeared in Quadrant 1 as Superstar. However, 3 out of 14 companies were found to have low efficiency and low profitability over 5

years.Mohamad and Said (2010b) attempted a very interesting work and assessed the performance of MB100Companies. They collected 100 largest listed companies from Malaysia Business magazine (2009). Sixoutputs were used: Rate of change of revenue; rate of change of net profit; rate of change of assets; thereturn on revenue; return on equity; and return on assets. Only one input is used: total operatingexpenditure (cost). By constant return to scale (CRS) assumption, 6% of the companies were found to beefficient. However, the 6% companies belonged to the bottom 20 by revenue. Those were also efficientunder variable return to scale (VRS) assumption. 13% of the companies were found as efficient underVRS assumption in which 99% companies have achieved technical efficiency more than 0.60. However,only one company scored 0.14. 80% companies were scale inefficient. By analyzing return to scale, sixcompanies were scale inefficient and exhibited decreasing return to scale (DRS). Furthermore, by slackanalysis, 22.2% in all outputs could be increased, and suggested a peer group analysis for non-performing

companies to improve their performance.Mustafa (2009) merged two approaches to assess the relative efficiency of Egyptian companies usingintelligent modelling techniques. Four inputs (assets, employees, market value and share price) and twooutputs (revenue and profit) were chosen.To estimateproduction function DEA-CCR and VRS and NN(Neural Network)models were employed to evaluate overall technical and scale efficiency. In this way,relative efficiency can be analyzed using percentage. The results indicated that the score ranged from 7%to 100% in both assumptions (CRS and VRS). Under CRS it was 47% and was 53% under VRS model.Only ten companies out of 62 companies were efficient.Wang (2008) conducted a study by evaluating the performance on SOEs (State Owned Enterprises) for2003. Seventeen companies were included in this study. Inputs were employee, total assets and theproportion of state- owned shares, and outputs were ROA and assets turnover. Researcher constructed anintelligent Decision Support System for Performance Evaluation (DSSPE) to evaluate the efficiency ofcompanies. Various DEA models were used in this research: BCC, CCR and Free Disposal Hull (FDH).The data were executed in each DEA model and relative efficiency was calculated in this way. The resultof CCR model, one electronic company was relatively efficient with efficiency score of one. Only fivecompanies were found as 100% pure technically efficient. On the other hand by FDH, seven companieswere efficient. So, only one company was found to be efficient by various models. Further, the study wasextended to slack analysis of BCC model. The results by slack analysis indicated that all inefficient SOEsshould decrease their inputs. For example, a company should be able to become efficient if the number ofemployees is cut, total assets decreased or proportion of state-owned shares decreased. Furthermore,reference set, frequency and rank were included in this study to improve the inefficient companies.However, the study on manufacturing industry of Pakistan is limited. Musleh-ud-din et al. (2007)investigated the technical efficiency of large-scale manufacturing sector in Pakistan using DEA approach.Data were collected from 101 industries for two periods: 1995 to 1996; and 2000 to 2001. Data from 1995-96 were obtained from Census Manufacturing Industries, and 2000-01 were obtained from summary

tables prepared by Federal Bureau of Statistics. Output used was contribution of GDP and inputs wascapital, labour, industrial cost and non- industrial cost. DEA technique was employed to evaluateproduction frontier with Output-Oriented Model. With CRS assumption, mean efficiency score hasincreased from 0.23 to 0.42 in the two periods. Out of the top five most efficient industries, only twoindustries maintained their ranking in 2000-01. With VRS assumption, it was found that the meanefficiency score increased from 0.31 in 1995-96 to 0.49 in 2000-01 showing an increase in technicalefficiency of large-scale manufacturing sector.

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EXPENSE PREFERENCE BAHAVIOURTHEORYExpenses are an input which can have negative effect on company, especially when managers prefermore to spend. However, owners will definitely oppose to it. For example: a manager, who allowsspending more on staff and other utilities, is said as, having expense preference behaviour. In this contextThe Expense Preference Theory assumed that staff expenditures have an influence not on productionbut on sales, and thus they affect the level of profits (Furubotn and Richter 2000). Moreover, Pegels (2003)

stated that, the managers of firm in which ownership is segregated from control will not minimize cost.The managers of these institutions are utility maximizers who will spend money beyond the costminimizing amount on salaries, goods and services (inputs) that they prefer.Gropper and Oswald (1996) suggested two approaches to Expense Preference Behaviour which had beenadvanced. First, it is hypothesized that managers view the maximization of true profits and themaximization of true utility as two separate decisions. Accordingly, managers choose to keep the firm'soutput and some of its inputs at the profit-maximizing level, but spend excessively on labour. Second,those managers treat profit and utility decisions as one single decision to maximize utility. EarlierEdwards (1977) did empirical work for the Expense Preference Theoretical Framework, and argued thatthis theory better explained the general behaviour of managers in regulated firms than in a profitmaximizing framework.

DATA AND METHODOLOGYIn this paper, fourteen companies are selected based on the availability of their financial variables that werequire for 5 years; 2006 to 2010. The list of companies is shown in Table 1.

Insert Table 1 here

Our analysis will be based on five variables: total assets as indicator of size; total expenses as indicator forcost; sales as indicator for revenue; profit before tax and return on assets (ROA) as indicators forprofitability. Each variable will be compared for each company for each year. The variables are as follows:

Total assets Expenses Sales Profit before tax Return on assets (ROA)

Variables are selected on the basis of previous researches as shown in Table 2.Insert Table 2 here

ANALYSISTables 3 to 7 show the data of fourteen publicly quoted manufacturing companies sorted by total assetsfor 5 year-period (2006-2010). It is collected from OSIRIS data base under classification of NAICS 2007 inthe form of financial statements. The values are given in thousand US dollars and ROA is showed as apercentage. The change in trend of listed companies can be seen year by year from 2006-2010 in ascendingorder. ROA as a percentage shows how profitable a company's assets are in generating revenue. Forexample, total assets of ENGRO gradually increased to the highest during 2010 from USD329 million in2006 to USD1.9 billion in 2010. Assets of other companies have changed over the years. The smallestcompany LIBM, has total assets increased from USD45 million in 2006 to USD251 million in 2010.

Insert Tables 3 to 7 here

Total expenses over 5 years show NRL with the highest expenses in 2008 with USD1.8 billion, and then

gradually decreased to USD127 million in 2010. ENGRO shows a gradual increase from USD519 millionin 2006 to USD511 million in USD 1.9 billion. FCCL and LIBM show low expenses over 5 years withUSD58 million and USD64 million in 2006 to USD44 million and USD65 million in 2010 respectively. Allother companies financial position is mixed. NRL, the leading company has the highest sales in 2008with USD1.8 billion. Then, FFC at the second place, with sales of USD726 million in 2008 increased toUSD 1.02 billion in 2010. ENGRO shows third highest sales in 2010 with USD933 million having somefluctuation in 5 years. Small and medium sized companies show slight changes over the years.In terms of PBT, FFC has the highest PBT, USD162 million in 2006 and increased to USD274 million in2010. While, SIEM being the eleventh largest company by total assets, show slightly increase in 2006 from

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USD24 million to USD32 million in 2007 and then decreased to USD18 million in 2010. Other companiesshow slight increase over the five-year period. FFC shows highest ROA achievement over 14 companiesin 2010 with 31%. FCCL with the second highest ROA of 28.68% in 2006 has decreased to 1.21% in 2010.ROA for other companies fluctuate during the five year period.In terms of assets, total assets has increased for all the companies from 2006 to 2010 indicating thatcompanies had expanded their size during the period. For expenses, the cost has increased from 2006 to

2009 but slightly decreased in 2010. Sales had increased from 2006 to 2008, decreased in 2009 butincreased again in 2010 indicating that sales fluctuated over the five year period. On average, thefourteen companies profit before tax has increased from 2006 to 2008, slightly decreased in 2009 butclimbing up again in 2010. Looking at ROA, the average ROA has decreased over the years under study.As a conclusion, the financial position of the fourteen manufacturing companies in Pakistan had shown aslight fluctuation in terms of size, expenses, sales, profit before tax and return on assets.Table 8 presents the descriptive statistics of financial position of fourteen companies during 2006 to 2010.The mean for total assets has increased over the period under investigation. On the other hand, the meanfor total expenses has increased from 2006 to 2008 but decreased slightly in 2009 and 2010. Similar patterncan be seen with the sales and profit before tax and return on asset.

Insert Table 8 here

Table 9 presents the Pearson correlation for all the variables used. The table shows that the coefficients of

total assets to sales and PBT are positive indicating large companies are able to reap more profits.Insert Table 9 here

CONCLUSION

The aim of this study is to examine the performance of top fourteen Pakistani firms using financialaccounting ratios. Descriptive statistics of the accounting variables are employed. The study concluded asENGRO being the largest company by total assets over 3 years (2006, 2007, 2008) spent more, making lowsales, having less PBT and ROA than the other thirteen smaller companies: for example, FCC beingsecond largest company by assets shows high sales, high PBT and ROA during the five year period. Onthe other hand, NRL being the fourth largest company by total assets shows highest sales in five years,lowest expenditures in 2010 as compared to other thirteen listed companies but it has decreasing PBT andROA during the period under investigation.Finally, this study concludes that higher expenses incurred in highlighted companies are as a result of

Expense Preference Behaviour Theory and low productivity growth. Kemal (2006) suggested that,manufacturing sector of Pakistan suffered from the problems of slow growth of investment, output andexports, allocative, technical and X-inefficiencies, poor quality of products and low level of R & Dactivities resulting in slow growth rates of productivity. This made Pakistani products incompetent in theworld market. Moreover, low rate of investment will cause low rate of growth.This paper is considered as an exploratory analysis. Therefore, future research will focus on theperformance and efficiency using a more appropriate model such as Data Envelopment Analysis (DEA).

REFERENCESAr, I. M., and Baki, B. (2007). Measuring and Evaluating Efficiency of Glass Company through Data

Envelopment Analysis. Problems and Perspectives in Management, 5(1), 72-81.Bayyurt, N. (2008). Performance Measurement of Turkish and Chinese Manufacturing firms: A

comparative Analysis. Eurasian Journal of Business and Economics ,1(2), 71-83.Bouton, L., and Sumlinski, M. A. (2000). Trends in Private Investment in Developing Countries:

International Finance corporation.Charnes A., Cooper, W. W., and Rhodes. E. (1978). Measuring the efficiency of decision making units.

European Journal of Operational Research, 2, 429-444.Edwards, F. R. (1977). Managerial Objectives in Regulated Industries. Journal of Political Economy, 85(1),

147-161.Enos, D. D. (2007). Performance improvement: Making it Happen. Second edition. Auerbach Publications,

Taylor and Francis Group.

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Furubotn, E. G., and Richter, R. (2000). Institutions and economic theory: The contribution of the newinstitutional economics: University of Michigan press.

Gropper, D. M., and Oswald, S. L. (1996). Regulation, deregulation and managerial behaviour. AppliedFinancial Economics, 6, 1-7.

Hassan., Z. (2011). Economic survey of Pakistan 2010-2011: Growth and Investment: Government ofPakistan, Ministry of Finance.

Hsu, C. W. and Liu, H. Y. 2008. Corporate Diversification and Company Performance: The ModeratingRole of Contractual Manufacturing Model. Asia Pacific Management Review. 13(1) pp. 345-360.

Jajri, I. and Ismail R. I. (2006). Technical efficiency, technological change and total factor productivitygrowth in Malaysian manufacturing sector. MPRA Paper No. 1956 Available at:http://ideas.repec.org/p/pra/mprapa/1956.html

Kemal, A. R. (2006). Key issues in Industrial Growth in Pakistan. The Lahore Journal of Economics, Specialedition, 49-74.

Lin, W.C., Liu, C. F., and Chu C. W. (2005). Performance Efficiency Evaluation of the Taiwan's ShippingIndustry: An Application of Data Envelopment Analysis. Eastern Asia Society for TransportationStudies, 5, 467-476.

Marimuthu, M. (2010). Bumiputera-Controlled companies: Performance evaluation using a Non-Parametric Approach. International Journal of Economics and Finance, 2(2), 178-185.

Milost, F. (2003). Non-financial performance ratios as a Management tool. Paper presented at theProceedings of the 4th International Conference of the Faculty of Management Koper, Universityof Primorska.

Mohamad, N. H., and Said, F. (2010a). Decomposing productivity growth in Malaysian foodManufacturing Industry.African Journal of Business Management, 4(16), 3522-3529.

Mohamad, N. H., and Said, F. (2010b). Measuring the performance of 100 largest listed companies inMalaysia.African Journal of Business Management, 4(13), 3178-3190

Musleh-ud-din, Ghani, E., and Mahmood, T. (2007). Technical Efficiency of Pakistan's ManufacturingSector: A Stochastic Frontier and Data Envelopment Analysis. The Pakistan Development Review,46(1), 1-18.

Mustafa, M. M. (2009). Modelling the competitive market efficiency of Egyptian companies: Aprobabilistic neural network analysis. Expert Systems with Applications, 36, 88398848.

Naser, K. and Mokhtar, M. Z. (2004). Determinants of Corporate Performance of Malaysian Companies,Paper presented at the Fourth Asia Pacific Interdisciplinary Research in Accounting Conference,

July, Singapore.Organization for Economic Cooperation and Development-OECD (2002). Official Development

Assistance and Private Finance.Ponnu, C. H., and Ramthandin, S. (2008). Governance and Performance: Publicly Listed Companies in

Malaysia.Journal of Business Systems, Governance and Ethics. 3(1), pp. 35-53.Pratheepkanth, P. (2011). Capital structure and financial performance: Evidence from selected Business

companies in Colombo stock exchange Sri Lanka. Journal of Arts, Science & Commerce, II (2), 171-181.

Pegels, C. C. (2003). Proven solutions for improving health and lowering health care costs: Informationage publishing (IAP)

Shah, A. (2011), Pakistan Economic Survey 2010-2011: Manufacturing and Mining. Government of

Pakistan, Ministry of Finance.Sharma, S. (2008). Analyzing the Technical and Scale efficiency Performance: A case study of Cement

firms in India.Journal of Advances in Management Research, 5(II), 56-63.Tran, K. (2008). Multi Disciplinary Approach to evaluate companies performances. Available at:

http://www.kevinidea.com/my-books-and-publications/multi-disciplinaryapproachtoevaluatecompaniesperformances

Wang, W. (2003). Ownership Structure and Company Performance: Evidence from Taiwan. Asia PacificManagement Review, 8(2), 135-160.
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Wang, W.K. (2008). An intelligent Support System for Performance Evaluation of State OwnedEnterprises of Electronic Industry. Citeserx Digital Library, 40-51.

Wober, K. W. (2002). Benchmarking in tourism and hospitality industries: The selection of benchmarkingpartners. CABI publishing.

Watkins, R. (2007). Performance by design: The systematic selection, Design and Development ofPerformance Technologies that produce useful results (Vol. III). Human Resource Development

Press.Wu, C.C., Kao, S.C., Wu, C.H., and Cheng, H.H. (2006). Examining Retailing Performance Via Financial

Index.Asia Pacific Management Review, 11(2), 83-92.Wu, D. D., & Ho, C.-T. B. (2007). Productivity and efficiency analysis of Taiwan's integrated circuit

industry. InternationalJournal of Productivity and Performance Management, 56(8), 715-730.Yusof, K. N. C. K., Razali, A. R. and Tahir, I. M. (2010). An Evaluation of Company Operation

Performance Using Data Envelopment Analysis(DEA) Approach: A study on Malaysian PublicListed Companies. International Business Management, 4(2), 47-52.

Zheng, X. (2010). Performance and Efficiency Assessment of Listed Real Estate Companies: An empiricalstudy of China. European Real Estate Society (ERES), 1-13.

Table 1: List of Companies and Abbreviations

Company Ticker no. Company Ticker no.Fauji Fertilizer Company FFC ICI Pakistan Limited ICI

Nishat Mills Limited NML Indus Motors Corporation Limited INDU

Ibrahim Fibre Limited IBFL Seimens (Pakistan) EngineeringCompany Limited

SIEM

Fauji Fertilizer Bin QasimLimited

FFBL Nestle Pakistan Limited NESTLE

National Refinery Limited NRL Nishat (Chunian) Limited NCL

Lucky Cement Limited LUCK Fauji Cement Company Limited FCCL

Engro Corporation Limited ENGRO Liberty Mills Limited LIBM

Source: OSIRIS database

Table 2: Variables ReferencesNo Variables References

1 Total Assets (Yusof et al.,2010); (Wu & Ho, 2007); (Lin, et al., 2005); (Wang, 2008);(Mustafa, 2009);

2 Total Expenses (Yusof et al., 2010)

3 Sales (Yusof et al., 2010); (Lin, et al., 2005); (Wu et al., 2006); (Sharma, 2008); (Wang.2008).

4 Profit before tax (Wu and Ho, 2007)

5 Return on assets (Lin et al., 2005); (Wu and Ho, 2007); (Wang. 2008); (Mohammad and Said,2010b).

Table 3: Pakistans Public Listed Companies, by total assets, in TH USD (2006)

No Company Total Assets Expenses Sales PBT ROA1 FFC 820,517 678,811 733,461 162,711 19.83

2 NML 508,783 274,013 276,444 29,186 5.74

3 IBFL 508,783 283,459 285,551 30,673 9.85

4 FFBL 454,404 221,849 241,428 61,672 13.57

5 NRL 413,556 1,311,953 1,342,328 87,317 21.11

6 LUCK 391,988 104,014 132,493 42,363 10.81

7 ENGRO 329,198 311,095 332,250 47,887 14.55

8 ICI 278,911 301,303 320,771 32,444 11.63

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9 INDU 262,553 557,701 584,703 67,582 25.74

10 SIEM 212,794 343,556 353,553 24,906 11.70

11 NESTLE 212,218 340,529 361,649 32,921 15.51

12 NCL 157,062 105,107 108,701 5,655 3.60

13 FCCL 102,849 58,586 71,123 29,498 28.68

14 LIBM 45,036 64,335 62,786 4,034 8.96

Table 4: Pakistans Public Listed Companies, by total assets in TH USD (2007)

No Company Total Assets Expenses Sales PBT ROA

1 FFC 868,258 649,780 664,625 170,008 19.58

2 ENGRO 799,952 519,378 557,338 64,567 8.07

3 NML 650,678 288,166 283,865 30,058 4.62

4 NRL 539,330 1,460,441 1,508,971 100,702 18.67

5 FFBL 474,447 178,936 199,980 63,703 13.43

6 LUCK 425,029 184,988 206,896 44,452 10.46

7 IBFL 341,490 270,141 269,700 29,223 8.56

8 ICI 303,453 351,681 375,612 42,538 14.02

9 SIEM 289,588 394,613 360,757 32,360 11.17

10 NESTLE 258,876 467,105 461,207 41,649 16.09

11 INDU 258,831 615,487 645,401 69,883 27.00

12 NCL 161,161 138,243 126,855 1,837 1.14

13 FCCL 105,757 47,764 57,223 13,023 12.31

14 LIBM 49,212 72,524 73,165 5,429 11.03



1 ENGRO 1,021,543 511,940 518,004 65,539 6.42

2 FFC 934,386 665,469 726,108 174,494 18.67

3 NRL 682,543 1,840,702 1,894,908 129,338 18.95

4 NML 669,123 275,665 282,182 93,590 13.99

5 FFBL 591,313 332,773 339,083 55,689 9.42

6 LUCK 501,445 220,375 248,355 33,780 6.74

7 IBFL 349,389 304,477 315,607 30,895 8.84

8 SIEM 317,489 351,826 344,468 32,859 10.35

9 ICI 233,516 351,314 351,449 35,561 15.23

10 NESTLE 210,930 443,721 432,171 28,161 13.35

11 INDU 201,347 592,142 606,669 51,870 25.76

12 FCCL 182,401 48,516 51,931 6,657 3.65

13 NCL 175,375 143,825 133,834 1,105 0.63

14 LIBM 48,654 62,889 67,399 5,556 11.42

Table 6: Pakistans Public Listed Companies, by total assets, in TH USD (2009)No Company Total Assets Expenses Sales PBT ROA

1 ENGRO 1567767 694212 690126 60077 3.83

2 FFC 832,766 841,720 865,320 200,099 24.03

3 NML 612,460 300,025 293,285 24,407 3.99

4 NRL 520,271 1,358,155 1,346,344 34,568 6.64

5 LUCK 471,711 276,121 323,511 63,608 13.48

6 FFBL 429,904 423,156 435,835 68,930 16.03

7 IBFL 325,332 283,861 271,037 23,666 7.27

8 SIEM 291,650 449,904 434,611 25,782 8.84

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9 FCCL 263,504 59,207 65,298 17,466 6.63

10 ICI 254,234 344,291 337,393 36,463 14.34

11 INDU 254,154 464,248 465,227 25,139 9.89

12 NCL 243,390 132,363 122,430 2,049 0.84

13 NESTLE 220,582 418,091 488,419 49,681 22.52

14 LIBM 105,522 62,234 64,240 7,439 7.05



1 ENGRO 1,922,493 1,284,487 933,089 96,570 5.02

2 FFC 863,565 983,811 1,028,513 274,240 31.76

3 NML 842,799 315,852 380,912 100,749 11.95

4 NRL 604,225 127,894 1,289,281 60,091 9.95

5 LUCK 448,265 264,333 286,775 39,988 8.92

6 FFBL 412,270 467,597 504,682 113,005 27.41

7 NCL 402,864 157,545 156,132 12,327 3.06

8 INDU 317,543 691,364 703,145 61,343 19.32

9 IBFL 316,123 326,957 317,374 45,561 14.41

10 FCCL 313,350 46,024 44,562 3,802 1.21

11 SIEM 284,419 328,267 304,211 18,291 6.43

12 NESTLE 267,787 591,281 600,710 66,458 24.82

13 ICI 257,035 414,058 409,866 43,536 16.94

14 LIBM 251,744 69,024 65,671 6,111 2.43

Table 8: Descriptive Statistics of the Variables Used

Year Statistics Total Assets Expenses Sales PBT ROA (%)

2006 Mean 335,618 354,022 371,946 47,061 14.38

Std. Deviation 201,508 327,465 336,105 40,211 7.23

Minimum 45,036 58,586 62,786 4,034 3.60

Maximum 820,517 1,311,953 1,342,328 162,711 28.68Coeff. of Variation 0.60 0.92 0.90 0.85 0.502007 Mean 394,719 402,803 413,685 50,674 12.58

Std. Deviation 247,870 359,344 371,563 43,617 6.50

Minimum 49,212 47,764 57,223 1,837 1.14

Maximum 868,258 1,460,441 1,508,971 170,008 27.00

Coeff. of Variation 0.63 0.89 0.90 0.86 0.52

2008 Mean 437,104 438,974 450,869 53,221 11.67

Std. Deviation 301,956 442,463 457,276 49,363 6.65

Minimum 48,654 48,516 51,931 1,105 0.63

Maximum 1,021,543 1,840,702 1,894,908 174,494 25.76


2009 Mean 456,661 436,256 443,077 45,670 10.38Std. Deviation 371,438 343,355 342,439 48,877 6.89

Minimum 105,522 59,207 64,240 2,049 0.84

Maximum 1,567,767 1,358,155 1,346,344 200,099 24.03

Coeff. of Variation 0.81 0.79 0.77 1.07 0.662010 Mean 536,034 433,464 501,780 67,291 13.12

Std. Deviation 447,959 354,896 371,588 69,037 9.77

Minimum 251,744 46,024 44,562 3,802 1.21

Maximum 1,922,493 1,284,487 1,289,281 274,240 31.76

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Coeff. of Variation 0.84 0.82 0.74 1.03 0.742006-2010 Mean 432,027 413,104 436,271 52,783 12.43

Std. Deviation 323,568 358,353 369,682 50,307 7.41

Minimum 45,036 46,024 44,562 1,105 0.63

Maximum 1,922,493 1,840,702 1,894,908 274,240 31.76


Table 9: Pearson Correlations of the Variables Used

Total Assets Expenses Sales Profit before tax ROA (%)

Total Assets 1

Expenses .505** 1

Sales .464** .920** 1

Profit before tax .584** .603** .614** 1

ROA (%)-.027 .408** .426** .638** 1

bmd110175_12_21

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