IMPACT OF DPS ON MPS: A STUDY ON LEADING INDIAN CEMENT COMPANIES

DOI: 10.1917/ijms.018.0111 IMPACT OF DPS ON MPS: A STUDY ON LEADING INDIAN CEMENT COMPANIES Sri Ayan Chakraborty Faculty of Management, Institute of Computer Accountants, Kolkata, India Abstract Risks and uncertainties are inherent in every organisation. Different class of investors in do not shoulder the same degree of risk. An investor in bonds earns return in from interest while shareholders depend on dividends, stock price appreciation. Dividend refers to the distribution of profit among the shareholders. Profit earned by a company can be retained for future usage, or distributed in form of dividend or both. Dividend decision is one of the important decisions, since it determines the amount of profit to be distributed among shareholders and the amount to be retained earnings for future investment purpose. This is known as Dividend Policy. The main objective of every company is to maximize shareholders wealth rather than profit. Shareholders gain both from Dividend as well as Capital Appreciation. Moreover, dividend policy of a company has an impact on its market price. Market price increases only if a company provides stable return to its shareholders. This paper focuses on the impact of dividend on Market Price of a company. Keywords: Indian Cement Sector, Net Profit Margin, Dividend Per Share, Dividend Yield, Earnings Per Share, Market Price Per Share, Price Earnings Ratio 1. INTRODUCTION Indian Cement Industry has the second largest market in the world after China with production of 79.81 million tons per annum. The Cement Industry comprises of 10 large and 365 mini cement plants. Cement is a cyclical commodity with a high correlation with GDP. The demand for cement in real estate sector is spread across rural housing (40%), urban housing (5%) and construction/infrastructure/industrial activities (5%). While the rest 10% demand is contributed by commercial real estate sector. The growth in the Real Estate sector has played a positive role behind the development in the Cement Sector. Cement demand is expected to reach 550 to 600 Million Tonnes Per Annum (MTPA) by 05 [1] []. Ultratech Cement: Headquartered in Mumbai, Ultra-Tech Cement Ltd was founded in 1983. It has a production capacity of 93 million tonnes per annum (MTPA) of grey cement. It operates across India, Bangladesh, Bahrain, UAE, and Sri Lanka. For white cement segment, it adopts the brand name of Birla White. ACC: Headquartered in Mumbai, Associated Cement Companies Limited was founded in 1936. It is the second largest Indian cement company with annual production capacity of 33.4 million tonnes. It operates with more than 40 ready mix concrete plants, 1 sales offices, and several zonal offices. Ambuja Cement: Headquartered in Mumbai, Ambuja Cements Ltd was founded in 1983 and stated its production in 1986. It is the third largest Indian cement company with annual production capacity of 9.65 million tonnes. It has 5 integrated cement manufacturing plants and 8 cement grinding units. Shree Cements: Headquartered in Kolkata, Shree Cements was founded in1979 in Bewar in Ajmer district of Rajasthan. It is the fourth largest Indian cement company with annual production capacity of 13.5 million tonnes. It has 6 cement manufacturing plants located at Beawar, Ras, Khushkhera, Jaipur, Rajasthan and Uttarakhand. Ramco Cement: Headquartered in Chennai Ramco was founded in 1984. It is the fifth largest Indian cement company with annual production capacity of 16.45 million tonnes. It has 8 manufacturing plants including grinding unit. It also produces Ready Mix Concrete and Dry Mortar products. India Cements: Headquartered in Tirunelveli, The India Cements Limited was founded in 1946. It is the sixth largest Indian cement company with annual production capacity of 15.5 million tonnes. It manufactures cement for various applications, including, precast concrete items, concrete components, and multi-storey buildings, as well as runways, concrete roads, bridges and for general-purpose use. Prism Cement: Prism Cement Limited is India s 8th leading integrated Building Materials Company, with a wide range of products from cement, ready-mixed concrete, tiles, and bath products to kitchens. The company has three Divisions Prism Cement, H and R Johnson (India), and RMC Readymix (India). Binani Cement: Headquartered in Mumbai, Binani was founded in the year 187. It is the seventh largest Indian cement company with annual production capacity of 11.5 million tonnes. It has integrated plants, one in India and another in China, and grinding units in Dubai. Birla Corp: M.P Birla is one of the top Industrial groups in India. It offers wide range of products including auto interiors, cables, jute, cement etc. The group include companies like Vindhya Telelinks Ltd, Universal-ABB Power Cables Ltd, Universal Cables Ltd, Hindustan Gum and Chemicals Ltd etc. JK Cement: Headquartered in Mumbai, J.K Cement Ltd was founded by Lala Kamlapat Singhania. It is one of the top manufacturers of white cement in India. It has 3 cement production plants located in Karnataka, Andhra Pradesh, and Maharashtra. It produces types of cements namely Portland Slag Cement, Ordinary Portland Cement and Ground Granulated Blast Furnace Slag. 1.1 OBJECTIVES OF THE STUDY To analysis the DPS and MPS of Leading Cement Companies like Ultratech Cement, ACC, Ambuja Cement, Shree Cement, India Cement, Prism Cement, Binani Cement, Ramco Cement, Birla Corp, JK Cement To know the overall efficiency and performance of the firm through financial analysis. To know the impact of Dividend on Market price 818

ISSN: 395-1664 (ONLINE) ICTACT JOURNAL ON MANAGEMENT STUDIES, AUGUST 018, VOLUME: 04, ISSUE: 03. REVIEW OF LITERATURE The concept of dividend policy has become an interesting issue in financial literature. Many researches have been made on dividend decision. Dividend is that part of the net earnings of a corporation that is distributed to its stockholders. It is a payment made to the equity shareholders for their investment in the company. A large number of studies have been conducted in the field of Dividend Policy and its impact on Market Price. A brief review of some of these studies has been presented. Krishman [3] propagated a bird in the hand theory, regarding dividend distribution. According to this theory investors are risk averse by their very nature. Lintner [4] focussed on the behavioural side of the policy regarding dividend payment decisions. He concluded that the managers take the decisions to increase the proportion of Dividend Payment, only when they are certain that the firm s earnings have increased permanently. Kanval and Kapoor [5] examined the determinants of dividend payment decision in the India s Information Technology (IT) sector. The time period of this study was 000-006. This study found that only liquidity and year to year variation in profit are the only two determinants of this decision. Bose and Husain [6] explored the dividend payout policy of five sectors in India, these five sectors were Software, Finance, Steel, Electrical Machinery, and Pharmaceutical. Profitability of the companies is found to be the sole Determinant of Dividend Pay-out decisions. Alzomania and Alkhadiri [7] examined the factors determining dividend policy represented by dividend per share for firms in the Saudi Arabia Stock Exchanges. They used regression model and used a panel data covering the period during 004-010 for 105 non-financial firms listed in the stock market. The results consistently supported that Saudi Arabia non-financial firms rely on current earning per share and past dividend per share of the firm to set their dividend payments. Baker et al. [8] surveyed 318 New York stock exchange firms and concluded that the major determinants of dividend payments are anticipated level of future earnings and pattern of past dividends. 3. SCOPE OF STUDY Dividend is a reward to equity shareholders for their investment in the company. It is a basic right of equity shareholders to get dividend from the earnings of a company. It is generally paid in cash form but also it can be paid in allocation of additional shares in the company. Dividend history report shows the amount of dividend a company pays during its life cycle. Normally investors want higher dividends from year to year. The study is concerned with the impact of DPS on Dividend Yield, EPS, MPS, P/E and Dividend Payout Ratio on 10 Leading Indian Cement Companies. The study covers a period of 6 years from 011-1 to 016-17. 4. METHODOLOGY 4.1 SOURCES OF DATA The study is based on secondary data. Information has been collected from the Annual Reports of Ultratech Cement, ACC, Ambuja Cement, Shree Cement, India Cement, Prism Cement, Binani Cement, Ramco Cement, Birla Corp, JK Cement and different books, journal, magazines, and data collected from various websites. 4. TOOLS APPLIED In this study various tools: Financial Tools [9] [10] - 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 viceversa. 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 H0. It is a negative statement which avoids personal bias of investigator during data collection as well as the time of drawing conclusion. 4.3 LIMITATION OF THE STUDY The study is related to a period of 6 years. Data is secondary i.e. they are collected from the published Annual Reports Profitability, structural and valuation ratio have been taken for the study. Dividend policy of a company is closely linked to its profitability and need for cash for financing future growth. Profit is the prime motive of every business. It plays a pivotal role behind the growth of an enterprise. It is the main base for liquidity as well as solvency. 819

Net Margin Ratio: It shows the relationship between net profit and sales. i.e., profit left for equity shareholders as a percentage of net sales. Table.1. Exhibit - 1: Net Profit Margin (%) Year Ultratech ACC Ambuja Shree India Prism Binani Ramco Birla JK Corp Cement 011-1 01-13 013-14 014-15 015-16 016-17 1.57 1.60 14.41 10.49 5.61-0.37-5.41 11.95 10.47 6.88 1.71 9.4 13.8 17.96 3.46-1.7-4.65 10.54 10.38 7.94 10.3 9.70 14.03 13.37-4.77-1.71-13.79 3.11 4.30.69 8.74 9.80 14.97 6.61-0.0 0.09-14.78 6.73 5.46 4.19 9.86 4.88 8.61 0.73.10 0.47-11.40 15. 5.13 1.45 10.69 5.33 7.06 15.89.69 0.30-1.80 16.74 5.05 5.43 Mean 10.81 8.59 1.06 14.17 1.51-0.4-10.47 10.71 6.80 4.76 SD 1.56.95 3.35 5.13 3.58 0.89 4.37 5.13.84.47 COV 0.14 0.34 0.8 0.36.37 -.14-0.4 0.48 0.4 0.5 CAGR (%) -3. -15.8-13.3 8.7-13.7-195.9 18.8 7.0-13.6-4.6 Exhibit-1 (Table.1) depicts that Shree Cements reported the highest mean value in terms of Net Profit Margin followed by Ambuja, Ultratech, Ramco etc. Standard deviation of Ramco Cement is the highest followed by Shree Cement, Binani, Ambuja etc. Binani Cement reported the highest CAGR of 18.8%. Ultratech, ACC, Ambuja, India Cement, Prism Cement, Birla Corp and JK Cement reported a negative CAGR. Hypothesis: =µ =µ 3=µ 4=µ 5=µ 6=µ 7=µ 8=µ 9=µ 10 (Net Profit of Cement Companies does not differ over years) µ µ 3 µ 4 µ 5 µ 6 µ 7 µ 8 µ 9 µ 10 (Net Profit of Cement Companies differ over years) Table.. Exhibit-: Net Profit Margin: Anova Single Factor Source of Variation Between Count Sum Average Variance Ultratech cement 6 64.88 10.81.45863 ACC 6 51.56 8.59 8.71698 Ambuja cement 6 7.36 1.06 11.54178 Shree cement 6 85.04 14.17 6.357876 India cement 6 9.07 1.51 1.8703 Prism cement 6 -.50-0.4 0.791994 Binani cement 6-6.83-10.47 19.0750 Ramco cement 6 64.9 10.71 6.366906 Birla Corp 6 40.80 6.80 8.037906 JK cement 6 8.58 4.76 6.117504 Table.3. Anova Variation SS df MS F P- value F criteria,941.38 9 36.804 6.79557 0.000.073351 Within 609.84 50 1.1968 Total 3,551. 59 Above analysis shows that the F value (6.79557) is more than the table value (.073351) in Table.3, therefore null hypothesis is rejected. Therefore, it is concluded that Net Profit Margin of the Cement Companies differs over the years. Earnings per Share (EPS): EPS is an important financial measure, which indicates the profitability of a company. It shows the relationship between Profit after Tax and no of Equity Shares outstanding. Table.3. Exhibit - 3: Earnings Per Share (EPS) Year Ultratech ACC Ambuja Shree India Prism Binani Ramco Birla Corp JK Cement 011-1 87.5 69.1 7.95 178 8.5-0.33-56.0 16. 31.1 5.0 01-13 98.1 56.3 8.37 88 5.8-1.0-70.4 17.0 35.1 33.0 013-14 80.8 58. 8.7 6-7.9-1.69-0.5 4.8 16.9 10.7 014-15 76.7 61.7 9.6 1 0.0 0.10-04.1 10.3.8 0.3 015-16 90.5 31. 5.3 38 3.8 0.49-137.6.9 1.8 7.8 016-17 99.0 3.1 7.15 384 5.1 0.30-149.6 7.9 8.5 3.4 Mean 89 51 8 54 3 0-140 17 6 SD 9 16 1 98 6 1 67 8 7 11 COV 0.10 0.31 0.19 0.38.9 -.4-0.48 0.50 0.6 0.49 CAGR (%).5-14. -.1 16.7-9.5-197.8 1.7 11.5-1.7 5.3 The Exhibit-3 (Table.3) depicts that Shree Cements reported the highest mean value in terms of EPS followed by Ultratech, ACC etc. Standard deviation of Shree Cement is the highest indicating the maximum deviation from the Mean value followed by Binani, ACC etc. Binani Cement reported the highest CAGR of 1.7%. ACC, Ambuja, India Cement, Prism Cement and Birla Corp reported a negative CAGR. Hypothesis: =µ =µ 3=µ 4=µ 5=µ 6=µ 7=µ 8=µ 9=µ 10 (EPS of Cement Companies doesn t differ over years) µ µ 3 µ 4 µ 5 µ 6 µ 7 µ 8 µ 9 µ 10 (EPS of Cement Companies differ over years) Table.4. Exhibit - 4: Earnings Per Share: Anova Single Factor Count Sum Average Variance Ultratech cement 6 53.60 88.77 81.30 ACC 6 308.6 51.44 53.90 Ambuja cement 6 46.59 7.77.18 Shree cement 6 1,56.57 54.43 9,53.99 India cement 6 15.7.54 33.94 Prism cement 6 -.34-0.39 0.77 Binani cement 6-838.18-139.70 4,515.66 Ramco cement 6 99.07 16.51 69.1 Birla Corp 6 156.09 6.0 45.39 JK cement 6 19.17 1.53 113. 80

ISSN: 395-1664 (ONLINE) ICTACT JOURNAL ON MANAGEMENT STUDIES, AUGUST 018, VOLUME: 04, ISSUE: 03 Source of Variation Between Within Table.5. Anova Variation SS df MS F P-value F criteria 5,1,61.59 9 56,956.95 38.9064488 1.5E-19.073351 73,197.3 50 1,463.95 Total 5,85,809.91 59 Above analysis shows that the F value (38.9064488) is more than the table value (.073351) in Table.5, therefore null hypothesis is rejected. Therefore, it is concluded that EPS of Cement Companies differs over years. Market Price Per Share (MPS): It is the price prevailing at NSE as on 31st March of the respective years. This reveals the value that the market currently assigns to each share. Table.6. Exhibit - 5: Market Price Per Share (MPS) Year Ultratech ACC Ambuja Shree India Prism Binani Ramco Birla Corp JK Cement 011-1 01-13 013-14 014-15 015-16 016-17 1,507 161 17 3,19 111.5 50.8 119 154 84.85 161.3 1,868 66 174 4,043 83.7 4.1 99 54 44.50 65.5,189 40 0 5,671 60.9 38.4 75 15 90.45 40.0,939 669 57 10,75 89. 99.3 91 94 40.80 668.8 3,7 676 33 1,41 86.3 80.5 6 400 370.15 675.5 3,990 935 37 17,083 16.5 97.9 73 673 739.75 935.0 Mean,60 491 1 8,865 99 68 87 33 389 491 SD 931 31 35 5,460 35 8 1 186 18 31 COV 0.36 0.63 0.17 0.6 0.35 0.41 0.4 0.56 0.47 0.63 CAGR (%) 1.5 4.1 6.6 39.6 7.83 14.0-9. 34.3 1.0 4.1 Exhibit-5 (Table.6) depicts that Shree Cements reported the highest mean value in terms of MPS followed by Ultratech, ACC etc. Standard deviation of Shree Cement is the highest indicating the maximum deviation from the Mean value followed by Ultratech, ACC etc. Both ACC and JK Cement reported the highest CAGR of 4.1%. Only, Binani Cements reported a negative CAGR. Hypothesis: =µ =µ 3=µ 4=µ 5=µ 6=µ 7=µ 8=µ 9=µ 10 (MPS of Cement Companies doesn t differ over years) µ µ 3 µ 4 µ 5 µ 6 µ 7 µ 8 µ 9 µ 10 (MPS of Cement Companies differ over years) Table.7. Exhibit - 6: Market Price Per Share: Anova Single Factor Count Sum Average Variance Ultratech cement 6 15,719.80,619.97 8,67,440.90 ACC 6,946.00 491.00 97,066.0 Ambuja cement 6 1,74.90 1.48 1,34.9 Shree cement 6 53,189.85 8,864.98,98,13,050.31 India cement 6 593.85 98.98 1,6.11 Prism cement 6 408.90 68.15 774.4 Binani cement 6 519.50 86.58 40.75 Ramco cement 6 1,989.40 331.57 34,715.6 Birla Corp 6,33.50 388.75 33,00.10 JK cement 6,946.00 491.00 97,066.0 Source of Variation Between Within Total Table.8. Anova Variation SS df MS F 40,57,3,8 9.35 15,47,9,9 8.75 56,04,53,8 75.10 9 4,50,80,4 3.48 50 30,94,599.66 59 14.5674 518 P- value 3.7E -11 F criteri a.0733 51 Above analysis shows that the F value (14.5674518) is more than the table value (.073351) in Table.8, therefore null hypothesis is rejected. Therefore, it is concluded that MPS of Cement Companies differs over years. Dividend per Share (DPS): It is an important financial metric which shows the money a company pays as dividend for each share. It is the relationship between Dividend Declared and no of Shares outstanding Dividend per Share = Total Dividends / Shares Outstanding or Dividend per Share = Earnings per Share Dividend Payout Ratio Year Table.9. Exhibit - 7: Dividend Per Share (DPS) Ultratech ACC Ambuja Shree India Prism Binani Ramco Birla Corp JK Cement 011-1 7.99 4.00 3.17 19.4.08 0.50 3.79.50 6 5.00 01-13 9 30 4 0.0.01 0.00 3 3.00 7 6.50 013-14 9.01 33.77 3.59.0 0.00 0.00 3.00 0.99 6 3.00 014-15 9.0 16.89 5.00 4.0 0.00 0.00.83 1.49 6 4.00 015-16 9.51 16.89.80 4.0 1.00 0.00.83.98 6 4.00 016-17 11.35 16.89.45 116.1 1.14 0.00 0.00.97 6 8.00 Mean 9 3 3 38 1 0 3 6 5 SD 1 7 1 39 1 0 1 1 0 COV 0.1 0.3 0.6 1.0 0.88.45 0.51 0.38 0.07 0.36 CAGR (%) 7.3-6.78-5.0 43.0-11.4-100.0-100.0 3.5 0.0 9.8 The Exhibit 7 depicts that Shree Cements reported the highest mean value in terms of DPS followed by ACC, Ultratech etc. Standard deviation of Shree Cement is the highest indicating the maximum deviation from the Mean value followed by ACC, JK Cement etc. Shree Cements reported the highest CAGR of 43%. ACC, Ambuja, Inia, Prism and Binani Cements reported negative CAGR. Hypothesis: =µ =µ 3=µ 4=µ 5=µ 6=µ 7=µ 8=µ 9=µ 10 (DPS of Cement Companies doesn t differ over years) µ µ 3 µ 4 µ 5 µ 6 µ 7 µ 8 µ 9 µ 10 (DPS of Cement Companies differ over years) 81

Table.10. Exhibit - 8: Dividend Per Share: Anova Single Factor Source of Variation Between Within Count Sum Average Variance Ultratech cement 6 55.88 9.31 1.5 ACC 6 138.4 3.04 55.07 Ambuja cement 6 0.58 3.43 0.79 Shree cement 6 5.6 37.60 1,483.47 India cement 6 6.4 1.04 0.84 Prism cement 6 0.50 0.08 0.04 Binani cement 6 15.45.57 1.7 Ramco cement 6 13.93.3 0.76 Birla Corp 6 37.00 6.17 0.17 JK cement 6 30.51 5.08 3.44 Table.11. Anova Variation SS df MS F P-value F criteria 7,790.96 9 865.66 5.593791305.54E-05.073351 7,737.70 50 154.75 Total 15,58.66 59 Above analysis shows that the F value (5.593791305) is more than the table value (.073351) therefore null hypothesis is rejected. Therefore, it is concluded that DPS of Cement Companies differs over years. Dividend Yield: It is a financial ratio that indicates how much a company pays out as dividends each year relative to its share price. Year Table.1. Exhibit - 9: Dividend Yield (%) Ultratech ACC Ambuja Shree India Prism Binani Ramco Birla Corp JK Cement 011-1 0.53 14.88 1.84 0.60 1.87 0.99 3.19 1.63.11 3.10 01-13 0.48 11..05 0.50.41 0.00 3.04 1.18.86.45 013-14 0.41 14.07 1.78 0.39 0.00 0.00 4.00 0.46.07 1.5 014-15 0.31.53 1.94 0. 0.00 0.00 3.10 0.51 1.49 0.60 015-16 0.9.50 1.1 0.19 1.16 0.00 4.54 0.74 1.6 0.59 016-17 0.8 1.81 1.03 0.68 0.70 0.00 0.00 0.44 0.81 0.86 Mean 0.38 7.8 1.6 0.43 1.0 0. 3.0 0.8 1.8 1.5 SD 0.1 6. 0.4 0. 1.0 0.4 1.6 0.5 0.7 1.1 COV 0.7 0.79 0.6 0.46 0.96.45 0.53 0.58 0.38 0.7 CAGR (%) -11.7-34.4-10.9.4-17.8-100.0-100.0-3.0-17.4 -.7 Exhibit 9 depicts that ACC reported the highest mean value in terms of Dividend Yield followed by Binani Cements, Birla Corp, Ambuja etc. Standard deviation of ACC is the highest indicating the maximum deviation from the Mean value followed by Binani, JK Cement etc. All the Cement Companies reported negative CAGR except Shree Cements. Hypothesis: =µ =µ 3=µ 4=µ 5=µ 6=µ 7=µ 8=µ 9=µ 10 (Dividend Yield of Cement Companies doesn t differ over years) µ µ 3 µ 4 µ 5 µ 6 µ 7 µ 8 µ 9 µ 10 (Dividend Yield of Cement Companies differ over years) Table.13. Exhibit - 10: Dividend Yield (%): Anova Single Factor Count Sum Average Variance Ultratech cement 6.31 0.38 0.01 ACC 6 47.01 7.84 38.6 Ambuja cement 6 9.85 1.64 0.18 Shree cement 6.58 0.43 0.04 India cement 6 6.14 1.0 0.97 Prism cement 6 0.99 0.16 0.16 Binani cement 6 17.87.98.48 Ramco cement 6 4.96 0.83 0.3 Source of Variation Between Birla Corp 6 10.96 1.83 0.48 JK cement 6 8.85 1.47 1.1 Table.14. Anova: Variation SS df MS F P-value F criteria 76.11 9 30.68 6.9864531.09E-06.073351 Within 1.40 50 4.43 Total 497.51 59 Above analysis shows that the F value (6.9864531) is more than the table value (.073351) therefore null hypothesis is rejected. Therefore, it is concluded that DPS of Cement Companies differs over 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. Dividend Policy is one of the major decisions in financial management. It determines the proportion of earnings to be paid by way of dividends and the proportion to be ploughed back for reinvestment purpose. Every firm must develop a DP i.e., divide its earnings into dividend and retained earnings in such a way which in turn, focuses on maximizing its shareholders wealth i.e., MPS. Table.15. Exhibit - 11: T-Test: Two-Sample Assuming Unequal Variances (Ultratech Cement) Mean 0.0038494 88.77 619.96666 9.604 0.1053 9.31 Variance 1.109E-06 81.3 867440.901 99.5375 0.0001 1.453-0.73306 0.518067 0.89674 0.73489 0.566807 t Stat -0.40573.917517 6.87340086 5.4090307-0.39494 P(T t) one-tail 0.0000061 0.0000014 0.00049848 0.0014604 0.0000066 t Critical one-tail.01504837.0150483.01504837.0150483.01504837 P(T t) two-tail 0.0000051 0.0000094 0.00099695 0.009086 0.00000533 t Critical two-tail.57058183.57058183.57058183.57058183.57058183 8

ISSN: 395-1664 (ONLINE) ICTACT JOURNAL ON MANAGEMENT STUDIES, AUGUST 018, VOLUME: 04, ISSUE: 03 = µ, Variance is not Equal) µ, Variance is Equal) Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that = µ EPS µ EPS Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that = µ MPS µ MPS Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that = µ P/E and µ P/E Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that DPR and DPS = µ P/E and DPR, Variance is not Equal) µ P/E and DPR, Variance is Equal) Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that Table.16. Exhibit -1: T-test: Two-Sample Assuming Unequal Variances (ACC) Mean 0.078355 51.44 491 1.177 0.4663 3.04 Variance 3.86E-03 53.9 97066.01 119.119 0.0155 55.068 0.863881 0.51061-0.8173-0.741776 0.411014 Mean Difference t Stat -7.63471689 5.070008 3.608575-1.5581006-7.5034583 P(T t) one-tail 0.00030663 0.0019335 0.0077043 0.089973 0.0003364 t Critical one-tail.01504837.0150483.0150483.01504837.01504837 P(T t) two-tail 0.0006137 0.0038670 0.0154086 0.17994643 0.0006659 t Critical two-tail.57058183.5705818.5705818.57058183.57058183 = µ, Variance is not Equal) µ, Variance is Equal) Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that = µ EPS µ EPS Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that = µ MPS µ MPS Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that = µ P/E and µ P/E Here the t Stat value lies between -.57058183 and +.57058183. Therefore, we reject Null Hypothesis stating that the = µ DPR µ DPR Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that Table.17. Exhibit - 13: T-TEST: Two-Sample Assuming Unequal Variances (Ambuja) Mean 0.0164180 7.77 1.4833333 8.5361 0.449 3.43 Variance 1.758E-05. 134.9066 80.3544 0.0054 0.7874 0.74096 0.797688 0.36409-0.435513 0.517805 Mean Difference t Stat -9.45607878 11.364409 14.6586018 6.559688-8.5918413 P(T t) one-tail 0.0001117 0.0000469 0.00001335 0.000600 0.0001761 t Critical one-tail.0150483.0150483.01504837.0150483.0150483 P(T t) two-tail 0.00033 0.0000938 0.0000669 0.001400 0.00035 t Critical two-tail.57058183.5705818.57058183.5705818.5705818 83

= µ, Variance is not Equal) µ, Variance is Equal) Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that = µ EPS µ EPS Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that = µ MPS µ MPS Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that = µ P/E and µ P/E Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that = µ DPR µ DPR Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that Table.18. Exhibit - 14: T-Test: Two-Sample Assuming Unequal Variances (Shree Cement) Mean 0.0043045 54.43 8864.975 37.904 0.1413 37.60 Variance 3.935E-06 9,54.0 9813050.3 73.8141 0.0083 1483.47 0.575608 0.651605 0.769089 0.1563 0.87549 t Stat -.391639 6.79516155 3.9816391 0.01694356 -.3874131 P(T t) one-tail 0.03114510 0.0005533 0.0055678 0.49356848 0.0319389 t Critical one-tail.01504837.01504837.01504837.01504837.01504837 P(T t) two-tail 0.06901 0.00105067 0.01051356 0.98713696 0.0658777 t Critical two-tail.57058183.57058183.57058183.57058183.57058183 = µ, Variance is not Equal) µ, Variance is Equal) Here the t Stat value lies between -.57058183 and +.57058183. Therefore, we reject Null Hypothesis stating that the = µ EPS µ EPS Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that = µ MPS µ MPS Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that = µ P/E and µ P/E Here the t Stat value lies between -.57058183 and +.57058183. Therefore, we reject Null Hypothesis stating that the = µ DPR µ DPR Here the t Stat value lies between -.57058183 and +.57058183. Therefore, we reject Null Hypothesis stating that the Table.19. Exhibit - 15: T-Test: Two-Sample Assuming Unequal Variances (India Cement) Mean 0.0109.54 98.975-371.871 0.1797 1.04 Variance 9.690E-05 33.9 16.1097 89479.955 0.011 0.84 0.956385 0.864874 0.340784 0.560056 0.90865 t Stat -.7756756 0.795077 6.9105609-0.96466004 -.674604 P(T t) one-tail 0.01955090 0.49085 0.000486 0.18950990 0.010889 t Critical one-tail.01504837.0150483.0150483.01504837.01504837 P(T t) two-tail 0.03910180 0.4984170 0.000975 0.37901980 0.0441777 t Critical two-tail.57058183.5705818.5705818.57058183.57058183 84

ISSN: 395-1664 (ONLINE) ICTACT JOURNAL ON MANAGEMENT STUDIES, AUGUST 018, VOLUME: 04, ISSUE: 03 = µ, Variance is not Equal) µ, Variance is Equal) Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that = µ EPS µ EPS Here the t Stat value lies between -.57058183 and +.57058183. Therefore, we reject Null Hypothesis stating that the = µ MPS µ MPS Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that = µ P/E and µ P/E Here the t Stat value lies between -.57058183 and +.57058183. Therefore, we reject Null Hypothesis stating that the = µ DPR µ DPR Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that Table.0. Exhibit - 16: T-Test: Two-Sample Assuming Unequal Variances (Prism Cement) Mean 0.0016417-0.39 68.15 19.8393-0.491 0.08 Variance 1.617E-05 0.8 774.418 1886.6317 0.374 0.0417 1.000000 0.0315-0.305434-0.419158-1.000000 t Stat -1.000000-1.3010038 5.9777635 1.3917754-1.000000 P(T t) one-tail 0.1816087 0.1499141 0.0009385 0.13513641 0.1816087 t Critical one-tail.0150483.01504837.0150483.01504837.0150483 P(T t) two-tail 0.363174 0.499883 0.0018770 0.70781 0.363174 t Critical two-tail.5705818.57058183.5705818.57058183.5705818 = µ, Variance is not Equal) µ, Variance is Equal) Here the t Stat value lies between -.57058183 and +.57058183. Therefore, we reject Null Hypothesis stating that the = µ EPS µ EPS Here the t Stat value lies between -.57058183 and +.57058183. Therefore, we reject Null Hypothesis stating that the = µ MPS µ MPS Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that = µ P/E and µ P/E Here the t Stat value lies between -.57058183 and +.57058183. Therefore, we reject Null Hypothesis stating that the = µ DPR µ DPR Here the t Stat value lies between -.57058183 and +.57058183. Therefore, we reject Null Hypothesis stating that the Table.1. Exhibit - 17: T-Test: Two-Sample Assuming Unequal Variances (Binani Cement) Mean 0.097819-139.70 86.5833333-0.8756-0.064.57 Variance.48E-04 4,515.7 40.750666 0.546 0.0006 1.711 0.85306 0.4087 0.509168-0.481696-0.714016 t Stat -4.8016-5.096681 10.3590-4.7537591-4.79598 P(T t) one-tail 0.004391 0.0017196 0.000073 0.005437 0.004579 t Critical one-tail.0150483.0150483.0150483.0150483.0150483 P(T t) two-tail 0.004878 0.003439 0.0001447 0.0050875 0.0049159 t Critical two-tail.5705818.5705818.5705818.5705818.5705818 85

= µ, Variance is not Equal) µ, Variance is Equal) Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that = µ EPS µ EPS Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that = µ MPS µ MPS Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that = µ P/E and µ P/E Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that = µ DPR µ DPR Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that Table.. Exhibit - 18: T-Test: Two-Sample Assuming Unequal Variances (Ramco Cement) Mean 0.008695 16.51 331.56667 3.076 0.1531.3 Variance.311E-05 69.1 34715.59 156.1308 0.001 0.7604 0.396630 0.896575 0.45445-0.810896-0.60090 t Stat -6.51314101 4.608518 4.3376163 3.871351-5.94495177 P(T t) one-tail 0.00063745 0.008976 0.00378 0.0058674 0.00096190 t Critical one-tail.01504837.0150483.0150483.0150483.01504837 P(T t) two-tail 0.0017489 0.0057953 0.0074456 0.0117348 0.0019380 t Critical two-tail.57058183.5705818.5705818.5705818.57058183 = µ, Variance is not Equal) µ, Variance is Equal) Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that = µ EPS µ EPS Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that = µ MPS µ MPS Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that = µ P/E and µ P/E Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that = µ DPR µ DPR Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that Table.3. Exhibit - 19: T-Test: Two-Sample Assuming Unequal Variances (Birla Corp) Mean 0.01861 6.0 388.75 15.6678 0.497 6.17 Variance 4.793E-05 45.4 3300.105 46.4534 0.0039 0.1667 0.733676 0.660707-0.389001-0.65506-0.394883 t Stat -37.356763 7.5089768 5.15407143 3.879788-33.19495 P(T t) one-tail 0.00000013 0.0003318 0.0018019 0.01088099 0.0000003 t Critical one-tail.01504837.01504837.01504837.01504837.01504837 P(T t) two-tail 0.0000006 0.0006656 0.0036058 0.0176199 0.00000047 t Critical two-tail.57058183.57058183.57058183.57058183.57058183 86

ISSN: 395-1664 (ONLINE) ICTACT JOURNAL ON MANAGEMENT STUDIES, AUGUST 018, VOLUME: 04, ISSUE: 03 = µ, Variance is not Equal) µ, Variance is Equal) Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that = µ EPS µ EPS Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that = µ MPS µ MPS Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that = µ P/E and µ P/E Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that = µ DPR µ DPR Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that Table.4. Exhibit - 0: T-Test: Two-Sample Assuming Unequal Variances (JK Cement) Mean 0.0147460 1.53 491 30.8115 0.70 5.08 Variance 1.115E-04 113. 97066.0 850.39 0.0148 3.4404 0.185105 0.87413 0.391950-0.9811-0.35441 t Stat -6.70613 4.4435915 3.89196.1171084-6.115080 P(T t) one-tail 0.0005594 0.0033711 0.006187 0.0439134 0.0007900 t Critical one-tail.0150483.0150483.0150483.0150483.0150483 P(T t) two-tail 0.0011189 0.006743 0.01574 0.087868 0.0015801 t Critical two-tail.5705818.5705818.5705818.5705818.5705818 = µ, Variance is not Equal) µ, Variance is Equal) Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that = µ EPS µ EPS Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that = µ MPS µ MPS Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that = µ P/E and µ P/E Here the t Stat value lies between -.57058183 and +.57058183. Therefore, we reject Null Hypothesis stating that the = µ DPR µ DPR Here the t Stat value don t lie between -.57058183 and +.57058183. Therefore, we accept Null Hypothesis stating that 5. ANOVA FINDINGS The study reveals that: Shree Cements reported the highest mean value in terms of Net Profit Margin, EPS, DPS The Mean Value of all the Cement Companies are positive in terms of EPS except Ramco In case of MPS, both ACC and JK Cement reported the highest CAGR of 4.1% In case of DPS, Prism Cement reported zero DPS from since 01. Moreover, ACC, Ambuja, Inia, Prism and Binani Cements reported negative CAGR. 87

ACC reported the highest mean value in terms of Dividend Yield followed by Binani Cements, Birla Corp, Ambuja T-Test Conducted with selected Cement Firms revealed that, There is significant relationship between Dividend Yield and DPS There is significant relationship between There is significant relationship between There is significant relationship between Price Earnings Ratio and DPS There is significant relationship between Dividend Payout Ratio and DPS 6. CONCLUSION DPS has significant effect on MPS. When a firm pays dividend regularly with periodic enhancements, the Shareholders Wealth gets maximized Retained earnings per share (RPS) act as an important factor in determining the SW since, increase in RPS lead to increase in net-worth. Shareholders prefer current dividend than future income so, dividend is considered to be an important variable, which maximizes Shareholders Wealth. REFERENCES [1] Cement Sector Analysis Report, Available at: https://www.equitymaster.com/research-it/sector- info/cement/cement-sector-analysis- Report.asp?utm_source=stockquote. [] Sri Ayan Chakraborty, Value Based Analysis: A Study On Leading Indian Cement Firms, International Journal of Business and Administration Research Review, Vol., No. 1, pp. 39-65, 018. [3] J.E. Krishman, Principles of Investment, McGraw Hill, 1963. [4] J. Lintner, Distribution of Incomes of Corporations among Dividends, Retained Earnings and Taxes, The American Economic Review, Vol. 46, No., pp. 97-113, 1956. [5] A. Kanwal and S. Kapoor, Determinants of Dividend Payout Ratios- A Study of Indian Information Technology Sector, International Research Journal of Finance and Economics, Vol. 7, No. 5, pp. 63-71, 008. [6] S. Bose and Z. Husain, Asymmetric Dividend Policy of Indian Firms: An Econometric Analysis, International Journal of Applied Economics and Finance, Vol. 5, No. 3, pp. 78-85, 011. [7] T.S.F. Alzomania and A. Al-Khadiri, Determination of Dividend Policy: The evidence from Saudi Arabia, International Journal of Business and Social Science, Vol. 4, No. 1, pp. 181-19, 013. [8] H.K. Baker, G.E. Farrelly and R.B. Edelman, A Survey of Management Views on Dividend Policy, Financial Management, Vol. 14, No. 3, pp. 78-84, 1985. [9] Chandra Prasanna, Financial Management Theory and Practice, 8 th Edition, McGraw Hill Education, 011. [10] I.M. Pandey, Financial Management, 11 th Edition, Vikas, 018. 88