WORKING CAPITAL MANAGEMENT: A STUDY ON INDIAN PHARMA COMPANIES

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1 ISSN (Print): ISSN (Online): WORKING CAPITAL MANAGEMENT: A STUDY ON INDIAN PHARMA COMPANIES Sri Ayan Chakraborty Faculty: Management: University Program (Techno India University), ICA Eduskills chakrabortyayankumar78@gmail.com ABSTRACT The three main aspects of Corporate Finance are Financing Decision, Dividend Decision and Working Capital Management. Working Capital is the amount of fund needed to manage the Operating Activities of an organization. Excess of Current Assets over Current Liabilities is termed as Working Capital. According to Smith K. V, Working capital management is concerned with the problems that arise in attempting to manage the current assets, current liabilities and the interrelationship that exist between them. Working Capital Management is considered as one of the most essential part of business. It comprises funds invested in Current Assets which in the ordinary course of business can be turned into cash within a short period without undergoing diminishing in value and without disruption of the organization. Efficient management of working capital is a fundamental part of the overall corporate strategy since it affects liquidity, profitability and at the same time has an impact on both EPS and PE ratio. This paper studies the impact of working capital Turnover on ROCE, ROE, ROTA, EPS and P/E of the Indian Leading Pharma Companies. Keywords: Indian Pharma Sector, Operating Profit Margin, Net Profit Margin, Current Ratio, Liquid Ratio, Working Capital Turnover, Inventory, Debtors, Creditors Turnover, ROCE, ROE, ROTA, EPS, P/E I. OBJECTIVE OF THE STUDY 1. To analysis the Working Capital of Sun Pharma, Lupin, Cipla, Dr Reddy's, Biocon, Abbott, Aurobindo Pharma & Cadila. To highlight the financial performance and return of the selected companies using Profitability Ratios, Working Capital Ratios, Liquidity Ratios REVIEW OF LITERATURE The researcher and economists have recognized that proper Working Capital Management is necessary to analyse and improve the financial performance of Pharma sector. A large number of studies have been conducted in the field of operation and financial performance of Cement Companies. A brief review of some of these studies has been presented. Grablowsky (1976), a significant relationship between various success measures and the employment of formal working capital policies and procedures was found. Cash conversion cycle and cash flow management plays vital role for overall financial management of all firms, especially those which are capital constrained and more reliant on short-term sources of finance Narasimhan & Murty (001), focus on improving return on capital employed by targeting some critical areas such as cost containment, reducing investment in working capital and improving working capital efficiency. Shin & Soenen (1998) studied the effect of working capital management on corporate profitability using sample of 58,985 firm years covering the period They examined the relationship between firm s net trade cycle and its profitability and found a strong negative relationship. They also found that shorter net trade cycles are associated with higher risk adjusted stock returns. Deloof (003) studied effect of working capital management on Belgian firms profitability. He used gross operating income as a measure of profitability and found significant negative relation between gross operating income and the number of days accounts receivable, inventories, accounts payable. He also suggested that less profitable firms wait longer to pay their bills hence negative relationship between accounts payable and profitability. Vol: I. Issue XCI, August 018 1

2 ISSN (Print): ISSN (Online): Raheman & Nasr (007) analysed different variables of working capital management on firms listed on Karachi Stock Exchange. They used net operating profit as a measure of profitability. Along with measures of working capital management including average collection period, inventory turnover ratio, average payment period and cash conversion cycle they includes current ratio as a measure of liquidity and found it to be most important liquidity measure that affects profitability. A. Ajanthan (013) studied the relationship between liquidity and profitability of trading companies in Sri Lanka using current and quick ratio for liquidity and return on equity and return on asset for profitability. He found significant impact of liquidity on profitability. Chandra Kartik (01) in his paper on Trends in Liquidity Management & impact on profitability : states that the selected companies always try to maintain adequate amount of net working capital In relation to Current Liability so as to maintain a good amount of liquidity. Eljelly (004) examined the liquidity-profitability trade-off on sample of firms in Saudi Arabia. He found significant negative relationship between liquidity, measured by current ratio, and profitability. He also found negative relationship being more evident in case of firms having longer cash conversion cycles and higher current ratios. II. SCOPE OF STUDY The study studies the Working Capital Management of Leading Indian Pharma Companies and impact of Working Capital Turnover on ROCE, ROE, ROTA, EPS, P/E ratio. Management of working capital refers to management of current assets, current liabilities and the relationship between them with the basic goal of maintaining a satisfactory level of working capital. Sound working capital policy ensures higher profitability and proper liquidity of a firm. PERIOD OF STUDY The study covers a period of 6 years from to METHODOLOGY Sources of Data The study is based on secondary data. Information required for the study has been collected from the Annual Reports of Sun Pharma, Lupin, Cipla, Dr Reddy's, Biocon, Abbott, Aurobindo Pharma & Cadila and different books, journal, magazines, and data collected from various websites. III. Tools Applied In this study various tools: Financial Tools Ratio Analysis and Statistical Tools (i.e.) Mean and ANOVA, t-test has been used for data analysis. MEAN = Sum of variable/n Standard Deviation is used to see how measurements for a group are spread out from Mean. A low Standard Deviation means that most of the numbers are very close to the average and vice-versa. (SD) = X/N-( X/N) Coefficient of Variation is a standardized measure of dispersion of a probability distribution or frequency distribution. It is the ratio of standard deviation to mean. Higher the coefficient of variation, the greater the level of dispersion around mean and vice-versa. Coefficient of Variation (COV) = SD/MEAN* 100 t-test (Two-Sample Assuming Unequal Variances): t-test assesses whether the means of two groups are statistically different from each other. Hypothesis An ANOVA is statistical hypothesis in which the sampling distribution of test statistic when null hypotheses is true. Null hypotheses have been set and adopted for the analysis of data. The null hypotheses are represented by H 0. It is a negative statement which avoids personal bias of investigator during data collection as well as the time of drawing conclusion. IV. LIMITATION OF THE STUDY 1. The study is related to a period of 6 years.. Data is secondary i.e. they are collected from the published Annual Reports 3. Profitability, Liquidity and Working Capital Turnover ratios have been taken for the study. INDIAN PHARMA SECTOR Indian pharma industry enjoys an important position in the global pharmaceuticals industry. The Indian pharmaceuticals market is the third-largest in terms of volume and thirteenth-largest in terms of value. Indian pharma industry is mainly operated as well as controlled by dominant foreign companies having subsidiaries in India due to availability of cheap labor in India at low cost. The Vol: I. Issue XCI, August 018

3 ISSN (Print): ISSN (Online): Revenue of the Indian Pharma Sector increased from $ 9.61 billion to $ 7.57 billion between 011 & 017 and is expected to reach $ 55 billion by the end of 00. EXHIBIT 1: REVENUE INDIAN PHARMA SECTOR ($ BILLIONS) Revenue ($ Bn) Growth (%) % % % % 4.35% % % (P) INDIA S LEADING PHARMA COMPANIES Sun Pharma: Sun Pharm is an international specialty pharma company which manufactures and markets pharmaceuticals formulations as branded generics in India as well as abroad. Its business is divided into four segments: Indian Branded Generics, US Generics, International Branded Generics (ROW) and Active Pharmaceutical Ingredients (API). Its brands are prescribed in chronic therapy areas like cardiology, psychiatry, neurology, gastroenterology, diabetology and respiratory. It makes specialty APIs, including peptides, steroids, hormones and anticancer. Lupin: Headquartered in Mumbai, Lupin is an innovation led transnational pharma company producing a wide range of quality, affordable generic and branded Pharmaceutical Ingredients in Cardiovascular, Diabetology, Asthma, Pediatrics, Anti-Infectives, NSAIDs therapy segments, Anti-TB etc. It is the 7th largest company in terms of market cap and 10th largest generic pharmaceutical company in terms of revenue globally. 67% of the overall business of the Company comes from International Markets. Cipla: Headquartered in Mumbai, Cipla is a leading global pharmaceutical company, dedicated to high-quality, branded and generic medicines. Cipla develops medicines to treat respiratory, cardiovascular disease, arthritis, diabetes, weight control, depression etc. Dr Reddy's: Headquartered in Hyderabad, Dr. Reddy's Laboratories is an Indian multinational pharmaceutical company. Through its three businesses Pharmaceutical Services & Active Ingredients, Global Generics, and Proprietary Products, it offers a portfolio of products and services including APIs, custom pharmaceutical services, generics, biosimilars and differentiated formulations. Its major therapeutic focus is on gastrointestinal, cardiovascular, diabetology, oncology, pain management and anti-infective. Its markets include India, USA, Russia and Europe etc. Biocon: Biocon is an Indian biopharma company based in Bangalore. It is committed to reduce therapy costs of chronic diseases like diabetes, cancer and autoimmune disease etc. It manufactures generic active pharmaceutical ingredients which are sold across the globe, including developed markets of the US and Europe. It also manufactures novel biologics, biosimilar insulins and antibodies, which are sold as branded formulations. Abbott: Headquartered in Mumbai, Abbott is one of India's fastest growing pharma companies. Its brands are prescribed in Women's Health, Gastroenterology, Neurology, Thyroid, Diabetes & Urology, Pain Management, Vitamins, Anti-Infectives etc. It employs over,600 people and reaches customers through a wide network of 35 distribution points, catering to over 4,500 stockists and 150,000 retail outlets Aurobindo Pharma: Headquartered in Hyderabad, Aurobindo Pharma manufactures generic pharmaceuticals and active pharmaceutical ingredients. It manufactures generic active pharmaceutical ingredients in antibiotics, anti-retrovirals, cardiovascular products, central nervous system products etc. Cadila: Headquartered in Ahmedabad, Cadila is of India s leading pharma company which has been developing and manufacturing pharmaceutical products in India as well as overseas. It specialization area includes cardiovascular, gastrointestinal, analgesics, haematinics, anti-infectives and antibiotics, respiratory agents, antidiabetics and immunologicals. Vol: I. Issue XCI, August 018 3

4 ISSN (Print): ISSN (Online): PROFITABILITY Profit is the prime motive of every business. It plays a pivotal role behind the success and growth of an enterprise. Profitability is the main base for liquidity as well as solvency. Analysing a company s profitability is an important part of financial statement analysis. Profitability of a company measures the ability to generate earnings. Operating Profit Margin Ratio: It shows the relationship between Operating Profit and Net Sales. EXHIBIT : OPERATING PROFIT MARGIN (%) Mean SD COV CAGR (%) Exhibit- depicts that Sun Pharma reported the highest mean value in terms of Operating Profit Margin followed by Biocon, Cipla, Aurobindo Pharma etc. Standard deviation of Cipla is the highest indicating the maximum deviation from mean followed by Aurobindo Pharma, Biocon etc. Aurobindo Pharma reported the highest CAGR of 38.9%. Sun Pharma, Cipla, Dr Reddy's, Biocon & Cadila reported a negative CAGR. =µ =µ 3 =µ 4 =µ 5 =µ 6 =µ 7 =µ 8 (Operating Profit of Pharma Companies doesn t differ over years) µ µ 3 µ 4 µ 5 µ 6 µ 7 µ 8 (Operating Profit of Pharma Companies differ over years) EXHIBIT 3: OPERATING PROFIT MARGIN: ANOVA SUN PHARMA LUPIN CIPLA DR REDDY'S BIOCON ABBOTT AUROBINDO PHARMA CADILA Between Groups, Within Groups 5, Total 7, Above analysis shows that the F value ( ) is more than the table value ( ) therefore null hypothesis is rejected. Therefore it is concluded that Operating Profit Margin of the Pharma Companies differs over the years. Net Margin Ratio: It shows the relationship between Net profit and sales. ie, Profit left for equity share holders as a percentage of Net sales. Vol: I. Issue XCI, August 018 4

5 Vol: I. Issue XCI, August 018 ISSN (Print): ISSN (Online): EXHIBIT 4: NET PROFIT MARGIN (%) Mean SD COV CAGR (%) Exhibit-4 depicts that Sun Pharma reported the highest mean value in terms of Net Profit Margin followed by Cipla, Aurobindo Pharma, Lupin etc. Standard deviation of Cipla is the highest indicating the maximum deviation from mean followed by Aurobindo Pharma, Biocon etc. Aurobindo Pharma reported the highest CAGR of 61.5%. Sun Pharma, Cipla, Dr Reddy's & Biocon reported a negative CAGR. =µ =µ 3 =µ 4 =µ 5 =µ 6 =µ 7 =µ 8 (Net Profit of Pharma Companies doesn t differ over years) µ µ 3 µ 4 µ 5 µ 6 µ 7 µ 8 (Net Profit of Pharma Companies differ over years) EXHIBIT 5: NET PROFIT MARGIN: ANOVA SUN PHARMA LUPIN CIPLA DR REDDY'S BIOCON ABBOTT AUROBINDO PHARMA CADILA Between Groups 1, Within Groups, Total 4, Above analysis shows that the F value (.77893) is more than the table value (.4904) therefore null hypothesis is rejected. Therefore it is concluded that Net Profit Margin of the Pharma Companies differs over the years. LIQUIDITY & WORKING CAPITAL MANAGEMENT Working Capital Management plays a significant role to enhance the profitability of an entity. Moreover, Profit has a direct relation with Liquidity. Working Capital (WC) is a financial metric which represents operating liquidity available to a business, or an entity. Working Capital is calculated as current assets minus current liabilities. If Current Assets are less than Current Liabilities, an entity has a Working Capital Deficiency. EXHIBIT 6: WORKING CAPITAL ,749 1,0 36,436 4,076 9,839 4,477 3,490 6, ,618 1,36 45,064 8,679 1,167 5,455 7,059 5, ,969 34,709 30,884 45,353 1,59 7,089 14,111 6, ,488 43,407 37,964 54,36 8,776 8,9,979 10, ,973 48,6 10,65 54,469 3,05 10,684 4,541 8, ,666 58,336 54,980 1,638 3,694 1,614 5,839 7,165 Mean 14,077 36,383 35,997 36,596 15,00 8,10 16,337 7,33 SD 35,81 17,3 14,934 17,350 6,67 3,106 9,569,045 COV CAGR (%)

6 ISSN (Print): ISSN (Online): Exhibit-6 depicts that Sun Pharma has the highest mean value in terms of Working Capital followed by Lupin, Dr Reddy's, Cipla etc. Standard deviation of Sun Pharma is the highest indicating the maximum deviation from mean followed by Dr Reddy's, Lupin, Cipla etc Aurobindo Pharma reported the highest CAGR of 49.%. Only Dr Reddy's reported a negative CAGR of 1.1%. =µ =µ 3 =µ 4 =µ 5 =µ 6 =µ 7 =µ 8 (Working Capital of Pharma Companies doesn t differ over years) µ µ 3 µ 4 µ 5 µ 6 µ 7 µ 8 (Working Capital of Pharma Companies differ over years) EXHIBIT 7: WORKING CAPITAL: ANOVA SUN PHARMA LUPIN CIPLA DR REDDY'S BIOCON ABBOTT AUROBINDO PHARMA CADILA Between Groups 61,060,6, ,7,946, E Within Groups 11,67,131, ,678,78.6 Total 7,37,753, Above analysis shows that the F value ( ) is more than the table value (.4904) therefore null hypothesis is rejected. Therefore it is concluded that Working Capital of the Pharma Companies differs over the years. LIQUIDITY RATIOS It refers to the ability of a firm to honour its short term obligations. Here short term generally means one year or within the working capital cycle. The important Liquidity ratios are as follows. Current Ratio: It measures the excess of Current assets over the Current Liabilities of an entity. Higher the Current Ratio indicates that firm can easily meet up its short term obligations with its available Current Assets. It should be noted that a firm with high proportion of Current Assets in the form of Cash and Debtors is more liquid than a firm with its maximum Current Assets in the form of Inventories, even though both have the same Current Ratio. Current Ratio also depends on the operating cycle of a firm. Longer the operating cycle, higher the Current ratio and vice versa. Normally a Current Ratio of :1 is acceptable. EXHIBIT 8: CURRENT RATIO Mean SD COV CAGR (%) Exhibit-8 depicts that Abbott has the highest mean value in terms of Current Ratio followed by Sun Pharma, Cipla etc. Standard deviation of Cipla is the highest indicating the maximum deviation from mean followed by Lupin, Abbott, Biocon. Lupin reported the highest CAGR of 7.01%. Sun Pharma, Cipla & Dr Reddy's all reported a negative CAGR. =µ =µ 3 =µ 4 =µ 5 =µ 6 =µ 7 =µ 8 (Current Ratio of Pharma Companies doesn t differ over years) µ µ 3 µ 4 µ 5 µ 6 µ 7 µ 8 (Current Ratio of Pharma Companies differ over years) Vol: I. Issue XCI, August 018 6

7 ISSN (Print): ISSN (Online): EXHIBIT 9: CURRENT RATIO: ANOVA SUN PHARMA LUPIN CIPLA DR REDDY'S BIOCON ABBOTT AUROBINDO PHARMA CADILA Between Groups E Within Groups Total Above analysis shows that the F value ( ) is more than the table value (.4904) therefore null hypothesis is rejected. Therefore it is concluded that Working Capital of the Pharma Companies differs over the years. Liquid Ratio: It refers to the ability of a firm to meet its short term obligations. Liquid / Quick / Acid Test Ratio = (Current Assets Stock) / Current Liabilities EXHIBIT 10: LIQUID RATIO Mean SD COV CAGR (%) Exhibit-10 depicts that Sun Pharma has the highest mean value in terms of Liquid Ratio followed by Abbott, Biocon etc. Standard deviation of Pharma is the highest indicating the maximum deviation from mean followed by Cipla, Abbott etc. Lupin reported the highest CAGR of 10.18%. Sun Pharma, Cipla & Dr Reddy's all reported a negative CAGR. =µ =µ 3 =µ 4 =µ 5 =µ 6 =µ 7 =µ 8 (Liquid Ratio of Pharma Companies doesn t differ over years) µ µ 3 µ 4 µ 5 µ 6 µ 7 µ 8 (Liquid Ratio of Pharma Companies differ over years) EXHIBIT 11: LIQUID RATIO: ANOVA SUN PHARMA LUPIN CIPLA DR REDDY'S BIOCON ABBOTT AUROBINDO PHARMA CADILA Vol: I. Issue XCI, August 018 7

8 Vol: I. Issue XCI, August 018 ISSN (Print): ISSN (Online): Between Groups E Within Groups Total Above analysis shows that the F value ( ) is more than the table value ( ) therefore null hypothesis is rejected. Therefore it is concluded that the Liquid Ratio of the Cement Companies differ over the years. TURNOVER RATIOS Turnover ratios are also known as Activity Ratios or Asset Management Ratios. It helps to measure, how well the Assets are employed by a firm. Working Capital Turnover: It reflects the efficiency of WCM management by a firm during a financial period. Higher the Working Capital Turnover ratio indicates that the inventories have been managed more efficiently and vice versa. Working Capital Turnover = Net Sales / (Current Assets Current Liabilities) EXHIBIT 1: WORKING CAPITAL TURNOVER Mean SD COV CAGR (%) Exhibit-1 depicts that Cadila has the highest mean value in terms of Working Capital Turnover followed by Aurobindo Pharma, Dr Reddy's, Cipla, Lupin etc. Standard deviation of Cipla is the highest indicating the maximum deviation from mean followed by Dr Reddy s, Aurobindo Pharma etc. Dr Reddy's reported the highest CAGR of.6%. Lupin, Biocon, Abbott & Aurobindo Pharma all reported negative CAGR. =µ =µ 3 =µ 4 =µ 5 =µ 6 =µ 7 =µ 8 (Working Capital Turnover of Pharma Companies doesn t differ over years) µ µ 3 µ 4 µ 5 µ 6 µ 7 µ 8 (Working Capital Turnover of Pharma Companies differ over years EXHIBIT 13: WORKING CAPITAL TURNOVER: ANOVA SUN PHARMA LUPIN CIPLA DR REDDY'S BIOCON ABBOTT AUROBINDO PHARMA CADILA Between Groups E Within Groups Total Above analysis shows that the F value ( ) is more than the table value ( ) therefore null hypothesis is rejected. Therefore it is concluded that the Working Capital Turnover of the Cement Companies differ over the years. Inventory Turnover Ratio: It reflects the efficiency of Inventory management by a firm during a financial period. Higher the Inventory Turnover ratio indicates that the inventories have been managed more efficiently and vice versa. Inventory includes Raw Materials, Work-in-Progress and Finished Goods 8

9 Vol: I. Issue XCI, August 018 ISSN (Print): ISSN (Online): Inventory Turnover Ratio = Cost of Goods Sold (COGS) / Average Inventory EXHIBIT 14: INVENTORY TURNOVER RATIO Mean SD COV CAGR (%) Exhibit-14 depicts that Abbott has the highest mean value in terms of Inventory Turnover followed by Biocon, Dr Reddy's etc. Standard deviation of Biocon is the highest indicating the maximum deviation from mean followed by Cadila, Dr Reddy's, Abbott. Biocon reported the highest CAGR of 4.58%. Only Abbott reported a negative CAGR. =µ =µ 3 =µ 4 =µ 5 =µ 6 =µ 7 =µ 8 (Inventory Turnover of Pharma Companies doesn t differ over years) µ µ 3 µ 4 µ 5 µ 6 µ 7 µ 8 (Inventory Turnover of Pharma Companies differ over years EXHIBIT 15: INVENTORY TURNOVER: ANOVA SUN PHARMA LUPIN CIPLA DR REDDY'S BIOCON ABBOTT AUROBINDO PHARMA CADILA Between Groups E Within Groups Total Above analysis shows that the F value ( ) is more than the table value ( ) therefore null hypothesis is rejected. Therefore it is concluded that Inventory Turnover of Cement Companies differ over the years. Debtors Turnover Ratio: Debtors Turnover ratio measures the liquidity of a firm in relation to its Debtors. It reflects the efficiency of management of Receivables by a firm during a financial period. Debtors Turnover Ratio = Net Sales/ Average Debtors EXHIBIT 16: DEBTORS TURNOVER Mean SD COV CAGR (%) Exhibit-13 depicts that Abbott has the highest mean value in terms of Debtors Turnover followed by Lupin, Cipla, Cadila etc. Standard deviation of Lupin is the highest indicating the maximum deviation from mean followed by Abbott, Sun Pharma. Abbott reported the highest CAGR of 8.1%. Lupin, Dr Reddy's, Cadila reported a negative CAGR. 9

10 ISSN (Print): ISSN (Online): =µ =µ 3 =µ 4 =µ 5 =µ 6 =µ 7 =µ 8 (Debtors Turnover of Pharma Companies doesn t differ over years) µ µ 3 µ 4 µ 5 µ 6 µ 7 µ 8 (Debtors Turnover of Pharma Companies differ over years EXHIBIT 17: DEBTORS TURNOVER: ANOVA SUN PHARMA LUPIN CIPLA DR REDDY'S BIOCON ABBOTT AUROBINDO PHARMA CADILA Between Groups E Within Groups Total 1, Above analysis shows that the F value ( ) is more than the table value ( ) therefore null hypothesis is rejected. Therefore it is concluded that Debtors Turnover of Cement Companies differ over the years. Creditors Turnover Ratio: It measures the time taken by a firm to pay off its Creditors or Suppliers. This ratio depends on Inventory and Debtors Turnover Ratio. Creditors Turnover Ratio = Cost of Goods Sold / Average Creditors EXHIBIT 18: CREDITORS TURNOVER Mean SD COV CAGR (%) Exhibit-18 depicts that Abbott has the highest mean value in terms of Creditors Turnover followed by Dr Reddy's, Cipla, Cadila etc. Standard deviation of Abbott is the highest indicating the maximum deviation from mean followed by Dr Reddy s, Cadila etc. Lupin reported the highest CAGR of 5.14%. Sun Pharma, Biocon, Abbott, Aurobindo Pharma and Cadila all reported a negative CAGR. =µ =µ 3 =µ 4 =µ 5 =µ 6 =µ 7 =µ 8 (Creditors Turnover of Pharma Companies doesn t differ over years) µ µ 3 µ 4 µ 5 µ 6 µ 7 µ 8 (Creditors Turnover of Pharma Companies differ over years EXHIBIT 19: CREDITORS TURNOVER: ANOVA SUN PHARMA LUPIN CIPLA DR REDDY'S BIOCON ABBOTT AUROBINDO PHARMA CADILA Vol: I. Issue XCI, August

11 ISSN (Print): ISSN (Online): Between Groups E Within Groups Total Above analysis shows that the F value ( ) is more than the table value ( ) therefore null hypothesis is rejected. Therefore it is concluded that Creditors Turnover of Cement Companies differ over the years. T-Test: It is used to test the null hypothesis that the variances of two populations are not equal. If t Stat value lies between - t Critical two tail and + t Critical two test we don t reject Null Hypothesis. Working Capital Management is an essential ingredient for every organisation. Working Capital Turnover measures how efficiently a business uses its working capital to generate sales. High WC Turnover ratio increases organisations efficiency and helps it to manage its operations smoothly. Moreover Working Capital management have an impact on Profitability as well as Liquidity and also helps to enhance Shareholders wealth. EXHIBIT 0: T-TEST: TWO-SAMPLE ASSUMING UNEQUAL VARIANCES (SUN PHARMA) ROCE ROE ROTA EPS P/E WC T/O Mean Variance Observations Hypothesized Mean Difference df t Stat P(T<=t) one-tail t Critical one-tail P(T<=t) two-tail t Critical two-tail ROCE & Working Capital Turnover = µ (There is significant relationship between ROCE & Working Capital Turnover, Variance is not Equal) µ (There is significant no relationship between ROCE & Working Capital Turnover, Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are ROE & Working Capital Turnover = µ (There is significant relationship between ROE & Working Capital Turnover, Variance is not Equal) µ (There is significant no relationship between ROE & Working Capital Turnover, Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are ROTA & Working Capital Turnover = µ (There is significant relationship between ROTA & Working Capital Turnover, Variance is not Equal) µ (There is significant no relationship between ROTA & Working Capital Turnover, Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are EPS & Working Capital Turnover = µ (There is significant relationship between EPS & Working Capital Turnover, Variance is not Equal) µ (There is significant no relationship between EPS & Working Capital Turnover, Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are P/E & Working Capital Turnover = µ (There is significant relationship between P/E & Working Capital Turnover, Variance is not Equal) µ (There is significant no relationship between P/E & Working Capital Turnover Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are Vol: I. Issue XCI, August

12 ISSN (Print): ISSN (Online): EXHIBIT 1: T-TEST: TWO-SAMPLE ASSUMING UNEQUAL VARIANCES (LUPIN) ROCE ROE ROTA EPS P/E WC T/O Mean Variance Observations Hypothesized Mean Difference df t Stat P(T<=t) one-tail t Critical one-tail P(T<=t) two-tail t Critical two-tail ROCE & Working Capital Turnover = µ (There is significant relationship between ROCE & Working Capital Turnover, Variance is not Equal) µ (There is significant no relationship between ROCE & Working Capital Turnover, Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are ROE & Working Capital Turnover = µ (There is significant relationship between ROE & Working Capital Turnover, Variance is not Equal) µ (There is significant no relationship between ROE & Working Capital Turnover, Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are ROTA & Working Capital Turnover = µ (There is significant relationship between ROTA & Working Capital Turnover, Variance is not Equal) µ (There is significant no relationship between ROTA & Working Capital Turnover, Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are EPS & Working Capital Turnover = µ (There is significant relationship between EPS & Working Capital Turnover, Variance is not Equal) µ (There is significant no relationship between EPS & Working Capital Turnover, Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are P/E & Working Capital Turnover H 0 : µ 1 = µ (There is significant relationship between P/E & Working Capital Turnover, Variance is not Equal) H 1 : µ 1 µ (There is significant no relationship between P/E & Working Capital Turnover Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are EXHIBIT : T-TEST: TWO-SAMPLE ASSUMING UNEQUAL VARIANCES (CIPLA) ROCE ROE ROTA EPS P/E WC T/O Mean Variance Observations Hypothesized Mean Difference df t Stat P(T<=t) one-tail t Critical one-tail P(T<=t) two-tail t Critical two-tail ROCE & Working Capital Turnover = µ (There is significant relationship between ROCE & Working Capital Turnover, Variance is not Equal) µ (There is significant no relationship between ROCE & Working Capital Turnover, Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are Vol: I. Issue XCI, August 018 1

13 ISSN (Print): ISSN (Online): ROE & Working Capital Turnover = µ (There is significant relationship between ROE & Working Capital Turnover, Variance is not Equal) µ (There is significant no relationship between ROE & Working Capital Turnover, Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are ROTA & Working Capital Turnover = µ (There is significant relationship between ROTA & Working Capital Turnover, Variance is not Equal) µ (There is significant no relationship between ROTA & Working Capital Turnover, Variance is Equal) Here the t Stat value lies between & Therefore, we reject Null Hypothesis stating that the variances are equal. EPS & Working Capital Turnover = µ (There is significant relationship between EPS & Working Capital Turnover, Variance is not Equal) µ (There is significant no relationship between EPS & Working Capital Turnover, Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are P/E & Working Capital Turnover H 0 : µ 1 = µ (There is significant relationship between P/E & Working Capital Turnover, Variance is not Equal) H 1 : µ 1 µ (There is significant no relationship between P/E & Working Capital Turnover Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are EXHIBIT 3: T-TEST: TWO-SAMPLE ASSUMING UNEQUAL VARIANCES (DR REDDY S) ROCE ROE ROTA EPS P/E WC T/O Mean Variance Observations Hypothesized Mean Difference df t Stat P(T<=t) one-tail t Critical one-tail P(T<=t) two-tail t Critical two-tail ROCE & Working Capital Turnover = µ (There is significant relationship between ROCE & Working Capital Turnover, Variance is not Equal) µ (There is significant no relationship between ROCE & Working Capital Turnover, Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are ROE & Working Capital Turnover = µ (There is significant relationship between ROE & Working Capital Turnover, Variance is not Equal) µ (There is significant no relationship between ROE & Working Capital Turnover, Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are ROTA & Working Capital Turnover = µ (There is significant relationship between ROTA & Working Capital Turnover, Variance is not Equal) µ (There is significant no relationship between ROTA & Working Capital Turnover, Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are EPS & Working Capital Turnover = µ (There is significant relationship between EPS & Working Capital Turnover, Variance is not Equal) µ (There is significant no relationship between EPS & Working Capital Turnover, Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are Vol: I. Issue XCI, August

14 ISSN (Print): ISSN (Online): P/E & Working Capital Turnover H 0 : µ 1 = µ (There is significant relationship between P/E & Working Capital Turnover, Variance is not Equal) H 1 : µ 1 µ (There is significant no relationship between P/E & Working Capital Turnover Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are EXHIBIT 4: T-TEST: TWO-SAMPLE ASSUMING UNEQUAL VARIANCES (BIOCON) ROCE ROE ROTA EPS P/E WC T/O Mean Variance Observations Hypothesized Mean Difference df t Stat P(T<=t) one-tail E t Critical one-tail P(T<=t) two-tail t Critical two-tail ROCE & Working Capital Turnover = µ (There is significant relationship between ROCE & Working Capital Turnover, Variance is not Equal) µ (There is significant no relationship between ROCE & Working Capital Turnover, Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are ROE & Working Capital Turnover = µ (There is significant relationship between ROE & Working Capital Turnover, Variance is not Equal) µ (There is significant no relationship between ROE & Working Capital Turnover, Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are ROTA & Working Capital Turnover = µ (There is significant relationship between ROTA & Working Capital Turnover, Variance is not Equal) µ (There is significant no relationship between ROTA & Working Capital Turnover, Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are EPS & Working Capital Turnover = µ (There is significant relationship between EPS & Working Capital Turnover, Variance is not Equal) µ (There is significant no relationship between EPS & Working Capital Turnover, Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are P/E & Working Capital Turnover H 0 : µ 1 = µ (There is significant relationship between P/E & Working Capital Turnover, Variance is not Equal) H 1 : µ 1 µ (There is significant no relationship between P/E & Working Capital Turnover Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are EXHIBIT 5: T-TEST: TWO-SAMPLE ASSUMING UNEQUAL VARIANCES (ABBOTT) ROCE ROE ROTA EPS P/E WC T/O Mean Variance Observations Hypothesized Mean Difference df t Stat P(T<=t) one-tail 3.8E E E t Critical one-tail P(T<=t) two-tail 7.64E-07.86E E t Critical two-tail ROCE & Working Capital Turnover Vol: I. Issue XCI, August

15 ISSN (Print): ISSN (Online): = µ (There is significant relationship between ROCE & Working Capital Turnover, Variance is not Equal) µ (There is significant no relationship between ROCE & Working Capital Turnover, Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are ROE & Working Capital Turnover = µ (There is significant relationship between ROE & Working Capital Turnover, Variance is not Equal) µ (There is significant no relationship between ROE & Working Capital Turnover, Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are ROTA & Working Capital Turnover = µ (There is significant relationship between ROTA & Working Capital Turnover, Variance is not Equal) µ (There is significant no relationship between ROTA & Working Capital Turnover, Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are EPS & Working Capital Turnover = µ (There is significant relationship between EPS & Working Capital Turnover, Variance is not Equal) µ (There is significant no relationship between EPS & Working Capital Turnover, Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are P/E & Working Capital Turnover H 0 : µ 1 = µ (There is significant relationship between P/E & Working Capital Turnover, Variance is not Equal) H 1 : µ 1 µ (There is significant no relationship between P/E & Working Capital Turnover Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are EXHIBIT 6: T-TEST: TWO-SAMPLE ASSUMING UNEQUAL VARIANCES (AUROBINDO PHARM) ROCE ROE ROTA EPS P/E WC T/O Mean Variance Observations Hypothesized Mean Difference df t Stat P(T<=t) one-tail t Critical one-tail P(T<=t) two-tail t Critical two-tail ROCE & Working Capital Turnover = µ (There is significant relationship between ROCE & Working Capital Turnover, Variance is not Equal) µ (There is significant no relationship between ROCE & Working Capital Turnover, Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are ROE & Working Capital Turnover = µ (There is significant relationship between ROE & Working Capital Turnover, Variance is not Equal) µ (There is significant no relationship between ROE & Working Capital Turnover, Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are ROTA & Working Capital Turnover = µ (There is significant relationship between ROTA & Working Capital Turnover, Variance is not Equal) µ (There is significant no relationship between ROTA & Working Capital Turnover, Variance is Equal) Here the t Stat value lies between & Therefore, we reject Null Hypothesis stating that the variances are equal. EPS & Working Capital Turnover = µ (There is significant relationship between EPS & Working Capital Turnover, Variance is not Equal) Vol: I. Issue XCI, August

16 ISSN (Print): ISSN (Online): µ (There is significant no relationship between EPS & Working Capital Turnover, Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are P/E & Working Capital Turnover H 0 : µ 1 = µ (There is significant relationship between P/E & Working Capital Turnover, Variance is not Equal) H 1 : µ 1 µ (There is significant no relationship between P/E & Working Capital Turnover Variance is Equal) Here the t Stat value lies between & Therefore, we reject Null Hypothesis stating that the variances are equal. EXHIBIT 7: T-TEST: TWO-SAMPLE ASSUMING UNEQUAL VARIANCES (CADILA) ROCE ROE ROTA EPS P/E WC T/O Mean Variance Observations Hypothesized Mean Difference df t Stat P(T<=t) one-tail E E-05 t Critical one-tail P(T<=t) two-tail t Critical two-tail ROCE & Working Capital Turnover = µ (There is significant relationship between ROCE & Working Capital Turnover, Variance is not Equal) µ (There is significant no relationship between ROCE & Working Capital Turnover, Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are ROE & Working Capital Turnover = µ (There is significant relationship between ROE & Working Capital Turnover, Variance is not Equal) µ (There is significant no relationship between ROE & Working Capital Turnover, Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are ROTA & Working Capital Turnover = µ (There is significant relationship between ROTA & Working Capital Turnover, Variance is not Equal) µ (There is significant no relationship between ROTA & Working Capital Turnover, Variance is Equal) Here the t Stat value lies between & Therefore, we reject Null Hypothesis stating that the variances are equal. EPS & Working Capital Turnover = µ (There is significant relationship between EPS & Working Capital Turnover, Variance is not Equal) µ (There is significant no relationship between EPS & Working Capital Turnover, Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are P/E & Working Capital Turnover = µ (There is significant relationship between P/E & Working Capital Turnover, Variance is not Equal) µ (There is significant no relationship between P/E & Working Capital Turnover Variance is Equal) Here the t Stat value don t lie between & Therefore, we accept Null Hypothesis stating that the variances are CONCLUSION Working Capital Management is an important component of Corporate Financial Management. Management of working capital is an important activity of a firm. The objective behind working capital management is to ensure continuity in the operations of a firm and ensure that a firm has sufficient fund to manage its daily operations and repay its short term debts in time. The study reveals that: In terms of Margin Ratios: Sun Pharma is in the top position of both Operating Profit and Net Profit In terms of Working Capital: Sun Pharma is in the top position In terms of Liquidity: Abbott is in the top position in terms of Current Ratio, & Sun Pharma in the top position Liquid Ratio Cadila depicted the maximum mean value in terms of Working Capital Turnover Abbott is in the top position, in terms of, Inventory Turnover, Debtors Turnover & Creditors Turnover Ratio Vol: I. Issue XCI, August