A Study of Profitability, Liquidity and Cash Conversion Cycle of Dabur Nepal Private Limited

A Study of Profitability, Liquidity and Cash Conversion Cycle of Dabur Nepal Private Limited Business Research Submitted to: Pokhara University In partial fulfillment of the requirements for the Bachelor of Business Administration - Banking and Insurance By: Dipesh Raj Pandey P.U. Regd. No: 2012-2-45-0029 P.U. Roll No: 13450028 Under the supervision of: Mr. Niranjan Phuyal “Seminar in Working Capital Management” Instructor Ace Institute of Management Kathmandu January 31, 2016 2 DECLARATION This Working Capital Management Report entitled “A Study of Profitability, Liquidity and Cash Conversion Cycle of Dabur Nepal Private Limited” submitted by me, is in partial fulfillment of the requirement for the award of BBA-BI degree of Pokhara University. This report is the result of my original work and it has not been previously submitted to any other university or any other examination(s). Signature ………………………. Name: Dipesh Raj Pandey BBA-BI (Batch): 2012 PU Exam Roll Number: 13450028 Date: 31st January 2016 Ace Institute of Management 3 BONAFIDE CERTIFICATE This is to certify that this report entitled “A Study of Profitability, Liquidity and Cash Conversion Cycle of Dabur Nepal Private Limited” is the Bonafide work of Dipesh Raj Pandey who carried out the Working Capital Management Report work under my supervision. This report is forwarded for examination. ……………………. ……………………… ……………………. Supervisor Program Director External Examiner Ace Institute of Management Ace Institute of Management 4 ACKNOWLEDGEMENT It gives me tremendous pleasure in acknowledging the valuable assistance extended towards me by various personalities in the successful completion of this research. First of all, I would like to express my gratitude to Mr. Niranjan Phuyal (Seminar in Working Capital Management Course Instructor) for entrusting me to conduct the project work. I would like to express my gratitude towards him for providing me with his valuable guidelines, comments, and suggestions which have given me great help while preparing this report. I am indebted to Pokhara University and my college Ace Institute of Management for providing me the opportunity to experience this type of practical and necessary work which will be beneficial in my future career. I would also like to thank the members of Dabur Nepal Private Limited for providing me their audited financial statements. I would also like to take this opportunity to thank all those who have directly or indirectly helped me in the preparation of this research report. Thanking You All Dipesh Raj Pandey 5 ABSTRACT The management of liquidity and cash conversion cycle is considered to be very important issue while making the financial decisions and they usually have an effect on the profitability of the firm. The purpose of this paper is to investigate the relationship that cash conversion cycle and liquidity have with profitability in case of a manufacturing company Dabur Nepal Private Limited (DNPL). This is a case study of DNPL. The audited financial statements of DNPL covering a period of 10 years from 2006-2015 were used for data collection. So, this study is based on secondary data. Profitability has been measured in terms of Return on Assets (ROA), Return on Equity (ROE), and Earnings per Share (EPS). The components of cash conversion cycle (CCC) that have been used are Inventory Conversion Period (ICP), Days Sales Outstanding (DSO) and Payable Deferral Period (PDP). Current Ratio (CR) and Quick ratio (QR) have been used as liquidity ratios. The correlation analysis and regression analysis have been used to find the relation that liquidity ratios, CCC and its components have with profitability. All the findings were tested at 0.10 level of significance. The study results confirm that there is statistically significant relationship of CCC and its components with profitability but there is not statistically significant relation of liquidity and profitability in case of DNPL. 6 LIST OF TABLES Table 1: Financial highlights of DNPL………………………………………….21 Table 2: Overview of financial ratios...………………………………………….10 Table 3: Descriptive Statistics…………………………………………………....11 Table 4: Correlation………………………………………………………………12 Table 5: Model 1…………………………………………………………………13 Table 6: Model 2…………………………………………………………………14 Table 7: Model 3…………………………………………………………………16 7 TABLE OF CONTENTS 1. Introduction………………………………………………………………………………8 1.1 Background of the Study..............................................................................................8 1.2 Problem Statement.......................................................................................................8 1.3 Research Objectives.....................................................................................................9 1.4 Limitations and Suggestions for Further Research......................................................9 2. Literature Review………………………………………………………………………..10 2.1 Relation between Cash Conversion Cycle and Profitability………………………...10 2.2 Relation between Liquidity and Profitability………………………………………..12 3. Research Methodology…………………………………………………………………..13 3.1 Variables of the Study……………………………………………………………….14 4. Results and Discussion…………………………………………………………………..17 4.1 Descriptive Statistics...................................................................................................18 4.2 Correlation...................................................................................................................19 4.3 Regression……………………………………………………………………………20 4.3.1 Model 1............................................................................................................20 4.3.2 Model 2………………………………………………………………………21 4.3.3 Model 3………………………………………………………………………22 5. Conclusion……………………………………………………………………………….24 5.1 Suggestions…………………………………………………………………………..25 References................................................................................................................................26 Annexes....................................................................................................................................28 8 1. INTRODUCTION 1.1 Background of the Study To enhance the performance in terms of profitability, an organization has to give attention in its working capital management. An organization is always exposed to various opportunities and threats. The organization’s positioning in terms of liquidity determines its ability to cope with the market condition and conditioning itself in the market. Managers and organizations both have to realize the value and importance of liquidity and cash conversion cycle for the organization. Since profitability is directly affected by liquidity and cash conversion cycle, they are important issues to address for any organization. Working capital management is very crucial in the profitability of any organization. Too much liquidity can increase cost of operation while less than adequate can put threat on its existence. Similarly, cash conversion cycle is equally important to increase the earnings of the company. Too long conversion cycle leads to less profitability while an ideal conversion cycle can provide with better income opportunities. Thus, this study investigates the relationship of cash conversion cycle and liquidity position with profitability. For this, I have taken Dabur Nepal Private Limited (DNPL) and used its audited financial statements for a period of 10 years from 2006-2015. DNPL is a manufacturing company involved in various fields and sectors such as health care, personal care, food products, home care and consumer health. 1.2 Problem Statement Most researchers have found a negative correlation between cash conversion cycle and profitability but some have found a positive correlation. So, it can be said that researchers have not found a clear cut or exact direction of the relation that exists between profitability of firm and CCC. Similar is the case with liquidity and profitability. Most researchers have found a positive correlation between these two but some have also found a negative correlation. Further, there are not many researches conducted in Nepal on the effects of cash conversion cycle and liquidity on the profitability of manufacturing companies. So, I have carried out this research to find the relationship that liquidity, CCC and its components have with profitability in case of a manufacturing company DNPL. 9 1.3 Objectives of the Study The main objective of this study is to analyze the relationship between cash conversion cycle and profitability of Dabur Nepal Private Limited (DNPL). The other objectives are: i. To find the Return on Assets (ROA), Return on Equity (ROE), Earning per Share (EPS), Net worth per Share (NWPS), Size, Current Ratio (CR), Quick Ratio (QR), Inventory Conversion Period (ICP), Days Sales Outstanding (DSO), Payable Deferral Period (PDP), Cash Conversion Cycle (CCC) of DNPL. ii. To investigate the relationship between profitability position and liquidity position of DNPL. 1.4 Limitations and Suggestions for Further Research Major Limitations: i. Due to time and resource constraints, only Dabur Nepal Private Limited (DNPL) is taken for study. This study is not comparative and doesn’t show the general scenario of total manufacturing industry in Nepal. ii. The study is limited to ten year period. The findings may vary if larger period is taken for study. iii. The study only focuses on CCC and liquidity but there are other many factors as well which affect the profitability of a firm. Suggestions for Further Research: i. Similar studies like this can be done covering a larger number of manufacturing companies in Nepal. ii. A larger time frame can be taken for study. iii. More variables related to liquidity and CCC can be taken to enhance the accuracy of findings. 10 2. LITERATURE REVIEW 2.1 Relation between Cash Conversion Cycle and Profitability Cash conversion cycle equals the length of time between the firm’s expenses (actual cash expenses) to pay for the required and useful resources (materials and labor) and the cash receipts of firm from the sale of its products (that is, the total time required between paying for its productive resources such as materials and labor and collecting its receivables). Deloof (2003) tells that cash conversion cycle is a very useful measure of working capital management of a firm. Cash conversion cycle (CCC) is usually calculated by deducting the accounts payable period (payable deferral period) from the gross operating cycle. Inventory conversion period (conversion period of raw material plus conversion period of work in progress) plus receivable collection period gives the gross operating cycle. CCC is then obtained by deducting the payables deferral period from the gross operating cycle (Muturi, 2015).According to Huynh (2011), purchasing the inventories, paying cash to creditors/suppliers, paying money for manufacturing expenses, marketing and selling the produced goods to the customers, collecting money from them and other similar short-term operating activities of a firm create cash inflows and outflows which are uncertain and unsynchronized. They are uncertain and unsynchronized because receipt of cash from the customers and payment of cash to creditors/suppliers usually does not occur at the same time. In the study “The impact of working capital management on profitability of pharmaceuticals sector in Bangladesh” by Naimulbari (2012), a negative relationship of CCC and profitability was found. As CCC and profitability have negative relationship, the firms should try to make this cycle as short as possible but without hurting their operations. According to this study, short CCC would improve profits because the cost of external financing would be more because of long CCC. The study done by Dong (2010), reports that the working capital management affected profitability and liquidity of a firm. From this research, it is found that firms’ profitability and liquidity are affected by working capital management and the relationship that CCC and profitability have is strongly negative. This denotes that decrease in the profitability occurs due to increase in cash conversion cycle. Anser and Malik (2013) in their study of cash conversion cycle and firms’ profitability of listed manufacturing companies in Pakistan made a research on listed companies from 2007 to 2011. The study found an inverse 11 and significant relationship and linkage of cash conversion cycle with profitability and concluded that cash conversion cycle has an opposite effect on return on assets and return on equity; hence cash conversion cycle of manufacturing firms is negatively related to the profitability of the firm. Owolabi and Alu (2012) in their study of “Effective working capital management and profitability of selected quoted manufacturing companies in Nigeria” carried out an Ex-postfacto research involving trend analysis of five years financial statements of five manufacturing companies using purposive sampling technique. The analysis indicated that there is a significant inverse relationship between some working capital management components (inventory conversion period, debtor collection period, creditor collection period and cash conversion cycle) and profitability of manufacturing companies. They revealed that it is very vital for firms to try as much as possible to manage their operating cycle as it impacts the profits of the company such that, the longer the operating cycle, the more risky and less profitable the firms become. Many studies have shown a negative relationship of CCC with profitability. But there are studies as well which have shown a positive relationship between these two. Panigrahi (2013) in his research article “Cash conversion cycle and firms’ profitability- a study of cement manufacturing companies of India” took into consideration top 5 cement companies in India for a period of 10 years starting from 2001 to 2010. The research showed that the selected companies were having low average rate of return and return on equity with significantly negative cash conversion cycle. The regression results showed that cash conversion cycle is having significantly positive association with both return on assets and equity indicating that it is not necessary that in all cases the lesser cash conversion cycle would result greater profitability measured through return on assets and equity. The study revealed that if the firm is able to sell the inventory and collect the receivables before it pays to the payables, then the situation would be little bit different. As per the study, the quoted firms were not under pressure to reduce the receivable collection period and inventory selling period along with the extension of payment period to increase the profitability. Gill, Biger, and Mathur (2010) studied the relationship of WCM and firm’s profitability by taking a sample of 88 manufacturing companies in America listed on New York Stock Exchange for three years period from 2005-2007 by using experimental and co-relational research design. The findings of this research showed significant positive relation of CCC and profitability of company which implies that higher CCC will lead to higher profitability of the firm 12 2.2 Relation between Liquidity and Profitability The amount of cash that a company can put its hands on quick to settle its liabilities/debts and to meet other demands for cash payment which are not expected is liquidity. Various decisions such as investment decision, finance decision, dividend decision, and liquidity decision have to be taken by financial managers in different times. Finance managers always face dilemma regarding liquidity and profitability but they have to strike a balance between the two for the growth of their firms. Eljelly (2004) examined the profitability and liquidity relationship, as measured by current ratio and cash gap (CCC) in Saudi Arabia using correlation and regression analysis and found that there exists a significant negative relationship between the firm’s profitability and its liquidity level. Nyamao, Patrick, Simeyo, and Nyanyuki (2013) did their study by using the independent variables such as current ratio, quick ratio, inventory turnover ratio, debtor’s turnover ratio, working capital turnover ratio, ratio of current assets to total asset, ratio of current asset to operating income, comprehensive liquidity index, net liquid balance size, leverage and growth while dependent variable (profitability) was measured in terms of return on investment ROI. The authors established a negative association between ROI and the current ratio, cash turnover ratio, current asset to operating income and leverage. On the other hand they established a positive association between ROI and the quick ratio, debtor’s turnover ratio, current asset to total asset and growth rate. Raheman and Nasr (2007) on their study about the effect of different variables of working capital management including average collection period, inventory turnover in days, average payment period, cash conversion cycle, and current ratio on the net operating profitability of Pakistani firms, selected a sample of 94 Pakistani firms listed on Karachi Stock Exchange for a period of six years from 1999-2004 and found a strong negative relationship between liquidity (as measured by current ratio) and profitability of the firm. Mekonnen (2011) also finds that there is a significant negative relationship between liquidity and profitability. Most of the studies have found a negative relation of liquidity and profitability. In contrast, some other studies (Naimulbari, 2012; Azam and Haider, 2011) have found a positive correlation of gross operating profitability with current ratio which shows that the gross operating profitability of a firm increases as its current ratio (liquidity) increases. 13 3. RESEARCH METHODOLOGY This study is based on the secondary data collected from the audited financial statements of Dabur Nepal Private Limited (DNPL) for 10 years period from 2006-2015. The profitability ratios ROA, ROE and EPS are the dependent variables. The main dependent variable is ROA. The independent variables are Net worth per Share (NWPS), Size, Current Ratio (CR), Quick Ratio (QR), Inventory Conversion Period (ICP), Days Sales Outstanding (DSO), Payable Deferral Period (PDP) and Cash Conversion Cycle (CCC). The main independent variables are CR, QR (which show the liquidity position), CCC and its components as the objective of this study is to find the relationship of liquidity and cash conversion cycle with profitability of DNPL. The descriptive statistical tools that have been used to summarize the collected variables are mean, median, standard deviation (S.D.), minimum, maximum and count. Pearson correlation has been used to find the relationship nature between dependent and independent variables. To analyze the extent of dependency of profitability on liquidity position, CCC and its components, multiple regressions have been conducted. The multiple regression models used are: MODEL 1: ROA = b0 + b1CR + b2CCC ROE = b0 + b1CR + b2CCC EPS = b0 + b1CR + b2CCC MODEL 2: ROA = b0 + b1Size + b2QR ROE = b0 + b1Size + b2QR EPS = b0 + b1Size + b2QR MODEL 3: ROA = b0 + b1ICP + b2DSO + b3PDP ROE = b0 + b1ICP + b2DSO + b3PDP EPS = b0 + b1ICP + b2DSO + b3PDP 14 Where, ROA = Return on assets ROE = Return on equity EPS = Earnings per share CR = Current Ratio CCC = Cash Conversion Cycle QR = Quick Ratio ICP = Inventory Conversion Period DSO = Days Sales Outstanding PDP = Payable Deferral Period b0, b1, b2 and b3 are the regression coefficients. After finding the descriptive statistics of the collected variables, correlation and regression of dependent and independent variables of DNBL, the findings have been interpreted and conclusion has been derived. 3.1 Variables of the study: i. ROA = Net Profit / Total Assets ii. ROE = Net Profit / Total Equity (Total Equity = Paid Up Capital + Reserve and Surplus) iii. EPS = Net Profit / Number of shares (Number of shares = Paid up Capital / 100) iv. NWPS = Total Equity / Number of shares v. Size = ln(assets size) (ln = Natural Log) vi. Current Ratio (CR) = Current Assets / Current Liabilities (This ratio measures the short term liquidity position of a firm indicating the amount of current assets available per unit of current liabilities. The higher the ratio the more will be the firm's ability to meet short term obligations and the greater will be the safety of funds of short term creditors. It is worthwhile to note that a very high 15 current ratio may not be indicative of good liquidity position but may be the signal of excessive inventories over the current requirements, inefficiency in collection of debtors and high cash and bank balances without proper investment. Conventionally, a current ratio of 2:1 is taken as satisfactory. However, this satisfactory norm may differ depending on the country's economic conditions, nature of industry, management pattern and other factors of a particular firm under an industry. Therefore, satisfactory current ratio should be developed by a firm on the basis of its past experiences and be considered as standard.) vii. Quick Ratio (QR) = Quick Assets / Current Liabilities (Quick assets refer to those current assets which can be converted into cash/bank immediately or at a short notice without suffering any loss. It actually means the current assets excluding inventories and prepaid expenses. The logic behind the exclusion of inventory and prepaid expenses is that these two assets are not easily and readily convertible into cash. This ratio measures the quick short-term solvency position of a firm. A high quick ratio indicates that the quick short term solvency position of a firm is good. Generally, a quick ratio of 1:1 is considered satisfactory for a firm though it depends on many factors. Quick ratio is a more rigorous and penetrating test of the liquidity position of an organization as compared to the current ratio of the firm.) viii. Inventory conversion period (ICP) = Inventory * 360 / Cost of Goods Sold (This addresses the question of how many days it takes to sell the entire inventory. The smaller this number is, the better.) ix. Days Sales Outstanding (DSO) = Debtors * 360 / Sales (This looks at the number of days needed to collect on sales and involves Accounts Receivables. While cash-only sales have a DSO of zero, people do use credit extended by the company, so this number will be positive. Again, smaller is better.) x. Payable Deferral Period (PDP) = Creditors * 360 / Cost of Goods Sold (This involves the company's payment of its own bills or Accounts Payables. If this can be maximized, the company holds onto cash longer, maximizing its investment potential; therefore, a longer PDP is better.) xi. Cash Conversion Cycle (CCC) = ICP + DSO - PDP 16 (The cash conversion cycle (CCC or Operating Cycle) is the length of time between a firm's purchase of inventory and the receipt of cash from accounts receivable. It is the time required for a business to turn purchases into cash receipts from customers. CCC represents the number of days a firm's cash remains tied up within the operations of the business. A cash flow analysis using CCC also reveals in, an overall manner, how efficiently the company is managing its working capital.) 17 4. RESULTS AND DISCUSSION The table below is the overview of the financial ratios over the ten years period for DNPL: Table 2: Overview of financial ratios Year ROA ROE EPS NWPS Size Current Quick Ratio Ratio ICP DSO PDP CCC 2006 3.32% 7.83% 97.12 1241.06 10.06 1.77 0.93 114.63 24.89 37.66 101.85 2007 3.21% 7.96% 106.79 1342.06 10.19 2.43 1.53 97.47 49.60 58.19 88.89 2008 2.18% 6.17% 88.33 1431.77 10.39 1.53 0.79 124.89 49.33 114.77 59.45 2009 0.08% 0.20% 2.96 1494.68 10.30 1.52 0.76 98.46 54.47 120.95 31.98 2010 5.24% 11.33% 189.15 1668.90 10.27 2.32 0.87 123.91 17.13 70.50 70.54 2011 4.72% 13.05% 232.80 1783.36 10.58 1.18 0.45 158.89 28.76 132.85 54.80 2012 0.93% 2.83% 51.95 1835.31 10.70 1.15 0.59 108.71 38.90 108.41 39.20 2013 6.69% 19.17% 435.23 2270.54 10.86 1.06 0.50 131.36 40.14 44.08 127.42 2014 7.86% 19.52% 592.10 3032.63 11.00 1.19 0.65 117.34 54.15 55.87 115.62 2015 8.98% 24.28% 899.06 3702.87 11.29 1.07 0.68 90.61 62.80 60.24 93.18 The Return on assets (an overall measure of profitability) has decreased significantly in 2009 (0.08%) and 2012 (0.93%) but after 2012 ROA is increasing at a good rate. The same is the case with ROE and EPS. NWPS has increased from 1241.06 in 2011 to 3702.87 in 2015. The size of the company gradually has come at 11.29 in 2015 from 10.06 in 2011.The current ratio of DNPL is mostly in decreasing trend (except 2007 ,2010, 2014) and quick ratio is fluctuating (sometimes increasing and sometimes decreasing) in the years of observation. DNPL has been able to decrease the number of days needed to sell its inventory from 114.63 days in 2006 to 90.61 days in 2015 which is a good sign. But DSO is in increasing trend. DSO has increased to 62.80 days in 2015 from 24.89 days in 2006 which indicates that the days needed to collect accounts receivables of DNPL is increasing and that is not a good sign. PDP has increased to 60.24 days in 2015 from 37.66 days in 2006 which is a good sign as it indicates that DNPL is holding onto cash longer and maximizing its investment potential. The CCC of DNPL has decreased in 2007, 2008, 2009; increased in 2010; decreased in 2011, 2012; increased in 2013, 2014 and decreased in 2015. The CCC has decreased from 101.85 days in 2006 to 93.18 days in 2015. 18 4.1 Descriptive Statistics The following table shows the descriptive statistics of the collected variables of DNPL: Table 3: Descriptive Statistics Current Quick Ratio Ratio ROA ROE EPS NWPS Size ICP DSO PDP CCC Mean 4.32% 11.23% 269.55 1980.32 10.56 1.52 0.78 116.63 42.02 80.35 78.29 Median 4.02% 9.65% 147.97 1726.13 10.48 1.36 0.72 115.99 44.73 65.37 79.72 Deviation 2.93% 7.79% 287.34 803.20 0.40 0.50 0.31 19.90 14.71 35.13 32.21 Range 8.90% 24.08% 896.10 2461.81 1.23 1.36 1.08 68.28 45.67 95.19 95.43 Minimum 0.08% 0.20% 2.96 1241.06 10.06 1.06 0.45 90.61 17.13 37.66 31.98 Maximum 8.98% 24.28% 899.06 3702.87 11.29 2.43 1.53 158.89 62.80 132.85 127.42 10 10 10 10 10 10 10 10 10 10 10 Standard Count The mean ROA is 4.32%, mean ROE is 11.23%, mean EPS is 269.55, mean NWPS is 1980.32, mean Size is 10.56, mean CR is 1.52, mean QR is 0.78, mean ICP is 116.63 days, mean DSO is 42.02 days, mean PDP is 80.35 days and mean CCC is 78.29 days. The mean CR of DNPL is 1.52. So, it can be said that DNPL holds more current assets than current liabilities but its CR doesn’t fall under the standard norm of 2:1. The mean QR is 0.78 which shows that DNPL holds less quick assets than current liabilities and doesn’t focus on its liquidity position. The mean CCC of 78.29 days shows that DNPL requires around 78.29 days for turning purchases into cash receipts from customers. The minimum ROA is 0.08% (in 2009 when profit was 23.65 lakhs) and maximum ROA is 8.98% (in 2015 when profit was 7179.2 lakhs). ROA has standard deviation of 2.93%. Similarly, ROE and EPS were minimum in 2009 and maximum in 2015. ROE has standard deviation of 7.79%. EPS has very high standard deviation of 287.34 because of less profit in 2009, 2012 and higher profit in other years. The maximum EPS is 899.06 (2015), minimum EPS is 2.96 (2009) and second minimum EPS is 51.95 (2012) due to which standard deviation of EPS is very high. Similarly NWPS has a very high standard deviation of 803.20 because NWPS is significantly 19 increasing in every year of observation. The standard deviation of size is 0.40, current ratio is 0.50, quick ratio is 0.31, ICP is 19.90, DSO is 14.71, PDP is 35.13 and CCC is 32.21. 4.2 Correlation Table 4: Correlation ROA ROE EPS NWPS Size ROA 1 ROE 0.9891 1 EPS 0.9230 0.9404 1 NWPS 0.8150 0.8364 0.9609 1 Size 0.7019 0.7585 0.8703 0.9453 1 -0.2861 -0.3845 -0.4837 -0.5912 -0.7612 Current Quick Ratio Ratio ICP DSO PDP CCC Current Ratio 1 Quick -0.2422 -0.3026 -0.3188 -0.4150 -0.5755 0.8547 1 ICP 0.0943 0.1117 -0.1421 -0.2171 -0.0741 -0.2057 -0.5024 1 DSO 0.1492 0.2090 0.4450 0.5323 0.5302 -0.3584 0.0619 -0.5897 1 PDP -0.5702 -0.5275 -0.4392 -0.2960 -0.1222 -0.2035 -0.3223 0.3103 0.0291 1 CCC 0.7484 0.7399 0.5945 0.4318 0.3296 -0.0688 0.0693 0.0102 0.0605 -0.8857 Ratio There is high positive correlation between ROA and ROE (0.9891), ROA and EPS (0.9385), ROE and EPS (0.9230) as they are the profitability ratios. Similarly, NWPS and EPS have high positive correlation (0.9609). CR has a negative correlation with profitability ratios ROA (-0.2861), ROE (-0.3845) and EPS (0.4837). CR increases when the amount of current assets increases / amount of current liabilities decreases. So, this negative correlation between CR and profitability ratios shows that profit of DNPL will increase if it holds fewer current assets and increases its investment. Another liquidity ratio QR also has a negative correlation with profitability ratios ROA (-0.2422), ROE (0.3026) and EPS (-0.3188) which indicates that profit of DNPL will increase if it holds fewer quick assets (current assets excluding inventory and prepaid expenses). So, in case of DNPL, the correlation analysis contradicted the common belief that maintaining good liquidity position increases profitability. 1 20 ICP has a positive correlation with ROA (0.0943), ROE (0.1117) and negative correlation with EPS (-0.1421). DSO has a positive correlation with ROA (0.1492) and other profitability ratios which shows that profit of DNPL will increase if it gives more days to its debtors to pay their bills. PDP has a negative correlation with ROA (-0.5702) and other profitability ratios which shows that DNPL can increase its profits if it pays its creditors from whom it has purchased inventory and other goods quicker. Finally, CCC has a positive correlation with all profitability ratios ROA (0.7484), ROE (0.7399) and EPS (0.5945) which indicates that profit of DNPL will increase if it gives more time to its customers to pay their bills. The reason for this can be that the customers of DNPL might be more satisfied if they get more time to pay their bills and this will help to increase the sales volume and ultimately sales revenue of DNPL. 4.3 Regression Since this is not a scientific research, 10% (0.10) is taken as the significance level. 4.3.1 Model 1: ROA = b0 + b1CR + b2CCC ROE = b0 + b1CR + b2CCC EPS = b0 + b1CR + b2CCC Table 5: Model 1 MODEL 1 Sig F Adjusted R2 0.0169 0.0353 0.5055 0.1734 0.0142 0.0231 0.5619 0.1234 0.0619 0.0609 0.4219 Dependent CR CCC Variables p-value p-value ROA 0.3492 ROE EPS For ROA, we can see that Adjusted R2= 0.5055 which indicates that 50.55% of change in ROA is due to change in CR and CCC. For independent variable CR, p-value is 0.3492 which is greater than 0.10 which indicates that CR is not significantly associated with ROA. But CCC is 21 significantly associated with ROA as its p-value is 0.0169 which is less than 0.10. Similarly, this overall model is significant as Significance F-value is 0.0353 which is less than α-value 0.10. For ROE, we can see that Adjusted R2= 0.5619 which indicates that 56.19% of change in ROE is due to change in CR and CCC. For independent variable CR, p-value is 0.1734 which is greater than 0.10 which indicates that CR is not significantly associated with ROE. But CCC is significantly associated with ROE as its p-value is 0.0142 which is less than 0.10. Similarly, this overall model is significant as Significance F-value is 0.0353 which is less than α-value 0.10. For EPS, we can see that Adjusted R2= 0.4219 which indicates that 42.19% of change in EPS is due to change in CR and CCC. For independent variable CR, p-value is 0.1234 which is greater than 0.10 which indicates that CR is not significantly associated with EPS. But CCC is significantly associated with EPS as its p-value is 0.0619 which is less than 0.10. Similarly, this overall model is significant as Significance F-value is 0.0609 which is less than α-value 0.10. So, CCC is significantly associated with all three profitability ratios (ROA, ROE and EPS) but CR is not significantly associated with any of the three profitability ratios. 4.3.2 Model 2: ROA = b0 + b1Size + b2QR ROE = b0 + b1Size + b2QR EPS = b0 + b1Size + b2QR Table 6: Model 2 MODEL 2 Sig F Adjusted R2 0.4692 0.0702 0.3981 0.0200 0.5143 0.0397 0.4884 0.0015 0.2221 0.0032 0.7517 Dependent Size QR Variables p-value p-value ROA 0.0325 ROE EPS 22 For ROA, we can see that Adjusted R2= 0.3981 which indicates that 39.81% of change in ROA is due to change in Size and QR. For independent variable Size, p-value is 0.0325 which is less than 0.10 which indicates that Size is significantly associated with ROA. But QR is not significantly associated with ROA as its p-value is 0.4692 which is greater than 0.10. This overall model is significant as Significance F-value is 0.0702 which is less than α-value 0.10. For ROE, we can see that Adjusted R2= 0.4884 which indicates that 48.84% of change in ROE is due to change in Size and QR. For independent variable Size, p-value is 0.0200 which is less than 0.10 which indicates that Size is significantly associated with ROE. But QR is not significantly associated with ROE as its p-value is 0.5143 which is greater than 0.10. This overall model is significant as Significance F-value is 0.0397 which is less than α-value 0.10. For EPS, we can see that Adjusted R2= 0.7517 which indicates that 75.17% of change in EPS is due to change in Size and QR. For independent variable Size, p-value is 0.0015 which is less than 0.10 which indicates that Size is significantly associated with EPS. But QR is not significantly associated with EPS as its p-value is 0.2221 which is greater than 0.10. This overall model is significant as Significance F-value is 0.0032 which is less than α-value 0.10. So, Size is significantly associated with all three profitability ratios (ROA, ROE and EPS) but QR is not significantly associated with any of the three profitability ratios. It has been found that the profitability of DNPL doesn’t depend on its liquidity position as both the liquidity ratios CR and QR didn’t show statistically significant relationship with ROA, ROE and EPS. 4.3.3 Model 3: ROA = b0 + b1ICP + b2DSO + b3PDP ROE = b0 + b1ICP + b2DSO + b3PDP EPS = b0 + b1ICP + b2DSO + b3PDP 23 Table 7: Model 3 MODEL 3 Sig F Adjusted R2 0.0295 0.1131 0.4071 0.0771 0.0274 0.0892 0.4554 0.0804 0.0905 0.1771 0.3024 Dependent ICP DSO PDP Variables p-value p-value p-value ROA 0.0982 0.1336 ROE 0.0648 EPS 0.2392 For ROA, we can see that Adjusted R2= 0.4071 which indicates that 40.71% of change in ROA is due to change in ICP, DSO and PDP. For independent variable ICP, p-value is 0.0982 which is less than 0.10 which indicates that ICP is significantly associated with ROA. But DSO is not significantly associated with ROA as its p-value is 0.1336 which is greater than 0.10. Like ICP, PDP is also significantly associated with ROA as its p-value is 0.0295. This overall model is not significant as Significance F-value is 0.1131 which is greater than α-value 0.10. For ROE, we can see that Adjusted R2= 0.4554 which indicates that 45.54% of change in ROE is due to change in ICP, DSO and PDP. For independent variable ICP, p-value is 0.0648 which is less than 0.10 which indicates that ICP is significantly associated with ROE. DSO is also significantly associated with ROE as its p-value is 0.0771 which is less than 0.10. PDP is also significantly associated with ROE as its p-value is 0.0274 which is less than 0.10. This overall model is significant as Significance F-value is 0.0892 which is less than α-value 0.10. For EPS, we can see that Adjusted R2= 0.3024 which indicates that 30.24% of change in EPS is due to change in ICP, DSO and PDP. For independent variable ICP, p-value is 0.2392 which is greater than 0.10 which indicates that ICP is not significantly associated with EPS. But DSO is significantly associated with EPS as its p-value is 0.0804 which is less than 0.10. Like DSO, PDP is also significantly associated with EPS as its p-value is 0.0905 which is less than 0.10. This overall model is not significant as Significance F-value is 0.1771 which is greater than αvalue 0.10. So, ICP and PDP are significantly associated with ROA. All the three components of CCC (ICP, DSO, and PDP) are significantly associated with ROE. DSO and PDP are significantly associated with EPS. 24 5. CONCLUSION The correlation analysis showed negative correlation of current ratio (CR) and quick ratio (QR) with profitability ratios ROA, ROE and EPS which indicated that profit of DNPL will increase if it holds fewer current assets and quick assets. This is contrary to most of the previous findings which suggest that profit of companies increase if they hold more current assets and maintain their liquidity position. This suggests that DNPL can hold less liquid assets and increase its investment for increasing its profit. The regression analysis didn’t show statistically significant relationship between liquidity (CR, QR) and profitability (ROA, ROE, and EPS) in case of DNPL. This shows that profitability of DNPL is not significantly related with its liquidity. ICP had positive correlation with ROA and ROE which showed that ROA and ROE of DNPL increases if it holds on to its inventory longer. This doesn’t match with the findings of most of the previous studies which tell that profit increases if a company can sell its inventory quicker and this positive relation might be due to the trust that the creditors of DNPL have on it. ICP had negative correlation with EPS which showed that EPS of DNPL increases if it sells its inventory quicker. DSO had positive correlation with all the profitability ratios which showed that profit of DNPL increases if it gives more days to its customers to pay their debts which also doesn’t match with the findings of most of the previous studies which tell that profit increases if a company can collect its account receivable quicker. This positive relation might be due to increased satisfaction of its customers after giving more days to them to pay their debts which will increase the sales volume and sales revenue of DNPL. Another component of CCC, PDP had negative correlation with all the profitability ratios which showed that profit of DNPL increases if it pays its creditors quicker. This too doesn’t match with the findings of most of the previous studies which tell that a company can maximize its profit and investment potential if it can maximize its time for paying account payables. The regression analysis showed statistically significant relationship of ICP with ROA and ROE, statistically significant relationship of DSO with all the three profitability ratios and statistically significant relationship of PDP with ROE and EPS. In case of CCC, the correlation analysis showed positive correlation of CCC with profitability ratios which indicated that profit of DNPL will increase if it gives more time to customers to pay their bills and vice versa. This again indicates that DNPL has good relation with its creditors and 25 can delay the payment to its creditors and increase its profit by satisfying its customers by giving more time to them to pay their bills. The findings of the correlation analysis in CCC are also contrary to common belief that short CCC is better than long CCC. The regression analysis showed that ROA, ROE and EPS are significantly affected by CCC in case of DNPL. So, it can be concluded that there is statistically significant positive relation between CCC and profitability, statistically significant relation between components of CCC and profitability but no statistically significant relationship between liquidity and profitability in case of DNPL. 5.1 Suggestions In all the cases, there might not be negative relation of CCC and profitability and positive relation of liquidity and profitability. This depends on the company. For example, due to negative CCC, customers may not be happy as they don’t have the facility of reasonable credit term which might affect sales revenue and profit. Similarly, if a company maintains excessive liquidity and holds a lot of current assets, it might not earn enough profit as its investment will be low. The findings of this research recommend that the factory managers should strike a balance of CCC (neither very short nor very long) and liquidity (the firm should not hold either excessive current assets or less current assets) for profitability. 26 REFERENCES Anser, R. & Malik, Q.A. (2013). Cash conversion cycle and firms’ profitability- A study of listed manufacturing companies of Pakistan. IOSR Journal of Business and Management, 8(2), 83-87. Azam, M. & Haider, S. I. (2011). Impact of working capital management on firms’ performance: Evidence from non-financial institutions of KSE-30 index. Interdisciplinary Journal of Contemporary Research in Business, 3(5), 481 - 492. Dabur Private Limited (2016). Dabur Annual Reports. Retrieved From http://www.dabur.com/Investors%20Relation-Reports Deloof, M. (2003). Does working capital management affect profitability of Belgian firms? Journal of Business Finance & Accounting, 5230, 573–587. Dong H. P. (2010). The relationship between working capital management and profitability. International Research Journal of Finance and Economic, Issue-49. Eljelly, A. (2004). Liquidity-profitability trade-off: an empirical investigation in an emerging market. International Journal of Commerce and Management, 14(2), 48-61. Gill, A., Biger, N., & Mathur, N. (2010). The relationship between working capital management and profitability: Evidence from the United States. Business and Economics Journal, 10, 1-9. Huynh, N. (2011). The influence of working capital management on profitability of listed companies in the Netherlands. University of Twente, s1062980. Mekonnen, M. (2011). The impact of working capital management on firms’ profitability. Unpublished Master Thesis, Addis Ababa University: Ethiopia. Muturi, H.M. (2015). Effects of cash conversion cycle on profitability of tea factories in Meru county, Kenya. International Journal of Economics, Commerce and Management, 3(6), 552-560. Naimulbari, K. (2012). “The impact of working capital management on profitability” of pharmaceuticals sector in Bangladesh. Unpublished undergraduate, research work: Independent university, Bangladesh. 27 Nyamao, N.R., Patrick, O., Simeyo, O. & Nyanyuki, N.F. (2013). An empirical analysis of the liquidity, solvency and financial health of small and medium sized enterprises in Kisii municipality, Kenya. European Journal of Business and Management, 5(8), 2222-2839. Owolabi, S.A. and Alu, C.N. (2012). Effective working capital management and profitabilty: A study of selected quoted manufacturing companies in Nigeria. Economics and Finance Review, 2(6), 55-67. Panigrahi, A.K. (2013). Cash conversion cycle and firms’ profitability- A study of cement manufacturing companies of India. International Journal of current Research, 5 (6), 1484-1488. Raheman, A. & Nasr, M. (2007). Working capital management and profitability – case of Pakistani firms. International Review of Business Research Papers, 3(1), 279-300. 28 ANNEXES Table 1: Financial highlights of DNPL ITEMS 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Net Profit/Loss 775.5 852.7 705.3 23.65 1510.4 1858.98 414.83 3475.38 4728 7179.2 23351.89 26603.65 32407.06 29730.43 28817.6 39354.3 44483.09 51926.03 60132.8 79934.4 798.52 798.52 798.52 798.52 798.52 798.52 798.52 798.52 798.52 798.52 Reserve+Surplus 9111.58 9918.08 10634.48 11136.82 12528 13441.97 13856.8 17332.18 23417.6 28769.6 Inventories 7062.56 6977.87 9878.98 8827.17 10928 16024.02 15062.33 18563.14 19705.6 16400 Sold 22180.51 25771.58 28476.16 32275.02 31748.8 36305.67 49879.41 50873.08 60456 65158.4 Sales 31040.46 35181.33 39159.92 43519.23 44243.2 52306.67 68542.07 73752.01 86569.6 94009.6 Debtors 2145.73 4847.33 5365.6 6585.28 2105.6 4178.73 7406.83 8222.89 13020.8 16400 Creditors 2320.35 4165.39 9078.46 10843.46 6217.6 13397.69 15020.78 6229 9382.4 10902.4 14919.06 18840.1 20518.94 17644.02 17502.4 25973.73 31020.12 34781.69 43320 45123.2 8432.83 7763.55 13396.46 11575.39 7558.4 21981.49 27007.48 32719.84 36336 42288 Total Assets Paid up Capital Cost of Goods Current Assets Current Liabilities (Note: Figures are in lakhs) 29 Regression Model: ROA = b0 + b1CR + b2CCC SUMMARY OUTPUT Regression Statistics Multiple R 0.7845 R Square 0.6154 Adjusted R Square 0.5055 Standard Error 0.0206 Observations 10 ANOVA Significance df SS MS F F Regression 2 0.0048 0.0024 5.6007 0.0353 Residual 7 0.0030 0.0004 Total 9 0.0077 Standard P- Upper Coefficients Error t Stat value Lower 95% 95% Intercept 0.0119 0.0283 0.4207 0.6866 -0.0550 0.0788 Current Ratio -0.0137 0.0136 -1.0033 0.3492 -0.0459 0.0186 CCC 0.0007 0.0002 3.1163 0.0169 0.0002 0.0012 30 Regression Model: ROE = b0 + b1CR + b2CCC SUMMARY OUTPUT Regression Statistics Multiple R 0.8119 R Square 0.6592 Adjusted R Square 0.5619 Standard Error 0.0515 Observations 10 ANOVA Significance df SS MS F F Regression 2 0.0360 0.0180 6.7713 0.0231 Residual 7 0.0186 0.0027 Total 9 0.0546 Standard Upper Coefficients Error t Stat P-value Lower 95% 95% Intercept 0.0554 0.0708 0.7820 0.4599 -0.1121 0.2228 Current Ratio -0.0517 0.0341 -1.5155 0.1734 -0.1324 0.0290 CCC 0.0017 0.0005 3.2412 0.0142 0.0005 0.0030 31 Regression Model: EPS = b0 + b1CR + b2CCC SUMMARY OUTPUT Regression Statistics Multiple R 0.7419 R Square 0.5504 Adjusted R Square Standard Error 0.4219 218.4677 Observations 10 ANOVA Significance df SS MS F F Regression 2 408976.1 204488 4.284435 0.0609 Residual 7 334096.8 47728.12 Total 9 743072.9 Standard Upper Coefficients Error t Stat P-value Lower 95% 95% Intercept 261.2342 300.2028 0.8702 0.4130 -448.6326 971.1011 Current Ratio -253.3353 144.6695 -1.7511 0.1234 -595.4244 88.7538 5.0309 2.2665 2.2197 0.0619 -0.3286 10.3903 CCC 32 Regression Model: ROA = b0 + b1Size + b2QR SUMMARY OUTPUT Regression Statistics Multiple R 0.7293 R Square 0.5319 Adjusted R Square 0.3981 Standard Error 0.0227 Observations 10 ANOVA Significance df SS MS F F Regression 2 0.0041 0.0021 3.9767 0.0702 Residual 7 0.0036 0.0005 Total 9 0.0077 Standard P- Upper Coefficients Error t Stat value Lower 95% 95% Intercept -0.6323 0.2615 -2.4179 0.0462 -1.2507 -0.0139 Size 0.0622 0.0234 2.6602 0.0325 0.0069 0.1176 Quick Ratio 0.0232 0.0303 0.7651 0.4692 -0.0484 0.0948 33 Regression Model: ROE = b0 + b1Size + b2QR SUMMARY OUTPUT Regression Statistics Multiple R 0.7760 R Square 0.6021 Adjusted R Square 0.4884 Standard Error 0.0557 Observations 10 ANOVA Significance df SS MS F F Regression 2 0.0329 0.0164 5.2967 0.0397 Residual 7 0.0217 0.0031 Total 9 0.0546 Standard P- Upper Coefficients Error t Stat value Lower 95% 95% Intercept -1.7427 0.6409 -2.7193 0.0298 -3.2582 -0.2273 Size 0.1719 0.0573 2.9971 0.0200 0.0363 0.3075 Quick Ratio 0.0510 0.0742 0.6868 0.5143 -0.1245 0.2265 34 Regression Model: EPS = b0 + b1Size + b2QR SUMMARY OUTPUT Regression Statistics Multiple R 0.8983 R Square 0.8069 Adjusted R Square Standard Error 0.7517 143.1664 Observations 10 ANOVA Significance df SS MS F F Regression 2 599596.5187 299798.3 14.62672 0.0032 Residual 7 143476.357 20496.62 Total 9 743072.8758 Standard Upper Coefficients Error t Stat -7803.4502 1647.5597 -4.7364 Size 745.4564 147.4247 Quick Ratio 255.7343 190.8213 Intercept P-value Lower 95% 95% 0.0021 -11699.3098 -3907.591 5.0565 0.0015 396.8523 1094.0604 1.3402 0.2221 -195.4862 706.9549 35 Regression Model: ROA = b0 + b1ICP + b2DSO + b3PDP SUMMARY OUTPUT Regression Statistics Multiple R 0.7776 R Square 0.6047 Adjusted R Square 0.4071 Standard Error 0.0226 Observations 10 ANOVA Significance df SS MS F F Regression 3 0.0047 0.0016 3.0599 0.1131 Residual 6 0.0031 0.0005 Total 9 0.0077 Standard P- Upper Coefficients Error t Stat value Lower 95% 95% Intercept -0.0680 0.0749 -0.9080 0.3989 -0.2513 0.1153 ICP 0.0010 0.0005 1.9561 0.0982 -0.0003 0.0023 DSO 0.0011 0.0007 1.7342 0.1336 -0.0005 0.0028 PDP -0.0007 0.0002 -2.8406 0.0295 -0.0012 -0.0001 36 Regression Model: ROE = b0 + b1ICP + b2DSO + b3PDP SUMMARY OUTPUT Regression Statistics Multiple R 0.7981 R Square 0.6369 Adjusted R Square 0.4554 Standard Error 0.0575 Observations 10 ANOVA Significance df SS MS F F Regression 3 0.0348 0.0116 3.5085 0.0892 Residual 6 0.0198 0.0033 Total 9 0.0546 Standard Upper Coefficients Error t Stat P-value Lower 95% 95% Intercept -0.2420 0.1909 -1.2677 0.2519 -0.7090 0.2251 ICP 0.0029 0.0013 2.2570 0.0648 -0.0002 0.0061 DSO 0.0036 0.0017 2.1306 0.0771 -0.0005 0.0077 PDP -0.0017 0.0006 -2.8992 0.0274 -0.0032 -0.0003 37 Regression Model: EPS = b0 + b1ICP + b2DSO + b3PDP SUMMARY OUTPUT Regression Statistics Multiple R 0.7314 R Square 0.5350 Adjusted R Square Standard Error 0.3024 239.9858 Observations 10 ANOVA Significance df SS MS Regression 3 397513.6923 132504.6 Residual 6 345559.1835 Total 9 743072.8758 F F 2.3007 0.1771 57593.2 Standard Upper Coefficients Error t Stat P-value Lower 95% 95% -774.8519 797.1956 -0.9720 0.3686 -2725.5193 1175.8155 ICP 7.1130 5.4443 1.3065 0.2392 -6.2087 20.4347 DSO 14.7185 7.0069 2.1006 0.0804 -2.4268 31.8638 PDP -5.0230 2.4924 -2.0153 0.0905 -11.1218 1.0758 Intercept