Term Paper

Department of EconomicsUniversity of the Punjab, Lahore

Masters in Business EconomicsSemester # 4

Analyzing the Financial Performance of Manufacturing Sector in Pakistan: Application of Altman Z-Score FormulaByMuhammad Bilal Latif

Date: 18/07/2012

Supervisor: Sir Azmat Hayat

Acknowledgement

First of all I am extremely grateful to Almighty ALLAH, The Most Beneficent; The Most Merciful, WHO blessed me with the strength to complete this term paper on time. I would like to thank my teacher, Sir Irfan, who helped me in different queries regarding this term paper.I would also like to thank my supervisor, Sir Azmat Hayat, who guided me well from beginning till end of this term paper.In preparing this term paper, a considerable amount of thinking and informational inputs from various sources were involved. I express my sincere gratitude to everyone who contributed towards making this report possible.

I.

AbstractThe main aim of this study is to analyze the financial performance/soundness (specifically in terms of solvency position). For this purpose, twelve companies from manufacturing sector of Pakistan have been selected by keeping in mind the easiness to get the data for evaluation. The evaluation has been performed on last 3 years data from 2009 to 2011. Altman-Z Score formula has been used to analyze the performance, which constitutes of 5 different ratios. The financial performance of the companies is mixed. The results show that 4 out of 12 companies performing consistently by sustaining their solvency level in Safe Zone (strong solvency position) for all the three years. 4 companies having their solvency level shifting either from Safe to Grey Zone (average solvency position) or Grey to Safe Zone. 1 company whose solvency position was average in 2009 but in following 2 years its solvency position turned to Distress Zone (weaker solvency position). Only a single company has weakest solvency position throughout the analyzed period.

II.

Table of Contents1.Introduction12.Literature Review23.Data and Methodology43.1Research Sample43.2Data43.3Methodology43.3.1Z-Score Formula53.3.1.1X1, Working Capital/Total Assets53.3.1.2X2, Retained Earnings/Total Assets53.3.1.3X3, Earnings Before Interest & Taxes/Total Assets63.3.1.4X4, Book Value of Equity/Total Liabilities63.3.1.5X5, Sales/Total Assets63.3.1.6Decision Parameter64.Calculation on Data7Z-Score For The Year 20097Z-Score For The Year 20107Z-Score For The Year 201185.Analysis8Z-Score Throughout The Analyzed Period96.Conclusion & Recommendations9Reference10Appendix11

1. IntroductionManufacturing sector is an important part of Pakistans economy. It is the second largest sector in the economy of Pakistan after agriculture sector. The manufacturing sector grew at an average rate of 8% from 1960s to 1980s, but fell to 3.9% during the 1990s. This was mainly caused by the reduction in investment levels due to lack of continuity and consistency in policies. Political instability, law and order situation in the major industrials centers, transport bottlenecks, as well as unreliability and inadequate availability of power supply at affordable rates were additional factors pulling down this sector. Pakistans manufacturing sector has experienced double-digit growth in recent years, from 2000 to 2007, with large scale manufacturing growing from a minimal 1.5% in 1999 to a record 19.9% in 2004-05 and averaged 8.8% by the end of 2007. Its contribution is about 18.9% in 2011s GDP and contributing 62.2% in national taxes. In manufacturing, large scale manufacturing plays a vital role and accounts for nearly 70% of overall manufacturing. Manufacturing sector is constituted of automobile, cement, chemicals, food, gas, oil, textile etc.. Pakistans manufacturing industry is heavily dominated by food, textiles and apparel and leather industries to the extent of over 50%. Sound financial health of a company is the guarantee not only to its shareholders but it is equally important for the employees and as well as for entire economy. It is very important to find out that the companies in this sector making money so that when time comes they can meet the liquidity and solvency needs. As an important sector in the overall economic growth, manufacturing sector requires an in-depth analysis to measure its financial performance. So the purpose of this paper is to analyze the financial soundness/performance of different manufacturing companies currently working in Pakistan and to find out the level of their performance e.g. how good they are doing.This analysis is helpful in a way that the results of the techniques used in analysis present the views on collective basis as well as on individual basis. It will help the manufacturing companies to take correct measures to improve companys financial position. The companies have been taken from Pakistan, so this study deals only with Pakistan. But the technique which is used to measure the performance can be applied globally.The analytical research has been done in the study. Section 1 is related to introduction. Section 2 deals with the literature review. Section 3 is about data and methodology. Section 4 is about calculation on data and section 5 deals with the analysis. In section 6, conclusion and recommendations have been discussed.1

2. Literature ReviewTo determine the efficiency of concrete companies, ranked in top 1000 manufacturing firms trading in ISE (Istanbul Stock Exchange) in 2002, a study was conducted by Bayyurt and Sagbansua (2007). They used multi-criteria Data Envelopment Analysis methodology (DEA is a linear programming application that compares a number of service units of same type based on their inputs and outputs. The model solution result indicates if a particular unit is less productive compared to the other units.). Current ratio, leverage, inventory turnover ratio, machinery and equipment, size of the company in terms of share holders equity and cash flows were used as inputs of the model. Profitability, productivity and stock performance of the company were used as outputs for the study. They selected 11 companies in Turkey Efficiencies of the companies were measured with the data provided by Istanbul Stock Exchange (ISE) and Istanbul Chamber of Industry (ICI). The solution to the DAE model was carried out using the optimization modeling system for linear programming called Microsoft Excel Solver. The DAE results (Efficiency Scores) for the 11 companies are as Afyon, Bolu, Cimsa, Goltas, Hazndr and Nigde having efficiency scores equal 1, while Akensa, Bati, Bursa, Cment and Ecyap having efficieny scores within the range of 0.6361 to 0.9581.The companies having score = 1 are comparatively efficient companies and the companies with score < 1 are relatively inefficient companies. According to their analysis, 5 out of 11 companies were found inefficient. These companies are Akensa, Bati, Bursa, Cment and Ecyap. The distribution of inefficient companies over efficiency scores range from 0.6361 to 0.9581. The relatively most inefficient company was Bati.

Mohamad and Said (2010) measured the performance of 100 listed companies in Malaysia. A modified strictly out-put oriented Data Envelopment Analysis (DEA) model was used to measure the relative performance of each company by utilizing a list of normalized performance indicators. The basic data were obtained from Malaysian Business Magazine (2009) for the financial year end that falls on or before 31st July. Only the companies which have been listed at Bursa Malaysia for at least two years were surveyed. Their financial performance was broken down into 6 sub listings including turnover, net profit, equity and assets. But no information was furnished on input factors relating to labor, capital and expenditure. The list also included 17 GLCs. The indicators which were used are; Input (X): cost approximated as net profit less revenue, Output 1 (Y1): Rate of change of revenue, Output 2 (Y2): Rate of change of net profit, Output 3 (Y3): Rate of change of assets, Output 4 (Y4): Return on revenue, Output 5 (Y5): Return on equity, Output 6 (Y6): Return on assets. The DEA scores indicated that only 6 and 19% of the companies are operating on the best practice frontier under the assumptions of constant return to scale (CRS) and variable return to scale (VRS) respectively. No company exhibited the increasing return to scale (IRS). Most of the 2

relatively large (revenue-top-ranked) companies showed serious scale inefficiency and exhibited decreasing return to scale. Ranking based on the performance index revealed that top ranked companies by revenue are not necessarily top-ranked performers. Although 10 of the 17 government linked companies, GLCs are top-20 by revenue, only one remains in the top-20 ranking by DEA. 3 GLCs from bottom-20by revenue join the top performers exhibiting full scale efficiency. Non-GLCs dominated 75% of the top-20 DEA ranking.

Performance analysis of manufacturing companies in Pakistan was studied by Memon and Tahir (2012) with the help of 5 different variables. Those variables were total assets (indicator for size), expenses (indicator for cost), sales (indicator for revenue), profit before tax and return on assets (indicator for profitability). They selected 14 companies to check their performances over the five years from 2006 to 2010. They also used the correlation analysis to determine the relationship among total assets, sales and profit before tax. Their study concluded that ENGRO being the largest company by total assets over 3 years (2006, 2007 & 2008), spent more, making low sales, having less profit before tax and return on assets than the other thirteen companies. The total assets of ENGRO increased to the highest during 2010 from USD 329 million in 2006 to USD 1.9 billion in 2010. The smallest company LIBM (Liberty Mills Limited) has total assets increased from USD 45 million to USD 251 million. FCC (Fauji Cement Company) being the second largest company by assets showed high sales, profit before tax and return on assets in 5 years (2006-2010). FCC and LIBM showed less expenses over 5 years with USD 58 million to USD 44 million and USD 64 million to USD 65 million respectively. NRL (National Refinery Limited), the fourth largest company showed the highest sales (USD 1.8 billion in 2008) in 5 years, lowest expenditures in 2010 as compared to other thirteen companies but with decreased profit before tax and decreased return on assets. Total expenses of NRL decreased from USD 1.8 billion to USD 127 million. THE FFC (Fauji Fertilizer Company) showed the second highest sales with USD 726 million in 2008 and increased to 1.02 billion in 2010.ENGRO showed the third highest sales in 2010 with USD 933 million. The financial position of other companies was mentioned as average. Correlation analysis showed that total assets, sales and profit before tax are positively correlated, indicating the economies of scale; large firms are able to take the advantage of their size. And they finally concluded that higher expenses were due to either the Expense Preference Behavior Theory (Expenses are input which can have negative impact on company, especially when manager prefers more to spend on non-creative utilities) or slow growth rate of investment.

Memon and Tahir (2012) conducted a study to evaluate the relative efficiency of 49 companies in Pakistan using a non-parametric approach-data envelopment analysis. Data were gathered from the financial statements for the period of 2008-2010. The relative efficiency of each company across the three years periods was examined. They used a performance matrix to 3

identify the performance of the companies. This matrix was divided into 4 quadrants based on vertical axis with profitability ratio (ROA; return on assets) and the horizontal axis with DEA efficiency. The quadrants are; Quadrant 1 (Super Star) represents High Efficiency High Profitability, Quadrant 2 (Cash Cow) represents Low Efficiency High Profitability, Quadrant 3 (Question Marks) represents High Efficiency Low Profitability and Quadrant 4 (Problem Child) represents Low Efficiency Low Profitability. The DEA results under the CRS (constant returns to scale) technology assumption showed that 8 companies are considered technically efficient while the average overall technical efficiency varies from 0.83 to 0.86. When the aggregate efficiency is decomposed into pure technical efficiency and scale efficiency using VRS (variable returns to scale) production function, it is found that the measure of inefficiency is pure technical inefficiency rather than scale inefficiency. Most of the companies are found operating under increasing returns to scale. Their result indicated that on average, 13 out of 49 companies are superstar characterized by high efficiency and high profitability while 20 companies are problem child characterized by low efficiency and low profitability.In almost all previous studies, the same methodology (Data Envelopment Analysis) has been used. So there is a gap regarding the use of methodology. No one has used ratio techniques like Z-Score or DuPont Analysis in their studies. So in this study, I have used Z-Score Formula to eliminate this gap.3. Data and Methodology3.1Research SampleFor conducting the study, I have selected 12 manufacturing companies from different sub-sectors currently working in Pakistan. The list of companies is attached in appendix.3.2DataData have been collected from the annual financial reports of the companies available on the internet for the 3 years, starting from 2009 to 2011. The data relate to the 12 companies of manufacturing sector of Pakistan. The detail of companies along with their data is attached in appendix.3.3MethodologyTo evaluate the companys performance many efforts have been made from time to time. Earlier evaluations have been done by using total factor productivity, composite input price index, data envelopment approach or by using financial accounting ratios, but Altman Z-Score Formula never been used in this context. So in this paper, an attempt has been made to evaluate 4

the financial performance of 12 companies from manufacturing sector of Pakistan by using Altman Z-Score Formula. 3.3.1Z-Score FormulaZ-Score Formula is a measurement tool to access a companys financial performance/health. It offers a consistent measurement methodology across all business segments and an enhanced level of transparency by use of fully disclosed and open calculation model. Z-Score is a combination of 5 different parameters weighted by co-efficient. Z-Score Formula was published by Edward Altman in 1968 to determine the financial soundness or bankruptcy. The Z-Score is a multivariate formula for measuring the financial health of a company and a diagnostic tool that forecasts the probability of a company entering bankruptcy within a 2 year period. By the passage of time some changes have been made to Z-Score Formula and now Z-Score Formula used for manufacturing companies and private firm is as follow:

Where;X1 =X2 = X3 = X4 = X5 = Z = Overall Index

3.3.1.1X1, Working Capital/Total AssetsThis ratio is frequently found in the studies of corporate problems. It is a measure of net liquid assets of the firm relative to the total capitalization. Working capital is defined as the difference between current assets and current liabilities. Ordinarily, a firm experiencing consistent operating losses will have shrinking current assets in relation to total assets. 3.3.1.2X2, Retained Earnings/Total AssetsRetained earnings, is the account which reports total amount of reinvested earnings and/or losses of a firm over its full life. The age of a firm is implicitly considered in this ratio. For example, a relatively young firm will show a low RE/TA ratio because it has not had time to build up its5

cumulative profits. The RE/TA ratio measures the leverage of the firm. Those firms with high retained earnings relative to total assets have financed their assets through retention of profits and have not utilized as much debt.3.3.1.3X3, Earnings before Interest & Taxes/Total AssetsThis ratio is the measure of true productivity of firms assets, independent of any tax or leverage factor. Since a firms ultimate existence is based on the earning power of its assets, this ratio appears to be particularly appropriate for studies dealing with corporate failures. 3.3.1.4X4, Book Value of Equity/Total LiabilitiesEquity is measured by combined value of share capital and any kind of reserves, while liabilities include both short and long-term liabilities. This ratio tells that what value equity has against each unit of liability.3.3.1.5X5, Sales/Total AssetsIt is a standard financial ratio which tells the sales generating ability of the firms assets. It is one measure of managements capacity in dealing with competitive conditions.

3.3.1.6Decision Parameter Safe Zone - Grey Zone Distress Zone

Safe Zone tells that the financial health or solvency position of the company is good and company needs not to have any fear regarding bankruptcy. Grey Zone tells that financial health of the company is average, it is not the alarming situation for the company. However, the company needs improve its position. And Distress Zone tells that companys financial position is very poor and company needs to take some serious steps to sustain its position otherwise there is high chances of bankruptcy.

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4. Calculation on DataAll the calculations have been performed by using the Microsoft Excel 2007, to find out the Z-Score value for each company.Z-Score For The Year 20092009X1X2X3X4X5ZZone

Abbot0.330.460.241.881.703.86Safe

Ecopack-0.16-0.030.050.270.951.08Distress

Fauji Fertilizer-0.080.160.360.510.942.35Grey

GSK0.420.620.122.761.163.52Safe

ICI0.250.560.151.991.333.28Safe

National Foods0.070.170.160.521.972.88Grey

PEL0.070.110.080.600.621.26Grey

Pakistan Tobacco-0.050.140.380.531.773.25Safe

Pak Suzuki Motors0.520.760.024.301.484.36Safe

DG Khan Cement-0.060.420.080.960.421.38Grey

Nestle-0.070.210.250.312.213.24Safe

JDW Sugar Mills-0.170.140.160.220.731.31Gray

2010X1X2X3X4X5ZZone

Abbot0.360.510.302.081.904.39Safe

Ecopack-0.23-0.030.070.291.041.19Distress

Fauji Fertilizer-0.060.200.400.561.042.64Grey

GSK0.410.590.132.641.273.57Safe

ICI0.290.600.182.351.593.85Safe

National Foods0.010.120.100.381.682.26Grey

PEL0.100.110.070.270.691.18Distress

Pakistan Tobacco-0.090.080.120.411.692.23Grey

Pak Suzuki Motors0.500.710.043.052.224.58Safe

DG Khan Cement0.060.490.051.290.341.49Grey

Nestle-0.060.220.270.322.243.35Safe

JDW Sugar Mills-0.080.240.260.391.672.78Grey

Z-Score For The Year 2010

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Z-Score For The Year 20112011X1X2X3X4X5ZZone

Abbot0.390.570.322.341.754.48Safe

Ecopack-0.31-0.060.020.101.050.88Distress

Fauji Fertilizer0.030.260.610.710.993.42Safe

GSK0.370.560.152.571.413.69Safe

ICI0.290.550.132.171.713.70Safe

National Foods0.140.180.170.481.932.91Safe

PEL-0.010.08-0.020.220.470.56Distress

Pakistan Tobacco-0.130.060.050.331.721.97Grey

Pak Suzuki Motors0.450.620.061.912.264.09Safe

DG Khan Cement0.110.520.051.550.371.7Grey

Nestle-0.100.200.210.281.842.71Grey

JDW Sugar Mills-0.090.220.180.331.282.09Grey

5. AnalysisTable shows the results of 12 companies financial health through their Z-Scores for 3 years (2009-2011). It is clear from table that Abbot, GSK, ICI and Pak Suzuki Motors have strong solvency position throughout the analyzed period as they have managed their financials in appropriate way to keep themselves in Safe Zone through consistent performance. Fauji Fertilizer has shown improvement in its performance as its Z-Score improved from 2009 to 2011 which led FFC from Grey Zone to Safe Zone in 2011. The same is the case with National Foods but Z-Score of National Foods decreased abruptly from 2.88 to 2.26 in 2010 and then increased to 2.91 in 2011, managing good solvency position in 2011. Nestle is enjoyed 2 years having strong solvency position for 2009 and 2010 but in 2011 its Z-Score deteriorated from 3.35 to 2.71 falling in Grey Zone, which shows the downfall in the performance of Nestle. There is decline in the performance of Pakistan Tobacco, as its solvency position has gone down Safe to Gray Zone (good to average). In 2009, solvency position was strong but in the following 2 years its Z-Score decreased continuously from 3.25 to 2.23 and then further decreased to 1.97 falling in Grey Zone. DG Khan Cement and JDW Sugar Mills are performing consistently on average level and managing Grey Zone for solvency position but the only difference in both the companies is that the performance of JDW Sugar Mills is somehow more uncertain as compared to DG Khan Cement because its Z-Score showing more fluctuations which is not a good sign. PEL and Ecopack are the poorest companies having very poor solvency position and having Distress Zone.

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Z-Score Throughout The Analyzed PeriodCompanies200920102011

ZZoneZZoneZZone

Abbot3.86Safe4.39Safe4.48Safe

Ecopack1.08Distress1.19Distress0.88Distress

Fauji Fertilizer2.35Grey2.64Grey3.42Safe

GSK3.52Safe3.57Safe3.69Safe

ICI3.28Safe3.85Safe3.70Safe

National Foods2.88Grey2.26Grey2.91Safe

PEL1.26Grey1.18Distress0.56Distress

Pakistan Tobacco3.25Safe2.23Grey1.97Grey

Pak Suzuki Motors4.36Safe4.58Safe4.09Safe

DG Khan Cement1.38Grey1.49Grey1.7Grey

Nestle3.24Safe3.35Safe2.71Grey

JDW Sugar Mills1.31Gray2.78Grey2.09Grey

6. Conclusion & RecommendationsThe aim of this study was to examine the financial health of the 12 manufacturing companies currently working in Pakistan with the use of Z-Score formula. I did all the calculations and from that calculations concluded that 4 out of 12 companies (Abbot, GSK, ICI & Pak Suzuki Motors) are performing excellently as their solvency position is strong as compared to others. The poorest performance is shown by 2 companies, which are Ecopack and PEL. The remaining 6 companies performance is mixed as their solvency position shuffling from either good to average or average to good.There are some recommendations which are as follow: The companies having negative working capital are need to manage their funds in such a way that they can improve working capital, because working capital is the base for running the daily operations of the company and if they dont manage working capital, they may have to face problems in running day to day operations. The companies which has negative retained earnings and negative EBIT showing that it has accumulated loss and more operating expenses. So company needs to manage it operations in better way to generate higher profits to overcome its losses. Finally, the companies having X4, Book Value of Equity/Total Liabilities ratios answer less than 1, need to increase their owners equity or need to pay off their liabilities to maintain a balance between internal equity (Shareholders equity) and external equity (Creditors claims). Because to have more burden of liabilities on the business have negative impact on investors. 9

Reference

http://www.dailytimes.com.pk/default.asp?page=2012%5C03%5C24%5Cstory_24-3-2012_pg5_3http://economicpakistan.wordpress.com/2008/01/27/large-scale-manufacturing/Bayyurt, and Sagbansua (2007), Determining The Efficiency Of Concrete Companies Ranked In Top 1000 Manufacturing Firms Trading In ISE: A Multi-Criteria Data Envelopment Analysis ModelMohamad, and Said (2010), Measuring the Performance of 100 largest listed companies in Malaysia, Vol. 4(13)Memon, and Tahir (2012), Performance Analysis of manufacturing Companies in Pakistan, Vol. 1Memon, and Tahir (2012), Company Operation Performance Using DEA and Performance Matrix: Evidence from Pakistan, Vol. 2Heine (2000), Predicting Financial Distress of Companies: Revisiting the Z-Score and Zeta Models

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Appendix

Research Sample1. Abbot Pakistan2. EcoPack Ltd3. Fauji Fertilizer Company Limited4. GlaxoSmithKline Pakistan Limited5. ICI Pakistan Limited6. National Food Limited7. Pak Elektron Limited8. Pakistan Tobacco Company9. Pak Suzuki Motor Company Limited10. D.G.Khan Cement Company Limited11. Nestle Pakistan Limited12. JDW Sugar Mills Limited

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Data2009Working CapitalTotal AssetsRetained EarningsEBITBook Value of equityTotal liabilitiesSales

Abbot1,652,6964,964,5762,259,4571,168,6223,238,4601,726,1168,450,118

Ecopack(295,351)1,850,827(49,646)89,555393,4531,457,3741,764,852

Fauji Fertilizer(2,937,118)38,551,5826,297,17114,002,16413,082,44225,469,14036,163,174

GSK6,056,07414,430,9088,886,7681,751,22510,593,4863,837,42216,753,873

ICI5,325,27021,422,65712,094,7733,240,04713,961,5697,008,01528,429,897

National Foods141,0301,911,776323,844307,543655,3861,256,3903,758,706

PEL1,660,60122,935,2202,593,1861,782,4738,560,67514,374,54514,182,041

Pakistan Tobacco(614,252)12,226,8611,705,2964,589,4654,260,2347,966,62721,666,525

Pak Suzuki Motors9,102,49917,655,73413,502,601440,40714,325,6003,330,13426,234,061

DG Khan Cement(2,547,207)42,723,04117,875,9483,383,25820,918,44221,804,59918,038,209

Nestle(1,237,602)18,586,9803,973,4594,628,3074,426,95514,160,02541,155,822

JDW Sugar Mills(1,771,234,432)10,410,661,9001,414,607,0091,630,303,8861,857,447,5798,553,214,3217,572,724,395

2010Working CapitalTotal AssetsRetained EarningsEBITBook Value of equityTotal liabilitiesSales

Abbot2,093,9735,790,4212,933,5361,744,7873,912,5391,877,88210,995,701

Ecopack(387,634)1,676,735(49,315)112,717374,1271,302,6081,742,074

Fauji Fertilizer(2,763,467)43,060,8568,662,27617,396,59015,447,54727,613,30944,874,359

GSK6,100,30714,891,7998,835,6961,951,75010,799,8144,091,98518,916,191

ICI6,396,22022,030,67213,160,0703,895,39615,455,4456,575,22735,129,980

National Foods22,5652,674,360327,518258,545741,9451,932,4154,489,946

PEL2,542,75025,530,1392,897,7161,884,8194,565,98016,801,53317,522,656

Pakistan Tobacco(1,107,812)12,378,4001,047,1491,530,7563,602,0878,776,31320,952,629

Pak Suzuki Motors9,560,68319,250,36413,674,916689,36414,497,9154,752,44942,642,762

DG Khan Cement2,631,30347,046,04322,868,2272,261,16326,519,22020,526,82316,275,354

Nestle(1,453,649)22,952,2325,128,3776,209,2615,581,87317,370,35951,487,302

JDW Sugar Mills(1,043,329,992)12,179,586,0492,927,602,5403,125,896,4583,417,492,3108,762,093,73920,380,683,879

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2011Working CapitalTotal AssetsRetained EarningsEBITBook Value of equityTotal liabilitiesSales

Abbot2,921,8367,405,3294,207,1802,378,0425,186,1832,219,14612,946,968

Ecopack(534,394)1,703,092(101,661)35,316128,1091,333,9951,784,754

Fauji Fertilizer1,712,07755,530,91614,588,63633,951,87823,070,22432,460,69255,221,168

GSK5,735,87215,437,5858,715,8812,273,41411,108,5724,329,01321,750,147

ICI6,732,97223,465,31412,967,3223,049,39516,068,6407,396,67440,114,908

National Foods388,7692,854,741508,384487,447922,8111,931,9305,520,780

PEL(402,991)23,682,6121,829,783(504,959)3,498,04716,222,52511,237,238

Pakistan Tobacco(1,705,211)13,318,487778,997660,6543,333,9359,984,55222,949,974

Pak Suzuki Motors10,576,90923,301,11714,470,0331,383,14215,293,0328,008,08552,718,563

DG Khan Cement5,637,83649,673,05025,836,0932,652,87030,217,28519,455,76518,577,198

Nestle(3,393,438)35,179,8597,158,9207,553,2197,612,41627,567,44364,824,364

JDW Sugar Mills(1,815,515,400)19,244,044,908

4,224,139,3423,512,801,6514,816,905,95214,427,138,956

24,729,491,207

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