International Journal of Marketing, Financial Services & Management Research________________________ ISSN 2277- 3622 Vol.2, No. 5, May (2013) Online available at www.indianresearchjournals.com

PERFORMANCE ANALYSIS AND SOLVENCY PREDICTION OF INDIAN PHARMA COMPANIES PROF. JYOTI NAIR ASSISTANT PROFESSOR – FINANCE THAKUR INSTITUTE OF MANAGEMENT STUDIES & RESEARCH KANDIVALI (E), MUMBAI

_____________________________________________________________________________________

ABSTRACT Globally pharma sector is witnessing huge upheavals due to large number of drugs facing patent expiration, lack of new drugs, pricing issues. At the same time Indian pharma sector is on a growth trajectory. The sector is expected to grow to US $ 15 bn by 2015. India is one of the fastest growing pharma markets in the world with a CAGR of 15% during 2006-11. The domestic pharma market grew by 13% in the year 2011. The growth is driven by an increase in urbanization, increase in health care expenditure, rising life expectancy, shift from chronic diseases to lifestyle diseases and support from Government in the form of liberal policies. Increase in mergers and acquisition activity has also contributed to the growth in pharma sector. The pharma sector in India comprise of over 10000 listed and unlisted companies. The objective of this study is to analyse the performance of pharma companies in India and predict the solvency of selected companies using Altman‟s „Z‟ score model which is based on Multi variate Discriminant analysis. The study also purports to identify significant variables affecting the performance and solvency of the company. Financial indicators like revenues, profits, liquidity, and capital market performance would be used. The identification of performance and distress indicators of the companies is expected to provide new insight into company analysis. Financial ratios are proposed to be used as variables. The results of the study will be validated using statistical tools of regression KEYWORDS: Pharma sector, performance analysis, financial distress, „z‟ score model, ratios _____________________________________________________________________________________

Introduction The Indian pharmaceutical industry is growing at about 8-9% annually. In 2011, the growth was pegged at 15%. According to McKinsey & Co.‟s report, “Indian Pharma 2020: Propelling access and acceptance, realizing true potential.” Indian pharmacy market will grow to USD 55 billion by 2020. It also ranks India 3rd in terms of volume among the top 15 drug manufacturing countries. Patent laws are also expected to boost development of pharma products. Demand for pharma products in India is also growing due to various factors. Market research firm Cygnus forecasts that Indian bulk drug industry will grow annually @21%. McKinsey report also mentions that household spending on health care will grow from 7% to 13% by 2025. All these factors will contribute to growth of pharma sector in the coming years. 34

International Journal of Marketing, Financial Services & Management Research________________________ ISSN 2277- 3622 Vol.2, No. 5, May (2013) Online available at www.indianresearchjournals.com

According to CARE research, demand triggers for the growth are: 1. Patent drugs worth USD170 billion are expected to go off patent leading to huge market for generic drugs. 2. Exports of pharma products enjoying high margin is expected to grow at a higher rate than domestic market. 3. Increased M&A activities will consolidate the market leading to development of specialized segments 4. There are currently approx.175 UDFDA and 90 UK- MHRA approved pharma plants in India which can supply high quality pharma products globally. 5. Rural demand is expected to surge given the rising income level and awareness. 6. Change in life style has contributed to a shift from chronic diseases to lifestyle diseases. Government has also taken positive steps to promote this sector by focusing on improvement of medical infrastructure, rural health care etc. Increased budgetary allocation to National Rural Health Mission ( NRHM ) is another step in this direction. With 100% FDI being allowed health and medical services through automatic route, an increased inflow of investment in pharma sector is visible. Allowing a weighted deduction of 200% for in house research will encourage research and development of Indian pharma companies. Pharma sector in India Indian Pharmaceutical industry is forecasted to have a double digit growth due to increasing investments in India by Multi -national companies and rising pharma outsourcing to India. Clinical trial markets if also expected to grow owing to rising R& D in the sector. Bio generics and pharma packaging are the emerging segments in this sector. Indian companies have developed an expertise in generic drug segment which will also improve the earning prospects of pharma companies in India. The major segment in this sector is Pharmaceutical formulations followed by API /Bulk Drugs and Contract Research and Manufacturing ( CRAMS). Ratings agency has Fitch has assigned a stable outlook for Indian pharma sector. The major players in terms of turnover and market capitalisation are Ranbaxy Laboratories, Cipla Ltd and Dr. Reddy‟s Laboratories. All these companies have registered a growth of 10% 15% during the year 2012 (Emkay research). At this stage it becomes relevant to study the impact of such upheavals on medium sized companies in Indian pharma sector. The ability of these companies to sustain their growth in the wake of major changes in global and domestic market is worth studying and analyzing. The need to assess the financial health of companies becomes urgent so that remedial steps can be taken to correct inefficiencies in business. This also becomes very important for managers to take stock of the situation at an early stage bases on the signals derived from such studies.

35

International Journal of Marketing, Financial Services & Management Research________________________ ISSN 2277- 3622 Vol.2, No. 5, May (2013) Online available at www.indianresearchjournals.com

Financial ratios as a tool to predict company’s financial distress Financial distress is end effect of a company‟s inability to survive in challenging times. Financially strong company indicates operational efficiency, managerial efficiency, optimum capital structure and best management practices. Analysis of financial statements using ratios have been found very useful in prediction financial distress. Ratios like ROA, RONA, Asset Turnover, Debt equity can help an analyst to assess the performance of the company. Timely intervention through sound financial strategies can turnaround companies with potential distress. Objective of the study 1. To study the performance of medium size Indian companies in pharma sector 2. To examine the existence of financial distress among selected Indian companies in pharma sector using Altman‟s score model. Research Methodology 1. 23 companies with a turnover ranging from 500 crs – 1000 crs were selected for study. The data required for the study was obtained from DION database and company‟s annual reports. 2. The study is conducted for a period of 5 years from 2008-2012. 3. Altman‟s z score model is used to identify the existence of financial distress in selected companies. 4. The data is analysed for significance using step linear regression. Literature review Financial ratios have played an important part in evaluating the performance and financial condition of an entity, (Chen and Shimerda, 1981in Hossari Ghassan and Shaikh Rehman, 2005). A major function of financial statement analysis is to assess the risk of financial distress. The use of financial data to predict corporate failure has been a topic of much research interest in accounting and finance since mid 60‟s, (Poston et al, 1994). Researchers have used various ratios from time to time. Hector (2004) discusses the development of these tools. According to him, the earliest study using financial ratios was undertaken by Alexander Wall and published in 1919. Wall‟s index consisted of seven ratios viz : Current ratio, net worth to total debt, net worth to fixed assets, sales to receivables, sales to inventories, sales to fixed assets, sales to net worth, (Aziz, 1984). Subsequent studies to differentiate bankrupt companies from non bankrupt companies have been attributed to various researchers for instance Ramser and Foster (1931), Fistzpatrick (1932), Foulke (1933) and Winakar and Smith (1935) in Hector, 2004. Later Beaver (1966) in Hector 2004 analysed different financial ratios in a univariate model. Altman (1968) improved on Beaver‟s univariate model by using Multiple Discriminant Analysis (MDA). He developed a five variant discriminant model out of 22 potentially predictive ratios as Working capital /Total Assets, Retained earnings / Total Assets, EBIT / Total Assets, Market value of equity / Total Assets, Sales / Total Assets. According to him companies went bankrupt if Z score 36

International Journal of Marketing, Financial Services & Management Research________________________ ISSN 2277- 3622 Vol.2, No. 5, May (2013) Online available at www.indianresearchjournals.com

was less than 1.81 which means they are not statistically significant at 20% level approximately. Z score model was found to be helpful in predicting failure in publicly listed companies, (Wang and Campbell, 2010). Altmans‟ Z score was found useful in evaluation financial distress of Indian IT companies, Arun and Kasilingam (2011). Subsequent research also includes investigations of the characteristics of failing firms in special sectors: Altman (1973) on the railroad industry; Edmister and Schlarbaum (1974), Sinkey (1975, 1977); Martin (1977); Santomero and Vinso (1977); Pettway and Sinkey (1980) on the banking industry, Altman (1977a) on savings and loan institutions, Altman and Loris (1976) on the over the-counter broker-dealer industry; Edmister (1972) on small-business failures, Schipper(1977) and Shrieves and Stevens (1979) on the educational entities; Pinches andTrieschmann (1974) on the insurance industry (all in Salehi and Abedini, 2009) , Chen and Lee ( 1993) on oil industry and Joseph et al ( 2008) on health systems. Moyer ( pre -1986) have found that multivariate models can provide useful insights but other factors such as firm age, macro economic considerations , impact of risk would have to be incorporated in future analysis Most of the studies done have focused on developed markets. There is a need to assess the financial strength of companies in developing economies. Data collection and analysis Altman‟s Z- score model is used to identify the financial distress in selected companies. The following model proposed by Altman ( 1968 ) for manufacturing companies is used for the study : Z = 1.2 X1+1.4X2+3.3X3+0.6X4+1X5 Where X1 = Working capital to Total assets X2= Retained earnings to Total assets X3= Earnings before interest and taxes to Total assets X4 = Market value of equity to Total liabilities X5= Sales to Total assets Significance of the variables Eidleman (1995) defines each of the above ratios as follows: 37

International Journal of Marketing, Financial Services & Management Research________________________ ISSN 2277- 3622 Vol.2, No. 5, May (2013) Online available at www.indianresearchjournals.com

X1 is a liquidity ratio , the purpose of which is to measure the liquidity of the assets „in relation to firm‟s size‟ .It is the measure of net liquid asset of a concern to the total capitalization which measures the firm‟s ability to meet its maturing short-term obligations. X2 is an indicator of the „cumulative profitability‟ of the firm over time which indicates the efficiency of the management in manufacturing, sales, administration and other activities. X3 is a measure of firm‟s productivity which is crucial for the long-term survival of the company. It is a measure of productivity of an asset employed in an enterprise. The ultimate existence of an enterprise is based on earning power. It measures how effectively a firm is using its resources. It measures the managements overall effectiveness as shown by the returns generated on sales and investment. X4 defines how the market views the company. The assumption is that with information being transmitted to the market on a constant basis, the market is able to determine the worth of the company. This is then compared to firm‟s debt. It is reciprocal of familiar debt equity ratio. Equity is measured by the combined market value of all shares, while debt includes both current and long term liabilities. This measure shows how much of an asset can decline in values before liabilities exceed the assets and the concerns become insolvent. It measures the extent to which the firm has been financed by debt. Creditors look to the equity to provide the margin of safety, but by raising fund through debt, owners gain the benefit of marinating control of the firm with limited investment. X5 is defined as a „measure of management ability to compete‟. The capital turnover ratio is the standard financial measure for illustrating the sales generating capacity of the assets. The zones are identified as under: Z < 1.81 Distress zone Z between 1.81 – 2.99 Grey zone Z > 2.99 Safe zone Altman has described that firms with scores between 1.81 to 2.99 should be thought of as a grey area. Firms, with Z scores within this range, are considered uncertain about credit risk and considered marginal cases to be watched with attention. Altman (1968) formerly described the grey area as the “zone of ignorance”. This area is where firms share distress and non-distress financial characteristics and should be carefully observed before it is too late for any remedial or recovery action. Firms with Z scores below 1.81 indicate failed firm, Z score above 2.99 indicates non-bankruptcy. Altman shows that bankrupt firms have very peculiar financial profiles one year before bankruptcy (Ray, 2011) 23 companies in Pharma sector with a turnover within a range of 500 crs to 1000 crs were selected for study. The independent variables (ratios) in the discriminant function were 38

International Journal of Marketing, Financial Services & Management Research________________________ ISSN 2277- 3622 Vol.2, No. 5, May (2013) Online available at www.indianresearchjournals.com

determined for the period 2008-2012. Table 1 gives the turnover and values of X1-X5 of the selected companies and their Z score for 2012. Table1 SN Name of the company 1 2 3 4

Elder Pharmaceuticals Ltd Parabolic Drugs Ltd Ind Swift Ltd Novartis India Ltd

5 6 7 8 9 10 11 12 13 14

Ankur Drugs and Parma Ltd Unichem Laboratories Ltd Shasun Pharmaceuticals Ltd Strides Arcolab Ltd Panacea Biotec Ltd FDC Ltd Hikal Ltd Aarti Drugs Ltd Twilight Litaka Parma Ltd Claris Life Sciences Ltd JB Chemicals and Pharmaceuticals Ltd Sharon Biomedicine Ltd Ajanta Parma Ltd Wyeth Ltd Merck Ltd Indoco Remedies Ltd Granules India Ltd Astra Zeneca Parma India Ltd Fresenius Kabi Oncology Ltd.

15 16 17 18 19 20 21 22 23

Turnover Rs. Crs 984.69 924.34 876.47 843.61

X2 0.15 0.04 0.13 0.26

X3 0.12 0.10 0.09 0.13

X4 0.67 0.20 0.09 2.18

X5 0.57 0.70 0.71 0.47

Z 2.10 3.09 3.09 3.09

824.00 803.19 735.91 728.50 700.58 699.24 694.24 665.36 657.34 650.95

X1 0.44 0.44 0.40 0.45 0.04 0.17 0.32 0.50 0.14 0.06 0.19 0.27 0.65 0.25

0.04 0.48 0.16 0.06 0.10 0.44 0.18 0.22 0.24 0.27

0.04 0.11 0.06 0.05 -0.09 0.20 0.13 0.09 0.20 0.10

0.02 5.97 1.95 2.61 0.72 1.72 1.03 0.44 0.09 1.98

0.48 0.78 1.01 0.22 0.38 0.82 0.61 1.09 1.39 0.41

0.63 5.59 3.01 2.62 0.83 3.21 2.13 2.28 3.24 2.58

642.41 606.05 604.27 588.67 576.31 564.31 563.07 530.89 524.32

0.34 0.56 0.29 0.70 0.62 0.24 0.19 0.27 0.29

0.41 0.15 0.11 0.54 0.10 0.24 0.20 0.39 0.31

0.07 0.11 0.17 0.32 0.16 0.10 0.11 0.08 0.04

0.56 0.99 1.42 12.72 11.21 0.32 0.44 27.33 3.45

0.55 1.03 0.98 0.90 1.14 0.86 1.11 1.55 0.52

2.08 2.88 2.87 11.19 9.29 1.94 2.85 19.09 3.52

Analysis Total no companies 23 100%

of Safe zone 11 48%

Grey zone

Financially distressed

10 43%

02 9%

39

International Journal of Marketing, Financial Services & Management Research________________________ ISSN 2277- 3622 Vol.2, No. 5, May (2013) Online available at www.indianresearchjournals.com

As seen above, 48% of the companies selected are in the grey zone and 9% of the sample selected is financially distressed. 10 companies out of 23 companies have been identified as potential cases of financial distress. Z- score of the companies in the grey zone for the years 2008 – 2012 is given in Table 2 Table 2 SN 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Name of the company Elder Pharmaceuticals Ltd Parabolic Drugs Ltd Ind Swift Ltd Novartis India Ltd Ankur Drugs and Parma Ltd Unichem Laboratories Ltd Shasun Pharmaceuticals Ltd Strides Arcolab Ltd Panacea Biotec Ltd FDC Ltd Hikal Ltd Aarti Drugs Ltd Twilight Litaka Parma Ltd Claris Life Sciences Ltd JB Chemicals and Pharmaceuticals Ltd Sharon Biomedicine Ltd Ajanta Parma Ltd Wyeth Ltd Merck Ltd Indoco Remedies Ltd Granules India Ltd Astra Zeneca Parma India Ltd Fresenius Kabi Oncology Ltd.

2012 2.1 3.09 3.09 3.09 0.63 5.59 3.01 2.62 0.83 3.21 2.13 2.28 3.24 2.58 2.08 2.88 2.87 11.19 9.29 1.94 2.85 19.09 3.52

2011 2.23 1.74 1.71 10.03 1.51 4.88 2.11 1.8 2.08 3.53 1.73 2.08 3.03 1.97 2.59 2.6 2.68 14.24 9.82 3.27 2.34 18.33 2.99

2010 2.28 2.06 1.85 10.53 1.74 4.73 2.69 1.31 2.08 4.14 2.21 2.76 3.42 2.17 2.95 2.58 2.21 11.33 9.36 3.94 2.53 25.47 4.29

2009 2.24 1.8 1.83 9.94 1.81 6.12 2.09 1.07 1.65 3.64 2.13 2.31 3.53 NA 2.55 2.56 1.9 9.48 7.35 NA 2.03 16.25 3.92

2008 2.79 2.14 1.71 6.91 2.44 4.08 1.78 0.79 2.65 2.76 1.77 1.77 3.52 NA 2.05 2.8 1.99 7.58 6.72 2.41 1.54 11.64 3.11

From the above table it is seen that the z score of the financially distressed companies have been continuously declining. The main triggers identified are EBIT and Market value of equity. Regression Analysis Regression between independent variable X1, X2, X3, X4 and X5 and dependant variable Z was computed using SPSS. 61% of the companies selected gave results while 39% of the sample gave indeterminate results. The results are summarized in Table 5 40

International Journal of Marketing, Financial Services & Management Research________________________ ISSN 2277- 3622 Vol.2, No. 5, May (2013) Online available at www.indianresearchjournals.com

Table 5 SN

Name of the company

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Elder Pharmaceuticals Novartis Unichem Lab Shasun Parma Strides Arcolab Panacea Biotec Hikal Ltd Aarti Drugs Ajanta Parma Wyeth Ltd Merck Ltd Indoco remedies Granules India Ltd Astra Zeneca Parma

Significant Variable X4 X3, X4 X3,X4 X3,X4 X4 X3,X4 X3 X4 X2 X4,X5 X3,X4 X4 X3 X4

Levels of significance 0.001 0.001 0.001 0.022 0.001 0.009 0.015 0.002 0.002 0.005 0.000 0.031 0.001 0.001

X3 and X4 are the most dominant variables in determining Z- score. EBIT to Total Assets and Market value to Total Liabilities are the factors influencing a company‟s Z- score. Observations 1. 48 % of the sample companies selected for study is in the „Grey Zone‟ and 9% of the companies are financially distressed. The main factors of financial distress are decreasing EBIT levels and volatility in market price of the shares. The companies in the grey zone have the potential of becoming distressed in future if EBIT levels are not improved or maintained. 2. In case of companies with very high Z score (Wyeth Ltd and Astra Zeneca Pharma Ltd), analysis gives varied results. Wyeth Ltd has shown an increase in operating income and margin with a very high current ratio. The MPS has also increased consistently thereby making it a very healthy company. Astra Zeneca Pharma Ltd. has shown reduction in operating income and operating profits. Its current ratio has also reduced from 2.25 in 2010 to 1.6 in 2012 thereby indicating that the fundamentals have not improved. However its MPS has shown very high volatility with closing prices in 2008 of Rs.495 to Rs. 1685 in 2012. 3. In case of companies with very low Z score ( Ankur Drugs & Pharma Ltd and Panacea Biotec Ltd.) , the results are consistent. Both companies have shown a considerable reduction in operating incomes and profits. The current ratio has also declined. The MPS of Ankur Drugs and Pharma have fallen from Rs. 96 in 2010 to Rs.18 in 2012 (closing prices). The MPS of Panacea Biotec have also shown high volatility. 41

International Journal of Marketing, Financial Services & Management Research________________________ ISSN 2277- 3622 Vol.2, No. 5, May (2013) Online available at www.indianresearchjournals.com

4. Novartis Ltd‟s Z –score has reduced from 10.03 in 2011 to 3.09 in 2012. Though the company is in a safe zone, its total liabilities have increased leading to reduction in Working capital to Total Assets ratio and Market value of equity to Total Liabilities ratio. 5. In case of Parabolic Drugs Ltd. Z score has improved basically due to increased revenues and EBIT. 6.In the case of Indoco Remedies Ltd., the reduction in Z-score is caused by a heavy decrease in its share price. The company‟s Sales and EBIT has shown a consistent growth. 7. Unichem Laboratories Ltd. Consistently high Z score is due to its consistently increasing Sales. Other companies with a Z- score above 2 are characterized with: a) Increase in operating income and profit b) Current ratio of more than 1.5 c) Net profit margins in the range 7% - 15% d) Marginal volatility in MPS. The above observations are validated in Table 5. Conclusion Financial ratios can be used to predict potential distress. Application of Z score model could identify companies with weak fundamentals. EBIT, MPS and Sales are the significant variables affecting Z score. Study of financial ratios and observing trends will help the management in evaluating the performance of the company and initiate steps to avoid financial distress and bankruptcy. Limitation of the study The study has focused on 5 financial ratios to identify the financial health of companies. Research has shown that other variables may be used as a tool to predict financial distress. The factors affecting the volatility of MPS have not been identified. Scope for future study Study can be done to identify other financial ratios and their effectiveness to predict financial distress. The model can be applied to large cap companies and small cap companies in pharma sector to evaluate the predictability of the model.

42

International Journal of Marketing, Financial Services & Management Research________________________ ISSN 2277- 3622 Vol.2, No. 5, May (2013) Online available at www.indianresearchjournals.com

References Aziz, M.A. & Dar, H.A. (2006), "Predicting corporate bankruptcy: where we stand?", Corporate Governance, vol. 6, no. 1, pp. 18-33. Chen, K. H., & Shimerda, T. A. (1981). An empirical analysis of useful financial ratios. FinancialManagement (Pre-1986), 10(1), 51-51. Retrievedfromhttp://search.proquest.com/ docview/ 205139148? accountid=34833 Dhankani Dinesh (2005) : “ Fundamental Analysis and stock returns : India ” in The Business Review; vol; 4 ,issue 2, pp 23-32 Ganesalingam, S;Kumar, Kuldeep (2001): “Detection of financial distress via multivariate statistical analysis ” in Managerial Finance;Volume 27, 4; pp 45-55 Hossari, Ghassan and Rahman, Sheikh (2005): “A Comprehensive Formal Ranking of the Popularity of Financial Ratios in Multivariate Modeling of Corporate Collapse” in Journal of American Academy of Business, vol 6 issue 1 pp 321-327. Leano, Hector J ( 2004) : “Discriminant analysis, factor analysis and linear regression analysis to classify financially distressed firms and predicting bankruptcy using financial ratios and macroeconomic predictors: Model application to selected M&A.”Dissertations and Theses; 2004 Mahdi Salehi ; Bizhan Abedini (2009) : “Financial Distress Prediction in Emerging Market: Empirical Evidences from Iran” in Interdisciplinary Journal of Contemporary Research in Business (2009) May , Vol 1 Issue 1; pp 6-26 Moyer, R Charles(pre-1986 ) : “Forecasting financial failure: a re-examination” in Financial Management, vol 6, Issue 1,pg. 11-22 Murty, A V N;Misra, D P(2004) : “Cash Flow Ratios as Indicators of Corporate Failure” in Finance India; Volume 18, Issue 3;pp 1315-1325 Murugan Anandarajan;Lee, Picheng;Anandarajan, Asokan (2001) : “ Bankruptcy prediction of financially stressed firms: an examination of the predictive accuracy of ” in Intelligent Systems in Accounting, Finance and Management; vol 10, issue 2; pp 60-81 Poston, Kay M; Harmon, W Ken; Gramlich, Jeffrey D (1994): “A test of financial ratios as predictors of turnaround versus failure among financially distressed firms” in Journal ofApplied Business Researchhttp://search.proquest.com/assets/r7.0.54/core/spacer.gif10.1http://search.proquest.com/assets/r7.0.5-4/core/spacer.gif (Winter 1994): 41. R.Arun ; P. Kasilingam (2011): “ Predicting solvency: Indian IT companies” in SCMS Journal of Management ; Jan-Mar, 2011,pp 81-95 Ying Wang;Michael Campbell (2010); “Business Failure Prediction for Publicly Listed Companies in China ” in Journal of Business and Management ; vol 16, issue 1 , pp 75-88. http://www.equitymaster.com/research-it/sector-info/pharma/Pharmaceuticals-Sector-AnalysisReport.asp, Date of access : 01/10/12 http://www.cci.in/pdf/surveys_reports/indian-pharmaceuticals-industry.pdf, Date of access : 01/10/12

43