A Study of Factors Driving Shareholders’ Value Free Essays

string(59) " Commodity Broking and Distribution of Financial Products\." [pic] A Study of Factors Driving Shareholders’ Value and Influencing Sensex Fluctuation In India Executive Summary The objective of this project is to analyze the most important factors which drive shareholders, value. Shareholders’ value here refers to the MVA (market value added) which means the additional value which shareholders are earning on their invested money. The performance of a company matters a lot in creating a positive image of that company in front of its stakeholders. We will write a custom essay sample on A Study of Factors Driving Shareholders’ Value or any similar topic only for you Order Now Moreover, the main objective of a company is to maximize the shareholders’ value. So, shareholders always want to know that the Company with whom they have entrusted their hard earned money is efficiently utilizing it and thus, creating Value for them. We have always read the annual report of the Companies to find out information about their â€Å"top line† and â€Å"bottom line†. We also have various financial ratios and terms which act as essential factors to consider for our aid like Return on Capital Employed (ROCE), Return on Net worth (RONW), Earning per Share (EPS), Dividend per Share (DPS), Debt Equity Ratio (D/E Ratio) and Economic Value Added (EVA). This research is an attempt to find out whether EVA, DPS, D/E Ratio, EPS, ROCE, RONW of the companies listed in sensex really explains the value accretion for the shareholders and cause fluctuation in sensex. So, I have taken these variables as Independent variables and MVA as a dependent Variable (shareholders’ value) to apply regression analysis to come out with a result that which variable is having a high degree of Correlation with MVA and significantly explains variation in MVA. To perform this analysis secondary data has been collected from Prowess and www. bseindia. com Out of 30 companies listed in sensex, 23 companies are selected as sample. 7 companies are eliminated because of inadequate information available for these companies. The reason behind choosing these companies is that their reliability in terms of selection of the Companies as only those Companies are selected which have a listing history of at least 3 month with sufficient trading frequency. After that the data of different financial indicators of these Companies (RONW, ROCE, D/E Ratio, EPS, DPS, Avg. Market Capitalization and Beta value) are collected for the period of 2003-2008. CAPM model is used for calculating cost of equity. The EVA and MVA is calculated. After that change in MVA has been calculated with respect of previous year. Here 2003 has been taken as a base year and study has been done year wise from 2004-2008. Both EVA and Change in MVA are standardized by dividing both of them by Net Worth of the respective companies. This is done in order to get relative value of EVA and MVA over the same Net Worth. SPSS software is used for applying simple and multiple regression analysis. First Simple regression has been applied taking one Independent variable at a time in order to find most important variable and eliminate least important variable and analyze each variables influence over Change in MVA individually. After this multiple regressions has been applied in order to find the combination of Independent variables which are strongly correlated with change in MVA. In my study EVA has been found the most important variable then RONW, and then ROCE. These variables are having a high degree of correlation with change in MVA and significantly explaining the variation in MVA individually. While the combination of EVA, EPS, and DPS are having a very high degree of correlation with change in MVA. So, my analysis shows that it is best to invest in a company generating higher and positive EVA, RONW, and ROCE it will add additional value to shareholders. INDEX Chapter-1 Introduction Page No. 1. 1 Company Profile†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 12-13 1. 2 Product Services Offered by IL Investsmart†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 14-16 1. 3 Background of the Problem†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦17-19 1. Introduction of the Project†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚ ¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 20-21 1. 5 Scope of the Project†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 22 1. 6 Literature Review†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 23-24 1. 7 Abbreviation†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 25 1. 8 Research Objective†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 26 1. 9 Introducing MVA and EVA†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 27 Chapter-2 Research Methodology†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦29-30 2. 1 Limitation of Research†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 31 Chapter -3 Research Analysis 3. 1 Different Measures used for Analysis†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦36-36 3. 2 Regression Analysis†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 37 3. Year wise Result of Simple Regression analysis†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦38-42 3. 4 Overall Res ult of Simple Regression Analysis†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 43 3. 5 Year wise Results of Multiple Regression Analysis†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 44-62 3. 6 Overall result of Multiple Regression Analysis†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 63-64 Recommendations†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 65 Conclusion†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 66 Bibliography†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â ‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 67 Appendices Appendix-1 Table of Annual Return of Sensex†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 68 Appendix-2 Table of Year wise Annual NOPAT of the Companies†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 9 Appendix-3 Table of Year wise Annual RONW of the Companies†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 70 Appendix-4 Table of Year wise Annual ROCE of the Companies†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 71 Appendix-5 Table of Year wise Annual D/E Ratios of the Companies†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 72 Appendix-6 Table of Year wise EPS of the Companies†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 73 Appendix-7 Table of Year wise DPS of the Companies†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢ € ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 74 Appendix-8 Table of Year wise Annual Market Cap. of the Companies†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 75 Appendix-9 Table of Year wise Equity of the Companies†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦76 Appendix-10 Table of Year wise Bank borrowing of the Companies†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 7 Appendix-11 Table of Year wise Annual Beta value of Companies†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 78 Appendix-12 Table of Year wise Levered Beta value of Companies†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦79 Appendix-13 Table of Year wise cost of equity of Companies†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 80 Appendix-14 Table of Year wise Cost of Capital of Companies†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 81 Appendix-15 Table of Year wise EVA of Companies†¦Ã¢â ‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 82 Appendix-16 Table of Year wise Stdz. EVA of Companies†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 83 Appendix-17 Table of Yaer wise MVA of Companies†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 84 Appendix-18 Table of Change in MVA of Companies†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 85 Appendix-19 Table of Stdz. MVA of Companies†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 86 Chapter-1 Introduction 1. 1-Company Profile: IL IL Investsmart Limited (IIL) is one of India’s leading financial services organizations providing individuals and corporate with customized financial management solutions. Investsmart has a strong presence across wide range of products and operates in the areas of Investment Management and Advisory Services, Broking Services, Merchant Banking, Project Syndication, Equity and Debt Broking, Commodity Broking and Distribution of Financial Products. You read "A Study of Factors Driving Shareholders’ Value" in category "Essay examples" Earlier the company was owned by IL Group but is now held by HSBC, one of the world’s largest banking and financial services organizations. According to press Release by HSBC, the Company has completed the acquisition of 93. 86% of IL Investsmart Limited for a total consideration of INR 1,311 Crore. According to Sandy Flockhart, Group Managing Director Chief Executive Officer of Asia-Pacific, Investsmart will give HSBC access to the World’s third-largest Investor base, with over 20 million retail Investors. The business already has 143000 Customers. The documentation and name changing process is yet going on (till 15th June 2009). In India, The HSBC Group offers a range of financial services including corporate, commercial, retail and private banking, insurance, asset management, investment banking, equities and capital markets, institutional brokerage, custodial services. It also provides software development expertise and global services facilities for the HSBC Group’s operations worldwide. IL Investsmart Ltd has an all India presence through its network of branches and franchisees over 128 cities. Following a successful Initial Public Offer (IPO), IIL has been listed on both the National Stock Exchange (NSE) and the Bombay Stock Exchange (BSE). IIL is geared towards understanding and achieving the financial goals of all its customers, through its specialists in the aforesaid areas. IIL’s 2000 employee provide a complete range of Investment solution in India through 88 branches and 190 Franchisee outlets from 128 Cities. It has been recognized as â€Å"National Best Performing Financial Advisor – Retail† for two years in a row (06-07 and 07-08) by CNBC TV 18, With a market capitalization of approximately US$260 million. The Corporate Office and Research Division are located in Mumbai. In Delhi, the regional office is at Caunaght Place and the branch office is at Pitampura. . 2-Products and Services Offered by the IL: The Company has grouped its Product and services in following manners: o Retail Offering o Institutional Offering o Advisory Report Advisory Services o Online Trading Retail Offering: It includes Advisory Products regarding research reports and analysis. Trading product: It includes Equity and Derivatives. NRI Products: It includes NRE Equity, NRI Portfolio Management services, Mutual Funds, IPO, Insurance, Wealth Management Products, PAN card services, Advisory Report and Accounting and income Tax return filling in India. Institutional Offering: |Investment Banking Services | |IL Investsmart (IIL) offers extensive range of Investment Banking Services for equity | |related products and instruments. Their team advises Customers on transactions like business | |structuring and capital raising opportunities based on their corporate needs and state of capital | |markets. Services it specialize in include Management of: | |Initial Public Offering (IPOs) | |Follow-on Offerings | |Qualified Institutional Placements (QIPs) | |Buyback of Equities | |Open Offers | |Mergers Acquisitions | |Private Equity Placements | |ESOPs | |Institutional Equity Broking Services: | It includes IPOs, equities, derivatives and mutual funds. It also focuses on identifying undiscovered value stocks to investors. Through its gamut of services, this division is well-suited to corporate investors, banks, financial institutions, insurance companies and FII’s. Their Institutional Equity Business (IEB) is well positioned to offer support for a complete range of investment banking service to corporate. Institutional Debt Broking Services: | Its institutional debt broking division includes, secondary market broking, primary market debt placement distribution and provident fund advisory services. Advisory Report Advisory Services: It includes Equity Report, Mutual Fund Report, Debt Market Report, Sector Report, Derivatives Technical Reports. The reports are sent to the Customers on a daily basis before opening of Stock Market in the Morning through email. It also provides Advisory Services by message alert and appointing Relationship Manager to HNI clients. Online Trading: For Online trading Company provides three products as online trading Platform: SmartStart It is a powerful browser based trading system for those who are relatively new to online investing. A unique integrated account, which integrates Cusomer’s banking, broking, and demat accounts. A comprehensive trading service, which allows Investors to invest in equities and derivatives. SmartStart trading platform allows you the flexibility of trading on any internet capable system, with access to both the NSE and BSE. SmartInvest is a browser-based system designed for customers who transact occasionally. It is ideal for investors who believe in the Buy and Hold approach towards investment in equities. SmartInvest’s capability as a browser-based trading platform gives the benefit of real-time streaming data with the flexibility of trading on any Internet capable system. With access to both the NSE BSE. SmartInvest sophisticated yet easy to use point and click order entry interface allows you to react more quickly to the markets and make better decisions. SmartTrade is an EXE based desktop software designed for active traders who transact frequently to capture favorable short-term price movements. The platform offers active traders the tools they need to make critical decisions with confidence. SmartTrade is designed and built from the ground up to address the needs of active traders. SmartTrade makes the most of state-of the-art technology to deliver power, speed and reliability. Through an easy-to-use interface, users are provided with the same tools and advantages that the professionals enjoy. 1. 3-Background of the Problem In two month of my training my job was divided into two parts. In one part I was told to sell the online trading product of the Company and in other part I was told to do my project in equity research. Since the Company is very much customer oriented, It wants to give a complete Investment solution to the customer so that the Company can delight them. For this company has a research division located in Mumbai. Since I was working in Pitampura Branch and that branch deals in Equity only (Online trading Product). So, I was upposed to do my project on Equity research. In this two month as a summer trainee I use to generate Client Database from my own sources and then approaching to the potential Customers by calling and arranging meeting with them and finally converting them into Customers. I was also handling the query of Existing Customer s of the Company regarding Online Products. On the basis of their Query I felt that the new retail Investors as well as existing Customers need a strong support from company to have an idea in which stock they should invest, so that in future their investment will result in a positive or will increase market value of their Investment in Stock market. For this Company use to send three research reports regarding better investment option on a daily basis on their email-id, message alert on mobile, and appoint Relationship Manager. So, for complete Customer satisfaction the company needs to have a strong research Division which is already there in Mumbai. Moreover, because of Global Melt Down investors are very much afraid to invest in Stock Market as the sensex reaches to a minimum of 8000 from 21000 within a Year. Now they are looking for those brokerage firms, which will guide them with a strong research analysis. So, in this case it is very much essential for the Research analysts working in the Company to analyze those factors which are really going to accelerate the market value of share holders in order to gain a competitive edge over the Competitors. It is the time to do an in depth study on those factors which an investor should consider before investing in a particular Company or Stock, if they want to add some more money to their pocket. So, I was given a task to analyze those financial factors which will drive the share holders’ value in future and will keep them at safe end for a long as well as short term Investment. Since, if the market value of stock will increase, sensex will also go up. So, here we need to study on those factors which will increase the Market Value. First thing which comes in my mind is that Stock of a particular Company is very much similar to a person, like whatever is happening in a person’s life, he or she is the one who is responsible for that. For example if a person is not able to pass an MBA exam then we say that something is wrong with his or her mind, if he or she is not able to walk properly then something is wrong with his or her health. It means the problem is within the person. The same things apply in the stock Market like if a company is not able to increase its market value (Share holders’ Value) or market Capitalization it might be the reason that Company is not performing well, not generating enough Profit, not able to use its assets in an effective and efficient manner, not able to increase the earnings of its owner and etc. because of which it is destroying the share holders’ value. So, I decided to work on the financial performance of Companies itself and to analyze whether the financial Performance of a company like (RONW, ROCE, D/E Ratio, EPS, DPS, EVA) is having any kind of correlation with their market value. I have also tried to analyze that is it so that we should consider these factors as a driver of Share holders’ value? Will positive change in these factors give positive result to the Share holders? This is the rationale behind working on this project which is very much required to understand in this recovery period of Economy. 1. 4-Introduction of the Project Today, one of the major goals of financial management is maximum utilization of the capital employed for maximization of Shareholders’ value. Since capital resources are scarce and costly, companies try to employ these resources in a way that yields highest return. Of course this should be accompanied by steps taken to minimize the cost of acquired resources. Otherwise, it will not increase the shareholders wealth and the firm’s value. The manager of a firm (as an internal user of financial information) and the investors and other parties (as the external users) are interested to use an appropriate performance measure in order to assess how the managerial actions affect the value of the firm. For this purpose the performance measure used, must consider at least three things, which are: the amount of capital invested, the return earned on the capital, and the cost of capital (Weighted Average Cost of Capital). So, the first question comes to our mind is that how do shareholders know that the Company with whom they have entrusted their hard earned money is efficiently utilizing it and thus, creating Value for them. We have always read the annual report of the Companies to find out information about their â€Å"top line† and â€Å"bottom line†. We also have various financial ratios and terms which act as essential factors to consider for our aid like Return on Capital Employed (ROCE), Return on Net worth (RONW), Earning per Share (EPS), Dividend per Share (DPS), D/E Ratio, and Economic Value Added (EVA). Out of all these factors EVA was introduced as an Indicator for Shareholders’ wealth maximization in 1990’s by Stern Stewart Co. It has been a focal point for majority of the studies. Stern has claimed that EVA as a tool of financial management is not just a phenomenon and neither is it limited to only `for profit’ organizations. Economic Value Added has been put to use for management performance evaluation and much more than just a measure of performance, it is a framework for complete financial management (for improving allocation of scarce capital; and for valuation of a target company at the time of acquisition). On the other hand, Market Value Added (MVA) is an indicator which measures the stock returns and shows the effect of different factors on share prices, in a particular market. While EVA is an accounting-based measure for the corporate performance of one year, MVA is a market-generated number. MVA is cumulative measure of the value created by the management in excess of the capital invested. This research is an attempt to find out whether EVA, EPS, ROCE, RONW, DPS, D/E Ratio of the companies listed in sensex explains the value accretion for the shareholders and fluctuation in sensex. 1. 5-Scope of the Research Since, BSE Sensex of India represents the whole Indian Economy. The companies listed in sensex are the representative of all the major industries of Indian Economy. Millions of investors have invested their money in the stock market. The stock market is something where investors can earn lot of money but risk is also there, because it follows fundamental of high Risk High Return. So, it is very much require to analyze the behavior of market. It means we should know in which company we should invest or we should not. So, the scope of research is to analyze the most important driver of shareholders’ value. Since, Research Division of IL Invetssmart are very much working on analyzing the behavior of stock market so that they can properly guide their customers regarding investment. This research will be a value addition for the Research Division of IL Investsmart, as it ill give an idea which factor is highly correlated with Market value of the Companies. The market value of the Companies is very much dependent on the performance of the Companies. But which performance measure should be taken into consideration by the Investors before investing in any company is very much required. So, this research will expla in whether performance measure does have any correlation with market Value added of the Companies. If it is so then which of the performance measures is strongly influencing the shareholders’ value? The research is an attempt to analyze the influence of few performance measures over the shareholders’ value and this will help in taking correct investment decision. 1. 6-Literature review Stewart (1991) had carried out a research to find out the relationship between EVA and MVA. This study was done by taking average EVA values for the year 1987 and 1988 of 613 companies in USA and then comparing them with their MVA values for 1988. The study found an r2 of 97% between the EVA and MVA value for the Companies with positive EVA while this correlation was insignificant for the companies with negative EVA values. Finegan (1991) took a sample of 450 Companies in USA and found that average value of EVA could explain 61% of the variance in MVA whereas the similar figure was 44% between the change in EVA and change in MVA. He also observed that this r2 was 47% between ROCE and MVA. Dodd and Chen (1992) found ROA as a better driver of Shares returns as compared to EVA. Stern (1993) found out that EVA is the best measure that drives the Shareholders’ value with an r2 of 50% with MVA. The next important driver was ROE with an r2 of 25% with MVA. Lehn and Makhija (1996) also studied the relationship of share returns with ROE, ROA, Return on Sales (ROS), EVA, MVA and CEO turnover. Correlation was found to be highest in case of EVA however, (EVA divided by the Cost of Capital), NOPAT (Net Operating Profit after Tax) and free cash flow and correlation with them with market value divided by invested Capital. He found NOPAT as a better indicator with an r2 of 33% compared with 31% in case of EVA. However, changes in EVA values explained 74% of the change in market value over a period of 10 years. Uyemara and others (1996) studied MVA’s correlation with EVA, Net Income, EPS, ROE, and ROA over a period of 10 years. r2 was highest technology industry for the period 1990-95 and found an r2 of 42%. EPS was judged as the second best measure of with an r2 of 34%. Kramer and Pushner (1997) established that lagged levels of NOPAT explained MVA better as compared to EVA. This correlation was found higher even when changes in NOPAT were correlated with changes in MVA. According to Biddle and others (1991), Net Income was found to be the best measure to explain Share returns. Majority of these studies were focused on US Companies. Giffith (2004) concluded that an Investor or analyst using EVA or MVA measures to forecast performance would have experienced significant losses. Ferguson and others (2005) also doubted that adopting EVA improves stock performance. JHvH de Wet (2005) analyze the database of 89 south African Companies and observed that the Standardized Cash Flow from Operations (CFO divided by the invested Capital in the beginning) had an r2 of 38% with the Standardized MVA (MVA divided by the invested Capital in the beginning), which was found to be the best driver as compared to the Standardized EVA (EVA divided by the Invested Capital in the beginning), ROA, ROE, EPS, and DPS. He also observed that Correlation of EPS and DPS for valuing the Shares. Roji George (2005) analyzed the data of 21 Indian banks for the period 1999-2003 and concluded that there is a positive relationship between EVA and productivity and negative relationship between EVA and NPA. So, what I have found that nobody has done any analysis on the 30 companies which is listed in Sensex, while these companies represent all the major Industries of Indian Economy. So, it is better to analyze these Companies’ behavior. So, this research is an attempt to bridge this research gap. 1. 7- Abbreviation |NOPAT |Net Operating Profit After Tax | |RONW |Return on Networth | |ROCE Return on Capital Employed | |D/E Ratio |Debt/Equity Ratio | |EPS |Earning per Share | |DPS |Dividend per Share | |EVA |Economic Value Added | |MVA |Market Value Added | |R |Coefficient of Correlation | |R2 |Coefficient of Determination | 1. 8-Research Objective The following are the objectives of my research: Main Objective The primary objective is to find out what drives the share ho lders’ value. Specific Objective 1) To find out the correlation of the measures like RONNW, ROCE, D/E Ratio, EPS, DPS and EVA with MVA(Market Value added) 2) To find out the most important factors or variable which explain variance in MVA and that variable should be consider before investing in any Company. 1. 9- Introducing EVA and MVA As the introductory paragraph of this paper suggests, EVA is the surplus profit after accounting for all the expenses including the cost of capital. We have always looked at the figures of Profit after Tax to find out whether a company is performing well or not. However, what we forget is that the shareholders invest money in a company in expectation of some return. So, the basis for evaluation should be whether the company has earned over and above the minimum required rate of return by the investors. If there is surplus after accounting for this opportunity cost of equity, the company is creating value for its shareholders. If not, then it is destroying value. In other words, value is created when return earned by the firm is more than its cost of capital or firm invests in the project with positive NPV. EVA can be calculated through any of the following methods:  · Increasing revenue, Reducing operating costs, Efficient utilization of assets and Raising funds at cheaper cost Chapter-2 Research Methodology Research MEthodology Quantitative Research Design has been used in this research. This analysis was carried out over a period of 6 years (2003-2008) on companies which form part of BSE Sensex. Nature of Data: Secondary Data has been used for this research. The Year wise annual data of NOPAT, RONW, ROCE, D/E Ratio, EPS, DPS, Avg. Market Capitalization, beta value of 23 Companies out of 30 Companies listed in the Sensex. Source of Data: For regression analysis the data has been collected from CMIE’s Prowess and www. bseindia. com. Research Design: Descriptive Research Design has been used as the problem is well define and key issues are known and which is to find out the most important variable which drive the Shareholders, Value. Under this Research design, Cross Sectional Study has been done. Year wise annual value of all the variables has been collected from 2003-2008 for finding the cause and effect relationship between Independent Variables (RONW, ROCE, D/E Ratio, EPS, DPS and EVA) with dependent variable (Change in MVA). The study has been done on yearly basis. Sampling: Judgment (Purposive) Sampling Method has been used for selecting Companies. The analysis has been carried out over 23 Companies out of 30 Companies listed in sensex. Though Sensex comprises 30 companies, 7 companies were eliminated because of the inadequate information available for these Companies. The reason for choosing these Companies are their reliability in terms of selection of the Companies as only those Companies are selected which have a listing history of at least 3 month with sufficient trading frequency. Sample Size: Sample size is of 23 Companies has been taken for the Year 2003-2008. The following Table shows the List of Companies: |Company Name | |Bharat Heavy Electricals Ltd. Oil Natural Gas Corpn. Ltd. | |Bharti Airtel Ltd. |Ranbaxy Laboratories Ltd. | |Grasim Industries Ltd. |Reliance Industries Ltd. | |H D F C Bank Ltd. |Reliance Infrastructure Ltd. | |Hindalco Industries Ltd. |State Bank Of India | |Hindustan Unilever Ltd. |Sterlite Industries (India) Ltd. | |Housing Development Finance Corpn. Ltd. |Sun Pharmaceutical Inds. Ltd. | |I C I C I Bank Ltd. |Tata Motors Ltd. | |I T C Ltd. |Tata Power Co. Ltd. | |Infosys Technologies Ltd. |Tata Steel Ltd. | |Larsen Toubro Ltd. |Wipro Ltd. | |Mahindra Mahindra Ltd. |   | Statistical Tool : The Simple Regression Analysis and Multiple Regression Analysis have been done using SPSS to establish the relationship of MVA with EVA, ROCE, RONW, EPS, DPS, and D/E Ratio on Yearly basis. 2. 1- Limitation of Research The following are the limitation of this research: Since, the research has been carried out to find out the important factors which drive shareholders value. So, only the financial ratios which measures performance of the Companies are taken into consideration. Hence, the focus of research is on micro economic factors only. While macroeconomic factors (like GDP, FIIs, and Inflation) also does matter in creating or eroding the value of shareholders. Chapter-3 Research Analysis 3. 1- Different measures used for the analysis In this research our main objective is to find out the factors which investor should look for or take into consideration before buying share of any Company. Now it becomes very much essential to know the correlation between these Variables and the Shareholders value. Hence we have included some of the main variables like RONW, ROCE, D/E Ratio, EPS, DPS, EVA. These are the variables based on which an Investor decide to buy the shares of a particular Company. As depending upon these variables they buy the shares, the market Value of that particular Company increase which results in increase in Shareholder’s Value. Hence Dependent Variable is MVA. (For data please see Appendix-17 p. no. 84) But since MVA is Stock concept so, for applying Regression Analysis change in MVA (Market Value Added) with respect to previous year has been used. MVA=Market Capitalization-Investment (Book Value) Change in MVA= (MVAt – MVAt-1 )/MVAt-1)x100 Here, MVAt= MVA of the Companies of Proceeding Year. MVAt-1= MVA of the Companies of preceding Year Independent Variables are: six performance measures are considered as Independent Variable RONW, ROCE, D/E Ratio, EPS, DPS EVA. All these variables are flow variables. 1) Return on Net Worth (RONW) =NOPAT/Total NETWORTH NETWORTH=EQUITY+RESERVE SURPLUSES Return on Networth measures a company’s earnings in relation to all of the Investor’s it is using. RONW tells us what earnings were generated from the Networth. The Networth of the company comprises both equity and reserve and Surpluses. These types of financing are used to fund the operations of the company. The RONW figure gives investors an idea as to how effectively the company is converting the money it has into net income. (For data please see appendix-3, p. no-70) 2) Return on Capital Employed (ROCE) = EBIT/(NET Worth+Debt) Return on Capital Employed (ROCE) is a measure of the returns that a company is realizing from its capital. It calculates as profit before interest and tax divided by the difference between total assets and current liabilities. The resulting ratio represents the efficiency with which capital is being utilized to generate revenue. (For data please see appendix-4, p. no. 71) 3)D/E Ratio=Total Debt /Total Equity D/E Ratio gives the idea about the Capital structure of the Company. It shows how risky is the Investment in a Company. On the basis of D/E ratio we can have an idea of the fixed liabilities of the Company if it is using more of Debt. (For data please see appendix-5, p. no. 72) 3) Earning per Share (EPS) = PAT/The Number of Equity Shares Earning per Share is the portion of a company’s profit allocated to each outstanding share of common stock. Earning per Share as the name indicates, is the â€Å"per share earning† of a company in a reported period. This is the most important factor in the fundamental analysis of a stock. This coupled with a few related ratios gives a fair idea about the worth of a stock. (For data please see appendix-6 p. no. 73) 4) DPS is the Dividend allotted to each share holders (For data please see appendix-7. P. No. 74) 5) Economic Value Added (EVA) = NOPAT-(Cost of Equity x Networth) EVA attempts to measure how much `value’ was created by an organization for its shareholders, during an accounting period. It is defined as the excess of a company’s after tax net operating profit over the required minimum rate of return that the investors and lenders could get by investing in other securities of comparable risk. For data see appendix-15, p. no. 82) For Calculating Cost of Equity CAPM (Capital Assets Pricing Model has been used) Ke=Cost of Equity =Rf +? *(Rm-Rf) (For data please see appendix-13, p. no. 80) Rf =Yearly Risk free Rate of Return=6% (The yield of Treasury Bill has been taken as risk free rate of return which is around 6% for the period of 2003-2008) Rm=Yearly Sensex Rate of Return=17% (Average from 1995 to 2007 comes out to be 17% – please see appendix-1, p. no. 68) ? = Beta Value of a particular Stock of a Company E= Total Networth (Equity+ Reserve and Surpluses) Since, the value of ? shows the riskiness of a particular stock with respect to market. This ? value shows riskiness on the basis of book value of a particular Company. So, these ? values of the Companies are converted into Unlevered ? and then Levered ? based on Company’s present Market Capitalization, so that an accurate and present riskiness of the stock of Company can be taken into consideration for the research The Formula is: Unlevered ? ju= ? /1+(D/S)(1-T) Here, D=Total Debt used by the Company S=Total Equity used by the Company (Book Value) T= Corporate Tax Rate (30%) Now Calculating Levered ? based on Market Capitalization using unlevered ? Levered ? = ? ju x (1+(D/S)x(1-T) (For data Please see appendix. -12, p. no. 79) Here, S=Present Market Capitalization of the Company. For an accurate result, the change in MVA and EVA has been standardized by dividing them by the Net worth of the respective Company. Standardization is done in order to find the relative value of EVA and MVA over the Net worth used by the Company. (For Stdz. EVA and Stdz. MVA please see Appendix-16, p. no. 83 and Appendix-19 p. no. 86) The collected and calculated data of ll the variables are attached in Appendices. Please see appendices for detail list. 3. 2-Regression Analysis The analysis is done in two parts. Firstly, simple regression analysis has been done between Dependent Variable (Stdz. MVA) and Independent variables (RONW, ROCE, D/E Ratio, EPS, DPS and Stdz. Eva) taking one Independent Variable at a time for all the Years (2004-2008). Year 2003 has been considered as a base year for the Year 2004 to get change in MVA in 2004 and the same process has been used to calculate change in MVA till 2008. This Simple Regression analysis has been performed in order to understand the key variables which are having high degree of correlation with MVA. After analyzing the key variables, multiple regressions Analysis have been applied with the key variables in order to analyze the impact of key Independent Variables together on Change in MVA. 3. -Year wise Result of Simple Regression Analysis from 2004 to 2008 Result of the Year 2004: | |RONW |ROCE |D/E Ratio |EPS |DPS |Stdz. EVA | |R |. 771 |. 817 |. 089 |. 332 |. 201 |. 851 | |R2 |. 594 |. 667 |. 008 |. 110 |. 040 |. 724 | |Adjusted R2 |. 575 |. 652 |-. 039 |. 068 |-. 005 |. 711 | |Standard error of Estimate |2. 47868 |2. 4331 |3. 87455 |3. 66887 |3. 81084 |2. 04402 | |Significance |. 000 |. 000 |. 686 |. 121 |. 358 |. 000 | |p-Value | | | | | | | Interpretation: From this table, it can be observed that Change in MVA is positively related with all the financial indicators but only three variable ROCE, RONW and EVA are highly correlated with change in MVA. The coefficient of correlation of change in MVA with RONW, ROCE and EVA is 0. 771, 0. 817 and 0. 51 respectively; moreover the p-value (significance) is also less than . 001. So, at 99% confidence level we can say that, these three variables significantly explain the variation in MVA. This shows that all these three variables are very much important from investment point of view. The coefficient of determination (Adjusted R2) Of Change in MVA with RONW is . 594 which means change in RONW explains 59. 4% of variation in MVA, while with ROCE it is . 652 which means change in RONW explains 65. 2% of variation in MVA and with EVA it is . 711, which means ch ange in EVA explains 71. 1% variation in MVA, the most important driver of change in MVA. So, the simple regression analysis for this year shows that these three variables are very much important while EVA is the most important variable to consider before investment. While, EPS explains 6. 8% of variation in MVA but p value is more than . 000 and DPS has a very little bit of significance and D/E Ratio is insignificant to consider as a driver of Shareholders’ value. Result of the Year 2005: | |RONW |ROCE |D/E Ratio |EPS |DPS |Stdz. EVA | |R |. 578 |. 543 |. 046 |. 323 |. 289 |. 96 | |R2 |. 334 |. 295 |. 002 |. 104 |. 084 |. 484 | |Adjusted R2 |. 303 |. 262 |-. 045 |. 061 |. 040 |. 459 | |Standard error of |2. 73331 |2. 81226 |3. 34638 |3. 17073 |3. 20682 |2. 40672 | |Estimate | | | | | | | |Significance |. 004 |. 007 |. 835 |. 133 |. 181 |. 00 | |p-Value | | | | | | | Interpretation: According to the result we can see that correlation of Change in MVA with Stdz. EVA is the highest (. 696) then with RONW (. 578) and then with ROCE (. 543) for this year. The adjusted R2 of Change in MVA with EVA, RONW and ROCE is . 459, . 303 and . 262 respectively which shows highest variation in MVA is explained by EVA that is 45. 9%. Moreover, the significance level lies between . 005 to . 010 which is less than . 010. Hence, these three variables are the most important variables to consider as standard error is also very low in comparison to other variable. While EPS and DPS has a very little significance and D/E Ratio is insignificant to consider. Result of the Year 2006 | |RONW |ROCE |D/E Ratio |EPS |DPS |Stdz. EVA | |R |. 725 |. 638 |. 025 |. 450 |. 054 |. 801 | |R2 |. 525 |. 407 |. 001 |. 203 |. 003 |. 641 | |Adjusted R2 |. 503 |. 379 |-. 047 |. 165 |-. 045 |. 624 | |Standard error of |2. 60034 |2. 0531 |3. 77240 |3. 36897 |3. 76802 |2. 25987 | |Estimate | | | | | | | |Significance |. 000 |. 001 |. 910 |. 031 |. 806 |. 000 | |p-Value | | | | | | | Interpretation: According to the result we can see that correlation of Change in MVA with EVA is the highest (. 801) then with RONW (. 725) and then with ROCE (. 638) for this year. The adjusted R2 of Change in MVA with EVA, RONW and ROCE is . 624, . 503 and . 379 respectively which again shows the highest variation in MVA is explained by EVA that is 62. 4% . Moreover, the significance level is also . 000 which is less than . 001. Hence, at 99% confidence level we can say that these three variables are the most important variables to consider and again EPS and DPS has a very little significance. D/E Ratio is again insignificant to consider Result of the Year 2007: | |RONW |ROCE |D/E Ratio |EPS |DPS |Stdz. EVA | |R |. 801 |. 795 |. 075 |. 66 |. 275 |. 896 | |R2 |. 641 |. 632 |. 006 |. 134 |. 075 |. 802 | |Adjusted R2 |. 624 |. 615 |-. 042 |. 092 |. 031 |. 793 | |Standard error of |2. 37916 |2. 40757 |3. 95859 |3. 69485 |3. 81708 |1. 76595 | |Estimate | | | | | | | |Significance |. 000 |. 000 |. 34 |. 086 |. 205 |. 000 | |p-Value | | | | | | | Interpretation: According to the result we can see that correlation of Change in MVA with EVA is again highest (. 896) the n with RONW (. 801) then with RONW (. 795) for this year. The adjusted R2 of Change in MVA with EVA, RONW and ROCE is . 793, . 624 and . 615 respectively which again shows highest variation in MVA is explained by EVA that is 79. 3% . Moreover, the significance level is also . 000 in each of three cases which is less than . 001. Hence, out of all variables these three variables are the most important variables out of these three variables EVA is coming out to be the most important variable to consider while EPS and DPS are again having a little Significance and D/E Ratio is insignificant to consider as the driver of shareholders’ value. Result of the Year 2008: | |RONW |ROCE |D/E Ratio |EPS |DPS |Stdz. EVA | |R |. 900 |. 890 |. 152 |. 252 |. 259 |. 950 | |R2 |. 811 |. 793 |. 023 |. 063 |. 067 |. 903 | |Adjusted R2 |. 802 |. 783 |-. 034 |. 019 |. 023 |. 99 | |Standard error of Estimate |2. 70932 |2. 83630 |6. 15528 |6. 02644 |6. 01501 |1. 93688 | |Significance |. 000 |. 000 |. 490 |. 246 |. 233 |. 000 | |p-Value | | | | | | | Interpretation: According to the result we can see that correlation of MVA with EVA is the highest (. 950) then with RONW (. 900) and then with ROCE (. 890) for this year. The adjusted R2 of Change in MVA with EVA, RONW and ROCE is . 899, . 802 and . 83 respectively which shows hi ghest variation in MVA is explained by EVA that is 89. 9%. Moreover, the significance level is also . 000 which is less than . 001. Hence, these three variables are the most important variables to consider while EPS and DPS are again having a little Significance and D/E Ratio is again insignificant to consider as the driver of shareholders’ value as significance is more than . 001. Moreover out of these three, EVA is the most important and powerful variable. 3. 4-Overall Result of Simple Regression analysis [pic] Over all Result of the Analysis: Hence, my over all Analysis shows that only three financial Indicators (EVA, RONW, and ROCE) are the important driver of shareholders’ value. Out of these three, EVA is the most important Indicator. So, if a company is earning more than its cost of capital, it is adding more value to the shareholders. In second, RONW is the important Indicator, which shows that companies utilizing its shareholders’ funds in an effective efficient manner are adding value to shareholders. Third important Indicator is ROCE which shows Companies generating higher ROCE will add value to Shareholders. SO, before investment these three variables must be considered. 3. 5- Year wise Result of Multiple Regression Analysis In multiple regression analysis I found that there is multi collinearity exist between ROCE, RONW and EVA. So, I applied multiple regression taking three independent variables at a time excluding the variables like D/E Ratio because this variable is insignificant to consider while EPS and DPS is little bit of significant. So to come out with a strong and accurate analysis it is irrelevant to ignore these two variables. Result -1: In Result-1 analysis between Change in MVA with EPS, DPS and RONW has been observed for the period of 2004 to 2008. EPS and DPS have been taken because of their little significance. In this case Hypothesis is as follows: H0: EPS, DPS, RONW are not significantly explaining variation in MVA H1: EPS, DPS, RONW are significantly explaining variation in MVA Result of the Year 2004 Model Summary |Model |R |R Square |Adjusted R |Std. Error of the | | | | |Square |Estimate | |1 |. 834(a) |. 696 |. 647 |2. 25663 | a Predictors: (Constant), RONW, EPS, DPS ANOVA(b) |Model | |Sum of Squares |df |Mean Square |F | |1 |. 742(a) |. 550 |. 479 |2. 36227 |2. 192 | a Predictors: (Constant), RONW, DPS, EPS b Dependent Variable: Stdz. MVA ANOVA(b) |Model | |Sum of Squares |df |Mean Square |F | |1 |. 831(a) |. 691 |. 642 |2. 20440 |2. 115 | Predictors: (Constant), DPS, RONW, EPS b Dependent Variable: Stdz. MVA ANOVA(b) |Model | |Sum of Squares |df |Mean Square |F | |1 |. 860(a) |. 739 |. 698 |2. 13011 |2. 501 | a Predictors: (Constant), RONW, EPS, DPS b Dependent Variable: Stdz. MVA ANOVA(b) |Model | |Sum of Squares |df |Mean Square |F | |1 |. 927(a) |. 859 |. 837 |2. 45654 |2. 493 | a Predictors: (Constant), RONW, EPS, DPS b Dependent Variable: Stdz. MVA ANOVA(b) Model | |Sum of Squares |df |Mean Square | |1 |. 875(a) |. 765 |. 728 |1. 98181 | a Predictors: (Constant), ROCE, EPS, DPS ANOVA(b) |Model | |Sum of Squares |df |Mean Square |F | |1 |. 728(a) |. 530 |. 455 |2. 41542 |1. 856 | a Predictors: (Constant), ROCE, DPS, EPS b Dependent Variable: Stdz. MVA ANOVA(b) |Model | |Sum of Squares |Df |Mean Square |F | |1 |. 764(a) |. 584 |. 519 |2. 55798 |1. 913 | Predictors: (Constant), ROCE, EPS, DPS b Dependent Variable: Stdz. MVA ANOVA(b) |Model | |Sum of Squares |df |Mean Square |F | |1 |. 850(a) |. 722 |. 678 |2. 19925 |1. 759 | a Predictors: (Constant), ROCE, EPS, DPS b Dependent Variable: Stdz. MVA ANOVA(b) |Model | |Sum of Squares |df |Mean Square |F | |1 |. 918(a) |. 843 |. 818 |2. 59745 |2. 016 | a Predictors: (Constant), ROCE, EPS, DPS b Dependent Variable: Stdz. MVA ANOVA(b) |Model | |Sum of Squares |df |Mean Square | |1 |. 888(a) |. 788 |. 754 |1. 88405 | a Predictors: (Constant), Stdz. EVA, DPS, EPS ANOVA(b) |Model | |Sum of Squares |df |Mean Square |F | |1 |. 824(a) |. 679 |. 628 |1. 99688 |2. 328 | a Predictors: (Constant), Stdz. EVA, EPS, DPS b Dependent Variable: Stdz. MVA ANOVA(b) |Model | |Sum of Squares |df |Mean Square |F | |1 |. 89(a) |. 790 |. 757 |1. 81740 |1. 914 | a Predictors: (Constant), Stdz. EVA, EPS, DPS b Dependent Variable: Stdz. MVA ANOVA(b) |Model | |Sum of Squares |df |Mean Square |F | |1 |. 923(a) |. 852 |. 829 |1. 60570 |2. 184 | a Predictors: (Constant), Stdz. EVA, DPS, EPS b Dependent Variable: Stdz. MVA ANOVA(b) |Model | |Sum of Squares |df |Mean Square |F | |1 |. 966(a) |. 932 |. 922 |1. 0292 |2. 103 | a Predictors: (Constant), Stdz. EVA, EPS, DPS b Dependent Variable: Stdz. MVA ANOVA(b) Model | |Sum of Squares |df |Mean Square |F |Sig. | |1 |Regression |759. 238 |3 |253. 079 |87. 270 |. 000(a) | | |Residual |5 5. 099 |19 |2. 900 | | | | |Total |814. 337 |22 | | | | |a Predictors: (Constant), Stdz. EVA, EPS, DPS b Dependent Variable: Stdz. MVA Coefficients(a) Model | |Unstandardized Coefficients |Standardized Coefficients | | |Collinearity Statistics | | | |B |Std. Error |Beta |t |Sig. |Tolerance |VIF | |1 |(Constant) |3. 899 |. 674 | |5. 789 |. 000 | | | | |EPS |-. 017 |. 010 |-. 147 |-1. 92 |. 089 |. 530 |1. 886 | | |DPS |-. 026 |. 065 |-. 033 |-. 405 |. 690 |. 525 |1. 905 | | |Stdz. EVA |21. 424 |1. 383 |. 933 |15. 494 |. 000 |. 982 |1. 018 | |a Dependent Variable: Stdz. MVA Interpretation: The model Summary shows that EPS, DPS and EVA together having a high degree of correlation that is . 966 with change in MVA while variation in these three variables together explains 92. 2% of variation in MVA as adjusted R2 is 0. 922. The Durbin-Watson is 2. 103 which show that variables are following a similar trend and are not scattered. The analysis shows that this year these three variables are very strongly related with Change in MVA. The ANOVA table shows F is 87. 270 and significance is . 000 which is less than . 001. It means Reject H0 and accept H1. Hence, these three variables significantly explain the variation in MVA and are very much important to consider. The coefficient table shows that there is no multi collinearity exists between independent variables because Tolerance is greater than 0. 2 and VIF is less than 5. It also shows that beta value of EVA is . 933. So, EVA is the most power full variable over here. Overall Result: The analysis of all the years results in rejection of H0 and acceptance of H1. It means these three variables are also significantly explaining variation in MVA. 3. -Overall Result of Multiple Regression Analysis Since, because of multi colinearity between RONW, ROCE and EVA it was not possible to include these three variables together in the multiple regression analysis. But as they are correlated with each other, so we can consider any one of them with the other variables to reac h at a conclusive result. Now after analyzing multiple regressions with three sets of independent variable with dependent variable which are: Set-1 Change in MVA with EPS, DPS, RONW Set-2 Change in MVA with EPS, DPS, ROCE Set-3 Change in MVA with EPS, DPS, EVA The question comes in our mind is which set is to be given preference over other. Though all the sets are highly correlated with change in MVA and there is a little bit of variation in their correlation we can consider any one set out of the three. But to conclude the analysis a Year wise Comparison has been done with the help of following graph between the three sets: [pic] From the graph we can see that correlation of Change in MVA with EPS, DPS and EVA was the highest throughout the Years. Moreover it is also increasing year by year. So, it is very much useful to consider as these three variables together act as a most important driver of shareholders’ value. While the second most important set to consider is EPS, DPS and RONW and then EPS, DPS and ROCE. Recommendations Since, my research analysis has shown that there are three most important factors EVA, RONW and ROCE which drive the shareholders’ value. Moreover a combination of EPS, DPS, and EVA together causes major variation in shareholders’ value. So, Research division of IL should focus on these factors because companies generating higher ROCE, RONW, and EVA from their business will add more value to the Shareholders’ investment. Now a day, it has become very much important for the Brokerage firms to provide valuable services to their customers specially a proper guide line that where they should invest and where they should not in order to bit the Competitors and retain customers with themselves. So, research division of IL Investsmart should guide the investors to invest in the shares of those companies which is earning more than cost of capital that is company with positive EVA moreover the companies which is effectively using the Owners fund means generating higher RONW and a higher ROCE. EPS, and DPS can be taken into consideration but can be avoided also if company is to good in generating positive EVA and higher RONW and ROCE because these variables indicate the growth of an organization. If the organization is growing and its not giving any dividend still it is good to invest in that Company, as the growth company will leads to increase in Market value and this will result in increase in Shareholders’ value. Conclusion At the end I would conclude that the year wise research done over the period of five years from 2004 to 2008 has shows that EVA is the most important driver of shareholders’ value as the correlation between EVA and change in MVA is very strong. so, a company generating positive and higher EVA is the best option to invest in because this will result in increase in market value which will result in increase in shareholders’ value. The second most important variable RONW and the third most important variable ROCE should be consider before investing in the share of any company because these two variables are also having a high degree of correlation with change in MVA. EPS and DPS alone are not the important factor to consider individually. But the combination of EPS, DPS and EVA together are highly correlated with change in MVA. According to my research analysis in 2008 it was found that these three variables together have explained 92. 2% of variation in MVA. So, the combination of these three variable can also be taken into consider before selecting a company to invest in. The analysis also shows that Correlation of change in MVA has been found to be increasing year by Year from 2004 to 2008. So, for future investment it is better to look into these ratios before investing in any company. The regression analysis shows strong correlation of change in MVA with EVA, RONW and ROCE, which is not a surprise since shareholders should value an enterprise, based on the return what they are getting on their invested oney, which proves that it doesn’t matter whether the company retains or distributed its earnings, so long it is being utilized for productive purposes. Bibliography o CMIE’S Prowess o http://www. bseindia. com/about/abindices/bse30. asp o http://www. bseindia. com/about/abindices/betavalue s. asp o http://www. bseindia. com/histdata/hindices. asp o http://neeravnagar. blogspot. com/2007/08/drivers-of-shareholders-value. html o Ali M Ghanbari (Jul’07) â€Å"The Relationship between Economic Value Added and Market Value Added: An Empirical Analysis in Indian Automobile Industry† The ICFAI Journals of Accounting Research. o Chapter 32 of â€Å"Investment Valuation† by Aswath Damodaran ———————– 65 How to cite A Study of Factors Driving Shareholders’ Value, Essay examples