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A Sectoral Analysis of Dividend Payment Behavior-Evidence from Karachi Stock Exchange

Abstract

This study analyzes the dividend payment behavior of Karachi Stock Exchange (KSE) covering period from 2006 to 2011. Secondary data of 475 companies has been collected from KSE web site. For the purpose of study first, descriptive research approach applied, to see the trend of the 3 years 2009 to 2011 with respect to sectors. Analysis shows that increasing trend in payment behavior of dividend. In 2009, 43% companies paid dividend, whereas in 2010, this percentage increased to 58%. In 2011 this trend was same as compare to 2010. In the second part, Levin-Lin-Chu unit root test for panel data and POLS test used on 15 sectors. Variables used dividend payout as dependent variable and earning per share, growth in sales, return on equity, free cash flow, and leverage as independent variables. Results showed earning per share has positive significant impact on dividend payout in beverages, Travel and leisure fixed line telecommunication, food processor, personal goods sectors. Similarly, growth in sales has positive impact on dividend payout in Oil and gas, construction and material, engineering, and household goods. Likewise, return on equity has positive significant relationship with dividend payout in chemical, forestry, and tobacco sectors. Furthermore, leverage also has positive relationship with dividend payout in forestry, tobacco, travel and leisure, fixed line telecommunication, and food processor sectors. Finally, free cash flow also has positive significant impact on dividend payment in fixed line telecommunication. It can be recommended that firms should focus on its internal policies as all factors which were part of the study, are related to the internal financial strength of the firm.

Keywords: Sectoral , dividend, payment behavior, panel data, KSE

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Introduction

Dividend payment decision is fundamentally a financial management decisions, widely discussed in financial literature (Brealey, Mayers, and Allen (2005). There are two alternatives available in dealing with the profits of a company. Either it can be distributed to the shareholders in the form of divided or retained in the business itself. The decision as to which course should be followed depends on divided policy decisions. Should a company fix its dividend first, and retained any profits left over as residual? Ferris et al. (2009) has studied dividend payment behavior of 25 common and civil law countries firms, and identified the decreasing tend within 11 years of time period from 72% to 55% . Mostly this has occurred due to inclusion of non-dividend paying firms. Dividend paying patterns decreasing significantly from more than two decades. This occurs due to change in the composition of different firms in USA and also unable to generate the enough profit for distribution. These firms usually want to retain the earnings for taking the opportunity for growth Fama and French (2001). Pandey (2003) also investigates the corporate dividend policy and behavior in Kuala Lumpur Stock Exchange, where it is followed that unstable dividend policy as the change in earnings, but non-dividend paying behavior is not followed. Firms adjust their policies as per the amount of earnings (Al-Ajmi and Hussain (2011). Also there is a positive relationship between the dividends and inflation, and mostly companies increase their dividend payments in the inflationary environment (Basseand and Reddemann, (2011).In Pakistan Nazir et al. (2010) investigates the role of corporate dividend policy in determining the stock price volatility. Afza and Mirza (2010) point out the ownership structure and cash flows as determinants of corporate dividend policy in Pakistan.

This particular study, which focuses on firms listed on the Pakistani Stock Market i.e. Karachi Stock Exchange (henceforth, KSE) into 32 sectors, contributes to the ongoing discussion of factors that influence dividend payments. Traditionally, financial managers have believed that the pattern of dividend payments is important, i.e. dividend policy is a relevant element in determining the market value of the company. Among the most severe critics of the traditional view have been Modigliani and Miller (1961). Basse and Reddemann (2011), John and Muthusamy (2010) Ramachandran and Packkirisamy (2010), Baker et al (2007), focus on dividend payments by keeping in mind that, earning is the main factor that set the tone of dividend. In the context of Pakistani stock market or rather KSE is the largest capital market of Pakistan, which has been analyzed by many scholars Afza and Mirza (2010), Ahmed and Javid (2009) for the payment of dividend.

There are hundreds of companies listed in KSE, classified in 33 sectors. Nothing has been studied altogether about the behavior of dividend payment. Sector wise analysis provide investors an idea about particular group of companies and how well it has been performing in a given period of time. Investors, who are considering rotation strategy from any particular sector for increasing their returns, need to know such kind of study for analysis the behavior within or outside a sector. They should consider which individual stock ultimately needs to be purchased. Therefore, there should be a detail analysis of future earnings, stability of earnings and pattern of past dividend (sector wise), which will address guiding principles to the investors in future.

Dividend policy of emerging market and developed markets are considered to have different nature, characteristics and efficiency. Researcher has the similar view about the developing markets that they are also quite different in nature, characteristics and efficiency. The complex unsolved puzzle of dividends is needed to be solved and this research focuses on adding up another block which might become the capstone for all previous researches. Some factors are

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chosen by the researcher, which are considered as determinants of dividend policy in several past researches. This study aims to provide the sectoral analysis of dividend payment, which helps the investors to see the trend among the sectors.

The paper continues as follows: Section 2, consists of existing empirical literature; Section 3, discusses the data, its methodology and data sources; Section 4, presents the results and analysis and in Section 5, lies conclusion and recommendations.

2. Review of Empirical literature.

Musiega et al. (2013) conducted studied on determinants of dividend policy on Nairobi stock exchange for the period of 2007 to 2011.Dividend payout is dependent variable while profitability, growth, earning and liquidity used as independent variable. Size and risk used as moderating variables. Results shows return on equity, size, growth has positive impact with dividend payout.

Nnadi. M; Wogboroma. N and Kabel .B (2013) investigated determinants of dividend policy on listed firms in African stock exchange. 29 stock exchange were considered .Unit root test, Tobit regression is used for analyzing the result. Government, age,market capitalization and earning per share has positive influence on African listed firms.

Al-Ajmi and Hussain (2011) investigate the corporate dividend decisions: evidence from Saudi Arabia by using the sample of 54 Saudi listed firms during 1990-2006. The results showed that most of the Saudi firms pay small amount of dividends of their earnings or of the cash flow. It is also reported that fixed dividend policy are not used by the firm and they change or adjust their policies as per the amount of earnings. The author determined that four factors can influence to dividend policy decision in Saudi Arabia; i.e. more profitability, large firms, firms that paid dividends in previous period, firms with having less investment opportunity.

Basse and Reddemann (2011) investigate the inflation and the dividend policy of US firms by using the quarterly data from 1980 to 2008. Variables are dividends corporate earnings, real growth and inflation. Cointegration technique has been used. The results showed that, there is a stable long run relationship between the all the variables. There is also a positive relationship between the dividends and inflation, and mostly companies increase their dividend payments in the inflationary environment.

Abor and Bokpin (2010) investigated the effect of corporate finance and investments on dividend policy in 34 countries covering period from 1990 to 2006. Fixed effect panel model was used in the analysis. Variables were used profitability, leverage, debt, external financing and stock market capitalization representing corporate finance in the research. The result showed that Stock market capitalization and profitability were more vital determinants of dividend policy. Results also recommended that there is a significant relation between investment opportunities and dividend policy. Insignificant impact of leverage, debt, external financing is found on dividend policy. They concluded that firms which belong to developed markets are more tending towards paying least possible dividends to their shareholders.

Fairchild (2010) in his study compared signally hypothesis and free cash flow hypothesis in order to examine dividend policy of firms of United Kingdom. Variables used in this study and considered in the research were firm value, managerial incentives and dividend policy. The result

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showed that there is an association of agency problems initially could be biasness for personal benefits and giving priority to current income rather being speculative. It is also indicated that this can cause false reputation of managers in front of investors which can be horrible for future. He recommended that his work can further be continued as this issue is of complex nature and so far unsolved.

Nazir et al. (2010) investigate the role of corporate dividend policy in determining the stock price volatility in Pakistan by using sample of 73 firms from Karachi stock Exchange from 2003 to 2008. The methodologies used are fixed effect and random effect regression with the test involves PV dependent variable and dividend yield. Variable independent It is suggested that the dividend policy have effect of stock price volatility in Pakistan.

John and Muthusamy (2010) investigate the relationship between leverage, growth and profitability on payout ratio in Indian paper industry, by using the data of top ten paper firms listed on Bombay Stock Exchange based on sales. OLS regression has been used, and secondary data collected from Center of Monitoring Indian Economy. Growth in sales, earning per share, price earning ratio, market value to book value, cash flow, leverage, liquidity and return on assets are used as independent variables, and dividend payout as dependent variable. The result showed that there are negative relationship between leverage and payout.

Ramachandran and Packkirisamy (2010) investigate the impact of firm size on dividend behavior: a study with reference to corporate firms across industrial in India by using the panel data of 73 firms from 1996 to 2007.Sectors was selected (cement, chemical and fertilizer, information technology, oil and gas, pharmaceutical, shipping and textile. The variables used were capital structure, dividend payout and dividend per share. The author used the multiple regression method (OLS).The result showed that, there is significant effect on selected independent variables. It is also reported by the author dividend per share of different sizes of firms in India is dependent to the level of debt and capital structure.

Afza and Mirza (2010) investigate the ownership structure and cash flows as determinants of corporate dividend policy in Pakistan, by using the data 100 listed companies at Karachi Stock Exchange from 2005 to 2007. Ordinary least Square regression has been used. The author used the dividend payout and dividend intensity as the dependent variables, whereas size, management, leverage and profitability are independent variables. The result showed that the negative relationship of the managerial ownership, cash flow sensitivity, firm size, and leverage, whereas there is a positive relationship between operating cash flow, and profitability with the cash dividend.

Al-Kuwari (2010) investigates the factors influencing the corporate dividend payout decision on firms listed on Gulf Cooperation Council (GCC) states by using the panel data of 191 non-financial firms from 1999 to 2003. Radom effects probit model has been used. Variables are used dividend ratio as dependent, and government ownership, free cash flow, market capitalization, growth rate, firm leverage, business risk, and firm profitability as independent variables. Result shows that government ownership, company profitability, and company size could be reasons of paying dividends. The author also concludes that payment of dividend may cause to reduce agency conflict, save the minority interest, and overall increase the good reputation in the market.

Fargher and Weigand (2009) investigated the cross-sectional differences between profits, risk of volatile market to book value ratio and returns before and after the initiating of regular cash dividend payments for the period starting from 1964 to 2000. Ordinary least square regression is

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used in the research and variables in the study were return on equity, return on asset, market to book value, market capitalization, debt to asset and profit margin. The result suggested that initiating dividends are going into a more mature life cycle phase.

Ferris et al. (2009) investigated an international analysis of dividend payment behavior by using the sample of 25 countries (9common law and 16 civil law) data from 1994 to 2007. The methodology used a logistic regression model. The result of both common and civil law regimes showed decreasing trend, it is also examined that there is an increasing trend in percentage of non-dividend paying firms. It is suggested by the author that a decline in the payment of dividend can effect on shareholders primacy and initial faith on such type of governance

Ahmed and Javid (2009) investigate the determinants of dividend policy in Pakistan by using the sample of 320 non-financial firms listed in Karachi Stock Exchange from 2001 to 2006.Lintner model has been used. The results showed that most of the listed non-financial firms set their dividend policy on the basis of current earning per share and dividend per share. It is also showed that investment opportunity and leverage have inverse relationship on firms’ dividend policy.

Al-Malkawi (2008) examines the factors influencing corporate dividend policy decision of publicly quoted companies in Jordan. Using the data of 1137 firms-year observation of 15years panel data from 1989 to 2003. Probit model has been used and results showed the factors that affect to the dividend policy decisions are size, profitability and age of the firm. The author also finds that financial leverage has the negative impact to this phenomenon.

3. Data and Methodology

The purpose of the study is to analyze the behavior of dividend payments in KSE listed companies. Therefore secondary data has been gathered from KSE Web site and State Bank library/ Web site. The data consists of 475 companies. All are listed companies from year 2006-2011. The penal ordinary least square is used for analyzing the result.In this research dividend payout (DP) is the depended variable and growth in sale (GS), earning per sale (EPS), leverage (LEV), free cash flow (FCF), and return of equity (ROE) are independent variables.

DP= α + β1 GS + β2 EPS + β3 LEV + β4 FCF + β5 ROE + έ Hypothesis of the study are as follows:

H 1 = Growth in sales has a significant impact on the dividend payout on the companies listed in KSE.

H2 = Earning per share has a significant impact on the dividend payout on the companies listed in KSE

H3 = Leverage has a significant impact on the dividend payout on the companies listed in KSE

H4 = Free cash flow has a significant impact on the dividend payout on the companies listed in

KSE

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H5 = Return on equity has a significant impact on the dividend payout on the companies listed in KSE.

3.1 Variables Description

Dividend payout (DP), ratio measures the relationship between the earnings belonging to the ordinary shareholders and the dividend paid to them. It is calculated as dividend paid to shareholders divided by net profit after tax available to equity holders. Growth in Sales (GS), it is the amount of increased sales as compare to last year. Earnings per share (EPS), it measures the profit available to the equity shareholders on a per share basis, or the amount that they can get on every share held. It is calculated by dividing the profits available to the equity shareholders by the number of the outstanding shares.Leverage (LEV), it is the employment of an asset or source of finance for which firm pays fixed cost or fixed return. Free cash flow (FCF) represents the cash that a company is able to generate after laying out the money required to maintain or expand its asset base. Return on Equity (ROE) calculated as Net Profit/Shares of Equity.

4. Data Analysis

Data analysis includes descriptive statistics, stationarity test and POLS.

4.1 Descriptive Analysis

Data analysis section divided in two parts, in first part descriptive analysis provided of all sectors in KSE for analysis of sector wise behavior for the period of 2009-2011. Above all, in later part, complete explanatory research by using the POLS applied to sectors to check the significance of the variables.

For sectoral analysis, initially all 33 sectors of KSE has been selected, but 1 sector data was not available therefore, it was excluded from our list. The data of 475 companies were obtained from KSE web site and the time period covered from 2009 to 2011. Our selected sectors are oil and gas, chemicals, forestry and paper, industrial metal and mining, construction and materials, general industrial, electronics and electrical equipment, industrial engineering, industrial transportation, support services, automobile and parts, beverages, food producers, households goods leisure goods, personal goods, tobacco, health care equipment and services, pharma and bio-tech, media, travel and leisure, fixed line telecommunication, electricity, gas water and multiutilities, banks, non life insurance, life insurance, real estate investment, and services, financial services, equity investment instruments, software and computer services, and technology hardware and equipment. This study is based on secondary data. For this purpose 3 years data (2009-2011) from KSE daily quotation has been obtained. In 2009 and 2010, the numbers of companies were 475. The table 4.1.1 & table 4.1.2 show sectoral analysis for all 32 sectors in term of dividend paid or not paid. In 2009, numbers of dividend payers were 202, which is 43% of the total 475 companies. Similarly, in 2010 numbers of dividend payers were increased to 273, which is 58% of the total 475 companies. This increased was occurred mainly in personal goods sector, which in 2009 were 44 companies and whereas, in 2010 it was increased to 80 companies out of total 122 companies. In non-life insurance sector was also increased from 11 to 17 companies in 2009 to 2010 respectively. In equity investment

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instruments the same tendency was seen and it was increased from 11 to 31 companies. In third column, researcher analyzes the percentage changes of dividend payers in 2010. Increasing trend is observed in 9 sectors. 33% industrial metals and mining, 20% in general industries, 82% in personal goods, 12% in food producers, 33% in fixed line telecommunication, 18% in banks, 55% in nonlife insurance, 75% in financial services, and 182% in equity investment instruments.There is also decreasing trend in 5 sectors, 20% in construction and materials, 29% in industrial engineering, 100% in support services, 10% in automobiles and parts, and 20% in household goods. In last column, we described the total percentage of dividend payers on the basis of total number of companies in each sector, and then we take the changes in 2009 and 2010.

In 2010 and 2011, there was increase in numbers of companies 475 to 484, which was mainly in personal goods sector. The table (appendix attached) shows that in 2010, number of dividend payers were 273, which is 58% of the total 475 companies .In 2011 number of dividend payers were remain almost same 272, which is 56% of the total 484 companies. There were multiple factors of this result. Oil and gas sector increased from 9 to 12 in the period of 2010 and 2011. Construction and materials increased from 4 to 10, personal goods decreased by 80 to 70 in the period of 2010 to 2011.In third column, the percentage changes of dividend payers in 2011. There is not much difference in overall percentage change. Increasing trend is observed in 8 sectors. 33% oil and gas, 6% in chemicals, 150% in construction and material, 29% in Pharma and bio tech, 100% in media, 17% in electricity, 23% in banks, and 12% in non life insurance. There is also decreasing trend in 10 sectors, 20% in industrial engineering, 7% in food producers, 13% in personal goods, 50% in tobacco, 25% in fixed line telecommunication, 33% life insurance, 14% financial services 3% in equity investment instrument, 100% in software and computers services, and 25% in household goods. In last column, we described the total percentage of dividend payers on the basis of total number of companies in each sector, and then we take the changes in 2010 and 2011.

In second part checking of regression analysis has been made between the dependent (dividend payout) and independent variables (free cash flow, earning per share, growth in sales, leverage, and return on equity).

4.2 Unit root Test for 15 sectors

Panel unit root test procedures for the analysis context have been used for the checking stationary of the data. The procedure emphasis in development the combined information from the time–series dimension with that obtained from the cross– sectional dimension, in the hope that inference about the existence of unit roots.In analysis of all sectors. Results of Table 4.2.1 clearly show that all the variables are stationary at first difference which implies, there is a long term relationship in the series of variables.

After applying stationery root test, now data can be analyzed by applying pooled ordinary lest square (POLS) test to check the significance of the model, what we developed in methodology section. Sectors results are shown below:

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4.3 Estimation for POLS

Results are shown by Table 4.3.1. Variables are used to find the relationship among all the sectors. EPS, ROE, GS, LEV and FCF are independent variables and DPR is dependent variable.

Earning per share

The results of table 4.3.1 show the EPS has positive significant impact on DPR in beverages, travel and leisure, fixed line telecommunication, food processors, household goods, personal goods, automobiles, and electricity. This happens when the earnings are increasing and also companies want to distribute these earnings to shareholders. Whereas forestry (paper and based) has negative significant relationship with the dividend payout ratio. Earnings per share are generally considered to be the single most important variable in determining a share's price. This happens due to retention of earnings, companies may cease to pay dividend. When companies in the growing face, then the procedure of residual dividend policy is very common phenomena, in which first companies cover their capital expenditure then remaining amount would be paid as dividend.

Return on equity

The results of table 4.3.1 show the chemical, forestry, and tobacco have significant positive relationship between return on equity and dividend payout ratio (Afza and Mirza (2010). Return on equity is the measure of profit, which is quite reasonable that, increase in the return will cause the result to increase in dividend payment. Return on equity has negative significant relationship with the dividend payout ratio in oil and gas, construction and materials, engineering, beverages, travel and leisure, fixed line telecommunication, household goods, personal goods, pharma and bio-tech, and electricity. Return on equity is the measure of profit and growth. Benefits of profit come from re-investment in the company, which will give the high growth rate in future.

Growth Sales

The results of table 4.3.1 show the oil and gas, construction and materials, engineering, household goods have significant positive relationship between growth in sales and dividend payout ratio, which is quite reasonable. Increase in the sales as compare to previous year causes the result to increase in profitability and consequence would ultimately increase dividend payment but chemical, forestry and food producer have negative significant relationship with the dividend payout ratio. This result shows that company has not been distributing their earnings even if sales are growing. A company’s market share has a big force on its future growth in sales. Sometimes companies want to increase market share for this purpose retaining of earnings is very important.

Leverage

The results of table 4.3.1show leverage have positive significant relationship with DPR in forestry, tobacco, travel and leisure, fixed line telecommunication, food processor, and personal goods. This result is also apparent that companies obtain debt to purchase fixed assets or sometimes total business to increase or diversification in the market. This leads to increase stock prices, and in such situation companies has to pay dividend but oil and gas, household goods and pharma and biotech has negative significant relationship (John and Muthusamy (2010), Afza and Mirza (2010), Ahmed and Javed (2009), Al Kuwari (2009) with the dividend payout ratio; this result is also quite possible that as the level of debt or total debt increase in the firm’s capital structure, it may cause the deceasing trend in the dividend payment behavior. This phenomenon

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occurs due to increase in amount of interest payment and most of the earnings of the company are engaged in the payment of interest or payment of cost of debt.

Free Cash Flow

The results of table 4.3.1 show fixed line telecommunication has positive significant relationship with the dividend payout ratio. Free cash flow shows that whenever company has cash after paying of all expenditure. The presence of free cash flow is a healthy sign which indicates that a company has cash for payment of dividends. If company has large amount of earnings, but does not have the cash, therefore it will not be able to pay dividends to shareholders such as chemical, forestry, construction and material, engineering, beverages, tobacco, travel and leisure, food processor, household goods, pharma and biotechand automobiles have negative significant relationship with the dividend payout ratio Free cash flow shows that whenever company has cash after paying of all expenditure. Negative relationship of free cash flow shows that companies put most of their cash into investment opportunities. This happens when company is in developing face, and at that time they need to purchase inventory, additional working capital for expending credit to customers.

5. Conclusion and Recommendations

This study investigates the factors influence the dividend payment behavior by using annual panel data from the period of 2006 to 2011 of 475 companies of sectors listed on KSE. First descriptive analysis used to provide the sector wise behavior of dividend payment and non-payment. In the second part of analysis, Augmented Dickey Fuller (ADF) conventional panel unit root test and panel ordinary least square techniques have been used. Variable used dividend payout as dependent and earning per share, free cash flow, growth in sales, leverage and return on equity as independent variables.

Results show that earning per share has positive impact on dividend payment in 8 sectors (beverages, travel and leisure, fixed line telecommunication, food processors, household goods, personal goods, automobiles and electricity), Whereas forestry (paper and board) has negative significant relationship with the dividend payout ratio. Besides, free cash flow has positive impact on dividend payment in only 1 sector (fixed line telecommunication) while chemical, forestry, construction and material, engineering, beverages, tobacco, travel and leisure, food processor, household goods, and pharma and biotech, and automobiles have negative impact on dividend payment.

Similarly, growth in sales has positive impact on divided payment in 4 sectors (oil and gas, construction and material, engineering, household goods, on the other hand, chemical, forestry, and food processor have contra impact on distribution of profits. In addition, leverage has positive impact on dividend payment in 6 sectors (forestry, tobacco, travel and leisure, fixed line telecommunication, food processor, and personal goods), on the other hand, oil and gas, household goods, and pharma and biotech have negative impact on dividend payout ratio. Moreover, the last variable, return on equity also has positive impact on dividend payout in 3 sectors (chemical, forestry and tobacco) on the other hand; oil and gas, construction and material, engineering, beverages, travel and leisure, fixed line telecommunication, household goods, personal goods, pharma and biotech, and electricity have negative impact on dividend payout ratio.

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It is observed that dividend payment behavior has important connections and transformations among the sectors. Mostly, once a company start paying of dividend look forward to continue this practice. Results document few characteristics related to the ability to pay dividends between firms based on the profitability, riskiness and life cycle. The differences that do occur are related to size and growth opportunities.

It can be recommended that firms should focus on its internal policies as all factors which were part of the study were related to the internal financial strength of the firm and most of them are found to be effective more or less. A higher level of cash flow is desired so its maintenance should also be done by taking all necessary measures. Net income which is the core reason of all business activities should also be given importance as they increase earnings per share of the firms. Effective internal management which can help in increasing sales growth should be hired this will make firms more lucrative for the investors which mostly take their decisions after considering dividend payouts of previous years.

Further research can be done by considering financial sectors which are also being considered by investors while taking their investment decisions. Dividend payouts are used as dependent variable and it is considered as one of the most believed factor by the investor but there are more factors which would have been considered by the investors those can also be used for future work, like corporate governance and internal management.

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14

Table 4.1.1 Sectoral analysis for all 32 sectors in term of dividend paid or not paid.

SECTORS NAME

TOTAL NUMBERS OF COMPANIES

NO. OF DIVIDEND PAYERS

COMPANIES % change2009 2010 2011 2009 2010 2011 2010 2011

Oil and Gas 13 13 13 9 9 12 0 33Chemicals 30 30 32 17 17 18 0 6Forestry and Paper 3 3 3 2 2 2 0 0Industrial metals and Mining 5 5 6 3 4 4 33 0Construction and Materials 31 31 32 5 4 10 -20 150General Industrials 12 12 9 5 6 6 20 0Electronic and Electrical Eq. 2 2 3 1 1 1 0 0Industrial Engineering 9 9 10 7 5 4 -29 -20Industrial Transportation 3 3 3 3 3 3 0 0Support Services 1 1 1 1 0 0 -100 0Automobile and Parts 15 15 14 10 9 9 -10 0Beverages 2 2 2 2 2 2 0 0Food Producers 45 45 46 26 29 27 12 -7Household Goods 9 9 9 5 4 3 -20 -25Leisure Goods 1 1 1 1 1 1 0 0Personal Goods 122 122 133 44 80 70 82 -13Tobacco 3 3 3 2 2 1 0 -50Health Care Equipment 1 1 1 1 1 1 0 0Pharma and Bio Tech 9 9 9 7 7 9 0 29Media 3 3 3 1 1 2 0 100Travel and Leisure 5 5 5 1 1 1 0 0Fixed Line Telecommunication 6 6 6 3 4 3 33 -25Electricity 13 13 15 6 6 7 0 17Gas Water and Multiutilities 2 2 2  - 2 2 0 0Banks 27 27 24 11 13 16 18 23Non Life Insurance 22 22 22 11 17 19 55 12Life Insurance 4 4 4 3 3 2 0 -33Real Estate Investment 2 2 2  - 1 1 0 0Financial Services 33 33 31 4 7 6 75 -14Equity Investment Instruments 40 40 38 11 31 30 182 -3Software and Computer Services 1 1 1 - 1 0 0 -100Technology Hardware and Equipment 1 1 1 0 0 0 0 0

TOTAL COMPANIES 475 475 484 202 273 272

15

Table 4.1.2 Sectoral analysis for all 32 sectors in percentages

SECTORS NAME % of PAYERS % OF NON PAYERS2009 2010 2011 2009 2010 2011

Oil and Gas 69 69 92 31 31 8Chemicals 57 57 56 43 43 44Forestry and Paper 67 67 67 33 33 33Industrial metals and Mining 60 80 67 40 20 33Construction and Materials 16 13 31 84 87 69General Industrials 42 50 67 58 50 33Electronic and Electrical 50 50 33 50 50 67Industrial Engineering 78 56 40 22 44 60Industrial Transportation 100 100 100 0 0 0Support Services 100 0 0 0 100 100Automobile and Parts 67 60 64 33 40 36Beverages 100 100 100 0 0 0Food Producers 58 64 59 42 36 41Household Goods 56 44 33 44 56 67Leisure Goods 100 100 100 0 0 0Personal Goods 36 66 53 64 34 47Tobacco 67 67 33 33 33 67Health Care Equipment 100 100 100 0 0 0Pharma and Bio Tech 78 78 100 22 22 0Media 33 33 67 67 67 33Travel and Leisure 20 20 20 80 80 80Fixed Line Telecommunication 50 67 50 50 33 50Electricity 46 46 47 54 54 53Gas Water and Multiutilities 0 100 100 100 0 0Banks 41 48 67 59 52 33Non Life Insurance 50 77 86 50 23 14Life Insurance 75 75 50 25 25 50Real Estate Investment 0 50 50 100 50 50Financial Services 12 21 19 88 79 81Equity Investment Instruments 28 78 79 73 23 21Software and Computer Service 0 100 0 100 0 100Technology Hardware equipment. 0 0 0 100 100 100

16

TABLE 4.2.1 :ADF TEST : STATIONARY TEST FOR DIFFERENT SECTORSBeverages Chemical Construction & Material Engineering Telecommunication

I(0) I(1) I(0) I(1) I(0) I(1) I(0) I(1) I(0) I(1)

C C&T C C&T C C&T C C&T C C&T C C&T C C&T C C&T C C&T C C&T

DPR -1.35 -1.06 -4.66* -5.11* -2.43 -2.63 -4.86* -4.90* -2.39 -2.39 -4.49* -4.43* -2.59 -2.87 -3.91* -3.88* -1.36 -1.98 -3.55** -3.45**

FCF -1.77 -1.03 -5.83* -6.37* -1.75 -1.88 -4.77* -4.85* -2.07 -2.53 -3.16** -5.40* -2.51 -2.83 -3.49** -3.48** -1.93 -1.80 -3.58** -3.92**

EPS -0.93 -0.94 -3.06** -5.38* -2.46 -2.81 -3.79** -4.65* -2.43 -2.89 -3.81* -3.69* -2.56 -2.83 -4.42* -4.36* -1.25 -1.72 -3.50** -3.42**

GS -2.66 -2.38 -5.33* -5.33* -2.30 -2.03 -5.10* -5.33* -2.07 -2.03 -3.25** -3.24* -2.19 -2.82 -5.60* -5.49* -2.15 -1.40 -5.19* -4.96*

LEV 0.66 -0.02 -3.97* -4.05* -2.57 -2.61 -4.16* -4.15* -2.55 -2.27 -4.00* -3.87* -2.16 -2.84 -2.77** -3.06** -0.70 -1.76 -3.22** -3.45**

ROE -2.03 -3.00 -3.15** -10.44* -2.41 -2.94 -4.57* -4.45* -2.38 -2.45 -4.31* -4.17* -2.18 -2.27 -4.10* -4.05* -2.23 -1.49 -5.07* -4.98*

Food Forestry Households Oil and Gas Personals

I(0) I(1) I(0) I(1) I(0) I(1) I(0) I(1) I(0) I(1)

C C&T C C&T C C&T C C&T C C&T C C&T C C&T C C&T C C&T C C&T

DPR -2.53 -3.19 -6.43* -6.83* -1.13 -1.91 -4.23* -4.09* -2.45 -2.50 -5.19* -5.11* -2.36 -2.28 -3.79** -3.61** -2.89 -2.90 -4.83* -4.76*

FCF -1.64 -1.52 -6.72* -6.67* -2.05 -2.47 -5.73* -4.63* -2.25 -2.23 -3.18** -4.85* -2.51 -2.51 -4.93* -4.89* -2.17 -2.14 -3.81* -3.76*

EPS -0.22 -1.84 -8.52* -9.28* -2.00 -2.60 -5.62* -13.06* -2.12 -2.83 -4.34* -4.19* -2.14 -1.95 -7.26* -7.17* -2.50 -2.70 -3.37** -4.43*

GS -2.13 -2.14 -5.26* -5.36* -1.77 -3.28 -3.38** -4.17* -1.90 -2.29 -3.51** -3.62** -2.26 -2.88 -6.87* -6.83* -2.48 -3.09 -4.76* -4.72*

LEV -2.23 -2.78 -3.40** -3.36* -1.84 -2.78 -5.29* -11.08* -2.19 -2.06 -4.34* -4.66* -2.16 -2.63 -7.13* -7.07* -2.43 -2.84 -3.85* -3.79**

ROE -2.51 -2.69 -3.62** -3.58* -2.01 -2.95 -3.18** -13.06* -1.99 -1.62 -4.35* -4.73* -1.25 -1.24 -4.55* -5.45* -2.49 -2.73 -6.95* -6.86*

Pharma Tobacco Travel Automobile Electricity

I(0) I(1) I(0) I(1) I(0) I(1) I(0) I(1) I(0) I(1)

C C&T C C&T C C&T C C&T C C&T C C&T C C&T C C&T C C&T C C&T

DPR -2.25 -2.57 -3.68** -3.74** -0.66 -2.65 -3.85* -3.80* -2.53 -3.26 -3.84* -3.72** -2.59 -2.66 -3.21** -3.21** -2.50 -2.83 -3.26** -3.64**

FCF -2.25 -2.81 -4.77* -4.68* -1.47 -2.16 -3.10** -4.40* -1.87 -1.97 -4.31* -5.64* -2.26 -2.23 -4.59* -4.55* -2.42 -2.32 -4.30* -4.25*

EPS -1.42 -2.10 -4.31* -4.55* -2.64 -2.31 -3.71** -3.72** -0.95 -1.35 -3.36** -4.17* -2.24 -2.26 -4.16* -4.10* -2.38 -2.46 -3.93* -3.89*

GS -1.75 -2.89 -5.05* -5.02* -0.44 -1.36 -6.29* -4.61* -2.53 -0.42 -3.25** -8.34* -1.23 -1.34* -5.23* -5.86 -2.39 -2.11 -3.07** -3.51**

LEV -1.97 -1.75 -5.69* -5.83* -1.70 -2.10 -4.35* -3.99* -1.36 -1.59 -4.80* -4.59* -2.22 -2.20* -4.47* -4.41 -2.45 -2.39 -3.76** -3.73**

ROE -2.40 -3.12 -4.92* -4.88* -1.82 -1.91 -4.17* -3.87* -2.14 -2.42 -4.60* -5.77* -2.06 -2.40** -3.62** -3.64 -2.00 -2.01 -4.79* -4.70*

17

TABLE 4.3.1 :POLS TEST FOR SECTORSBeverages Chemicals Construction and Material Engineering Telecommunication

Variables Coefficient t-stats Coefficien

t t-stats Coefficient t-stats Coefficient t-stats Coefficient t-stats

C 0.075 2.764 -2.124 -3.136 2.020 3.018 0.99254 2.07732 -0.469881 -1.034948EPS 0.012 7.378 -0.126 -0.871 0.088 0.326 0.02159 0.78870 0.70647 5.007934FCF 0.000 -2.746 0.000 -3.081 0.000 -5.526 0.00000 -2.80736 0.148456 6.921875GS 0.000 -0.154 -0.004 -1.826 0.023 2.035 0.01549 1.92068 -0.017578 -0.722994LEV 0.001 1.330 0.119 0.764 -0.583 -1.137 -0.82476 -1.55606 0.369159 3.355058ROE 0.000 -2.972 0.491 2.380 -0.911 -3.047 -0.05271 -2.67951 -0.772841 -3.886591Adj. R2 0.854 0.430 0.844 0.65095 0.977331F-stats (prob) 16.4136(0.0000) 4.079282(0.0069) 12.94915(0.000173) 5.221874(0.006513) 17.24526(0.055713)

Food Producers Forestry Household Goods Oil and Gas Personal Goods(Textile)

Variables Coefficient t-stats Coefficien

t t-stats Coefficient t-stats Coefficient t-stats Coefficient t-stats

C -1.087499-

2.579924 -12.8528 -5.9331 8.656063 4.364604 -0.440508 -0.214372 -0.771176 -1.537549EPS 0.009385 2.950831 -2.5312 -5.7611 1.668659 4.351611 -0.153231 -0.792991 0.254098 1.78997

FCF -4.50E-07-

4.357843 -2.16E-06 -2.6117 -2.94E-05 -19.00429 0.08633 0.590723 5.75E-08 0.513511

GS -0.019193-

2.201953 -0.01958 -2.6172 0.060761 3.949681 0.014132 2.870835 0.157073 1.151956LEV 0.392174 2.018584 21.84313 6.12834 -2.150279 -2.873842 -0.216527 -1.821682 0.364336 2.858064ROE -0.005672 -1.09068 0.848649 6.06342 -0.251525 -2.21186 -0.01919 -1.875182 -0.046143 -8.653879Adj. R2 0.660466 0.918411 0.928599 0.348302 0.873816F-stats (prob) 7.3918(0.000534) 9.005282(0.026784) 72.82998(0.0000) 2.672268(0.045575) 20.77484(0.000003)

18

Pharma and Biotech Tobacco Travel and Leisure Automobiles Electricity

Variables Coefficient t-stats Coefficien

t t-stats Coefficient t-stats Coefficient t-stats Coefficient t-stats

C 1.253697 1.796652 0.860629 5.68906 40.26285 5.584052 -0.012161 -0.084063 0.189044 3.199174

EPS -0.001309-

0.351978 -0.02419 -1.8254 2.616691 4.184848 0.01139 2.422323 0.047793 3.150717

FCF -1.97E-06-

1.877628 -3.33E-07 -3.0409 -2.711086 -4.902783 -1.08E-07 -1.724838 9.06E-09 0.53201GS 0.125934 0.835274 0.000124 0.09401 0.003768 0.242979 -0.000705 -0.95835 6.67E-05 0.097699

LEV -1.063562-

2.173611 0.210193 4.95867 3.292225 3.448249 0.023256 0.4011 1.15E-06 0.004566

ROE -0.047975-

2.295945 0.001686 4.04564 -3.292051 -3.230131 0.000307 0.318944 -0.000175 -0.32661Adj. R2 0.492779 0.961424 0.863138 0.25072 0.280285F-stats (prob) 2.91458(0.049297) 14.95382(0.000563) 7.567958(0.014306) 1.72087(0.1352) 2.280999(0.0465)