Money Is Personal Best Friend

Money is personal best friend â€Å"Man is nothing else but what he makes of himself. † ~ Jean Paul Sartre everyone uses money. We all want it, work for it and think about it. If you don't know what money is, you are not like most humans. However, the task of defining what money is, where it comes from and what it's worth belongs to those who dedicate themselves to the discipline of economics. While the creation and growth of money seems somewhat intangible, money is the way we get the things we need and want. Here we look at the multifaceted characteristics of money.This investment vehicle is often the perfect stop-gap measure for growing your money. Before the development of a medium of exchange, people would barter to obtain the goods and services they needed. This is basically how it worked: two individuals each possessing a commodity the other wanted or needed would enter into an agreement to trade their goods. †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ Money is not the only measure of succes s in life For most people in our modern-capitalism world, money is the first thing, and sometimes the only thing that measures success in their life.Money can buy power. Money can buy fame. Money can buy time. Sometimes money can even buy a life. So money has become the first common goal for everybody. However, there is something else that can be the measurement of success in life. One important thing that defines success in our lives is our careers. With different careers, we will have different goals and measurements of our success. If you are an athlete, your success can be measured by lots of things such as wining tournaments, breaking records, playing in the top league or competing in the Olympics.If you are a scientist, the success in your life will mostly depend on your researches. One line of formula that can prove your theory is true can be the great success in your life. If you are a writer, you will probably succeed if your book gets on the best-selling shelf in the books tore or if your works get published in a magazine. If you are a musician, playing in the country’s orchestra or a number of your CDs that have been sold can be counted as the measurement of your success. So we can see that another measurement of success in your life besides money is how much you have succeeded in your career.Success in human life is often measured by numbers. Therefore, everything that can be counted can be used as a measurement. Again, these measurements vary with each career. If you are an athlete maybe it is the number of tournaments or the number of matches you have won. But if you are a writer, it is probably the number of your books that have been sold out or if you are a musician it might be the number of your CDs. Sometimes it could be even a silly thing like the number of girlfriends you have. In fact, it is human nature that we always want to compare ourselves to others.In almost every career there is money involved, and maybe that is the reason why people always look at money as primary measurement of their success – they can easily compare it with the others. The other measurement of success in our lives is awards. For the scientists one of the greatest accomplishments in their life is to win a Nobel Prize, for the writers there is the Pulitzer award, for actors and actresses there is an academy award, for musicians a Grammy’s and so on. These things are different from the other measurements in that it is judged by other people not ourselves.Besides those measurements we can compare them with other people or let other people measure our success, sometimes just accomplishing our wishes or dreams can be measures of success in our lives. So it does not matter that you have won the world championships or just the tournament in your school, working in the biggest company in the country or just set up your small company. If it is your dream, you have already succeeded in your life. The common goal in people’s life is to be peaceful and happy.Being the richest man in the world does not mean you are the happiest man in the world, although money can buy you happiness sometimes, but not always. So another measurement of success you are in your is how happy your life is. It might be having a warm family and children, having lots of friends or maybe just having someone who truly loves you. In addition, as times change, the measurement of success in life also changes. For examples, wealth, thing that many people use to judge success in one’s life, is changing when time has passed.Thousands of years ago when humans were still a cavemen, it might have been how big you cave was. Hundreds of years ago it might have been how big your land was. Today it is how much money is in your bank account, so maybe in the future it might be something else. In conclusion, the measurement of success in your live can be many things. It just depends on time, what goals you have and who is the one doing the j udging Money is just the first common, one but it is not the only one †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦..Our living conditions  are gradually improving and  thus  our need  of money  for daily life is on the increase. It is obvious that money is very  helpful  to us, but in addition to  its  benefits, there are many downsides that it has brought  into life. Firstly, money brings about a lot of  advantages as seen from the fact. We use it to equip our houses with furniture, to buy food, clothes and all other things we need to live comfortably. Money is an essential precondition for every student to go to school and for every patient to see a doctor.Moreover, we can use it for charity to help  compensate somewhat our  unfortunate feeling / sense of duty  by making material / financial  contributions. Without money, our living conditions  would be  penurious  / poor,  and  very hard to meet the minimum requirements  / basic needs  in life, which is simply eating or drinking. Apart from those advantages,  money has brought us  numerous downsides. First of all, it makes us grasping. Money is  undoubtedly  very valuable in life, the more money we earn, the more materials we have.Therefore, people always try their best to earn as much money as possible due to their desire to have a better life. Some, however, do not. They want more money simply because of their rivalries. They wish to be as wealthy as millionaires or billionaires  without working for it. This  undeliberat  / unreasonable attitude to life is contrary to  morality. Secondly, money rules over  evil-doing  behaviours  / evil deeds. Earning money legitimately is a difficult task, but some people want it to be an easy one by  committing  conscienceless  / irresponsible  acts which are likely to be robbing or murdering.They are not alive to the fact that such behaviours can end them up in prison. / The road of their lives is certain ly not to become criminal and end up in prison. In  other  words, whether money does  us  good or harm  it  depends greatly on our perception of it. With money in our hands, we should let it be our servants, not our masters in order to build a better and better world where no crimes exist. / We should consider the money in our hands to be our servant, not our master in order to build an increasingly better world without crimes.

American war for independence II

The American war for Independence was a new revolution that was relevant for various political and social reasons. Due to the changes caused by such revolution, the changes that happened during this event greatly changed the course of American History. In order to fully understand the radical nature of the American Revolution, it is first important to briefly discuss the events during this period. Occurring in the latter half of the 18th century, the American Revolution was the event that allowed the Thirteen (13) Colonies to become the United States of America.This was also the event that finally granted these colonies the independence that they desired from the British Empire. The American War of Independence or Revolutionary War that ensued from 1775 to 1783 was largely radical in nature because of the fact that it was the first instance that a colony had tried and succeeded to gain independence from the British Empire. One of the key developments resulted from this revolution was the birth and growth of enlightenment philosophy in America.The influence of this wave of thinking created a certain sect that was opposed to an absolute monarchy, such as that of the British Empire, and instead embraced a new form of government that was revolutionary for its time. The broad intellectual and social paradigm shifts within the colonies introduced new ideas with regard to republican ideals that began to take hold among the members of the colonies. Democracy soon began to play a larger role in the determination of the new government that was going to replace the absolute monarchy.The steadily expanding role of democracy in government caused the deterioration of traditional social hierarchies that existed. Instead a new ethic was created within the core of American Political values. While the United States was arguably not the first democracy to ever exist, the neo-classical model that arose during this time was largely unheard of in other parts of the world, particular ly from a British Colony.These social and political changes greatly impacted the way that the world saw the Americas from that point on. As the birthplace of modern democracy as the world now recognizes it, the United States of America soon became the symbol of world democracy. The birth of new ideas with regard to government, representation and social class were all altered by this event. It was so influential that it became the foundation of the American Constitution and the reason why the United States will always be known as the land of the free.

Bhojraj Lee Paper

Accounting Research Center, Booth School of Business, University of Chicago Who Is My Peer? A Valuation-Based Approach to the Selection of Comparable Firms Author(s): Sanjeev Bhojraj and Charles M. C. Lee Source: Journal of Accounting Research, Vol. 40, No. 2, Studies on Accounting, Entrepreneurship and E-Commerce (May, 2002), pp. 407-439 Published by: Blackwell Publishing on behalf of Accounting Research Center, Booth School of Business, University of Chicago Stable URL: http://www. jstor. org/stable/3542390 . Accessed: 15/01/2011 08:35 Your use of the JSTOR archive indicates your acceptance of JSTORs Terms and Conditions of Use, available at . http://www. jstor. org/page/info/about/policies/terms. jsp. JSTORs Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at . ttp://www. jstor. org/action/showPublisher? publisherCode=black. . Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission. JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact [emailprotected] org. Blackwell Publishing and Accounting Research Center, Booth School of Business, University of Chicago are collaborating with JSTOR to digitize, preserve and extend access to Journal of Accounting Research. http://www. jstor. org Research Journalof Accounting Vol. 40 No. 2 May2002 in Printed U. S. A. Who Is My Peer? A Valuation-Based Approach to the Selection of Comparable Firms SANJEEV BHOJRAJ AND CHARLES M. C. LEE* Received4January2001;accepted4 September2001 ABSTRACT This study presents a general approach for selecting comparable firms in market-based research and equity valuation. Guided by valuation theory, we develop a warrantedmultiple for each firm, and identify peer firms as those having the closest warranted multiple. We test this approach by examining the efficacy of the selected comparable firms in predicting future (one- to three-year-ahead) enterprise-value-to-sales and price-to-book ratios. Our tests encompass the general universe of stocks as well as a sub-population of socalled new economy stocks. We conclude that comparable firms selected in this manner offer sharp improvements over comparable firms selected on the basis of other techniques. 1. Introduction Accounting-based market multiples are easily the most common technique in equity valuation. These multiples are ubiquitous in the reports and recommendations of sell-side financial analysts, and are widely used in *Johnson Graduate School of Management, Cornell University. We thank Bhaskaran Swaminathan, as well as workshop participants at the Australian Graduate School of ManConferagement, Cornell University, Indiana University, the 2001 Journal ofAccountingResearch ence, the 2001 HKUST Summer Symposium, Syracuse University, and an anonymous referee, for helpful comments. The data on analyst earnings forecasts are provided by I/B/E/S International Inc. 407 of of 2002 Copyright University Chicagoon behalfof the Institute Professional Accounting, ? , 408 S. BHOJRAJ C. M. C. LEE AND investment bankers fairness opinions (e. g. , DeAngelo [1990]). They also appear in valuations associated with initial public offerings (IPOs), leveraged buyout transactions, seasoned equity offerings (SEOs), and other merger and acquisition (M) activities. Even advocates of projected discounted cash flow (DCF) valuation methods frequently resort to using market multiples when estimating terminal values. Despite their widespread usage, little theory is available to guide the application of these multiples. With a few exceptions, the accounting and finance literature contains little evidence on how or why certain individual multiples, or certain comparable firms, should be selected in specific contexts. Some practitioners even suggest that the selection of comparable firms is essentially an art form that should be left to professionals. 2 Yet the degree of subjectivityinvolved in their application is discomforting from a scientific perspective. Moreover, the aura of mystique that surrounds this technique limits its coverage in financial analysis courses, and ultimately threatens its credibility as a serious alternative in equity valuation. In this study, we re-examine the theoretical underpinnings for the use of market multiples in equity valuation, and develop a systematic approach for the selection of comparable firms. Our premise is that the popularity of market-based valuation multiples stems from their function as a classic satisficingdevice (Simon [1997]). In using multiples to value firms, analysts forfeit some of the benefits of a more complete, but more complex, pro forma analysis. In exchange, they obtain a convenient valuation heuristic that produces satisfactory results without incurring extensive time and effort costs. In fact, we believe it is possible to compensate for much of the information these multiples fail to capture through the judicious selection of comparable firms. Our aim is to develop a more systematic technique for doing so, through an appeal to valuation theory. Specifically, we argue that the choice of comparable firms should be a function of the variables that drive cross-sectional variation in a given valuation multiple. For example, in the case of the enterprise-value-to-sales multiple, comparable firms should be selected on the basis of variables that drive cross-sectional differences in this ratio, including expected profitability, growth, and the cost-of-capital. 3 In this spirit, we use variables nominated by valuation theory and recent advances in estimating the implied cost-of-capital (i. . , Gebhardt, Lee, and Swaminathan [2001]) to develop a 1 For example, Kim and Ritter [1999] discuss the use of multiples in valuing IPOs. Kaplan and Ruback [1995] examine alternative valuation approaches, including multiples, in highly levered transactions. 2For example, Golz [1986], Woodcock (1992), and McCarthy (1999). We use the enterprise-value-to-sales ratio (EVS) rather than the price-to-sales (PS) ratio because the former is conceptually s uperior when firms are differentially levered (we thank the referee for pointing this out). We also report results for the price-to-book (PB) ratio. We focus on these two ratios because of their applicability to loss firm, which are particularly important among the so-called new economy (tech, biotech, and telecommunication) stocks. However, our approach is general, and can be applied to any of the widely used valuation multiples. WHO IS MYPEER? 409 warrantedmultiple for each firm based on large sample estimations. We then identify a firms peers as those firms having the closest warranted valuation multiple. Our procedures result in two end products. First, we produce warranted multiples for each firmn-that is, a warranted enterprise-value-to-sales (WEVS)and a warranted price-to-book (WPB)ratio. These warranted multiples are based on systematic variations in the observed multiples in crosssection over large samples. The warranted multiples themselves are useful for valuation purposes, because they incorporate the effect of cross-sectional variations in firm growth, profitability, and cost-of-capital. Second, by ranking firms according to their warranted multiples, we generate a list of peer firms for each target firm. For investors and analysts who prefer to conduct equity valuation using market multiples, this approach suggests a more objective method for identifying comparable firms. For researchers, our approach suggests a new technique for selecting control firms, and for isolating a variable of particular interest. Recent methodology studies have demonstrated that characteristic-matched control samples provide more reliable inferences in market-based research (e. . , Barber and Lyon [1997], Lyon et al. [1999]). Our study extends this line of research by presenting a more precise technique for matching sample firms based on characteristics identified by valuation theory. Our approach is designed to accommodate both profitable and loss firms, which have become pervasive in the so called new economy. In short, the methodology developed in this paper can be useful whenever the choice of control firms plays a prominent role in the research design of a market-related study. We test our approach by examining the efficacy of the selected comparable firms in predicting future (one- to three-year-ahead) EVSand PB ratios. 4Our tests encompass the general universe of stocks as well as a sub-population of new economy stocks from the tech, biotech, and telecommunication sectors. Our results show that comparable firms selected in this manner offer sharp improvements over comparable firms selected on the basis of other techniques, including industry and size matches. The improvement is most pronounced among the so-called new economy stocks. The main message from this study is that the choice of comparable firms can be made more systematic and less subjective through the application of valuation theory. In the case of the EVSmultiple, our approach almost triples the adjusted r-squares obtained from using simply industry or industry-size matched selections. The PB multiple is more difficult to predict in general, but our approach still more than doubles the adjusted r-square relative to industry or industry-size matched selections. Interestingly, we find that using the actual multiples from the best comparable firms is generally better than using the warranted multiple itself. Moreover, the choice of comparable 4We forecast future multiples because we do not regard the current stock price as necessarily the best benchmark for assessing valuation accuracy. As discussed later, forecasting future multiples is not equivalent to forecasting future prices or returns. 410 s. BHOJRAJAND C. M. C. LEE firms is, to some extent, dependent on the market multiple under consideration-the best firms for the EVSratio are not necessarily the best firms for the PB ratio. While we illustrate our approach using these two ratios, this technique can be generalized to other common market multiples, including: EBITDA/TEV, E/P, CF/P, and others. In the next section, we further motivate our study and discuss its relation to the existing literature. In section 3, we develop the theory that underpins our analysis. In section 4, we discuss sample selection, research design and estimation procedures. Section 5 reports our empirical results, and section 6 concludes with a discussion of the implications of our findings. . Motivationand Relationto PriorLiterature There are at least three situations in which comparable firms are useful. First, in conducting fundamental analysis, we often need to make forecasts of sales growth rates, profit margins, and asset efficiency ratios. In these settings, we typically appeal to comparable firms from the same industry as a source of reference. Second, in multiples-based valuation, the market multiples of comparable firms are u sed to infer the market value of the target firm. Third, in empirical research, academics seek out comparable firms as a research design device for isolating a variable of particular interest. Our paper is focused primarily on the second and third needs for comparable firms. 5 Given their widespread popularity among practitioners, market multiples based valuation has been the subject of surprisingly few academic studies. Three recent studies that provide some insights on this topic are Kim and Ritter (KR;[1999]), Liu, Nissim, and Thomas (LNT; [1999]), and Baker and Ruback (BR; [1999]). All three examine the relative accuracy of alternative multiples in different settings. KR uses alternative multiples to value initial public offers (IPOs), while LNT and BR investigate the more general context of valuation accuracy relative to current stock prices. KRand LNT both find that forward earnings perform much better than historical earnings. LNT shows that in terms of accuracy relative to current prices, the performance of forward earnings is followed by that of historical earnings measures, cash flow measures, book value, and finally, sales. In addition, Baker and Ruback [1999] discuss the advantages of using harmonic means-that is, the inverse of the average of inversed ratios-when aggregating common market multiples. None of these studies address the choice of comparable firms beyond noting the usefulness of industry groupings. 5 Our technique is not directly relevant to the first situation, because it does not match firms on the basis of a single attribute (such as sales growth, or profit margin). Instead, our approach matches firms on the basis of a set of variables suggested by valuation theory. Our paper also does not address the trivial case whereby a firm is its own comparable. As we point out later, in multiples-based valuation of public firms, a firms own lagged multiple is often the most useful empirical proxy for its current multiple. WHO IS MYPEER? 411 Closer to this study are three prior studies that either investigate the effect of comparable firm selection on multiple-based valuation, or examine the determinants cross-sectional variations in certain multiples. Boatsman and Baskin [1981] compare the accuracy of value estimated based on earningsto-price (EP) multiples of firms from the same industry. They find that, relative to randomly chosen firms, valuation errors are smaller when comparable firms are matched on the basis of historical earnings growth. Similarly, Zarowin [1990] examines the cross-sectional determinants of EPratios. He shows forecasted growth in long-term earnings is a dominant source of variation in these ratios. Other factors, such as risk, historical earnings growth, forecasted short-term growth, and differences in accounting methods, seem to be less important. Finally,Alford [1992] examines the relative valuation accuracy of EPmultiples when comparable firms are selected on the basis of industry, size, leverage, and earnings growth. He finds that valuation errors decline when the industry definition used to select comparable firms is narrowed to twoor three-digit SIC codes, but that there is no further improvement when a four-digit classification is used. He also finds that after controlling for industry membership, further controls for firm size, leverage, and earnings growth do not reduce valuation errors. Several stylized facts emerge from these studies. First, the choice of which multiple to use affects accuracy results. In terms of accuracy relative to current prices, forecasted earnings perform relativelywell (KR,LNT); the priceto-sales and price-to-book ratios perform relatively poorly (LNT). Second, industry membership is important in selecting comparable firms (Alford [1992], LNT, KR). The relation between historical growth rates and EP ratios is unclear, with studies reporting conflicting results (Zarowin [1999], Alford [1992], Boatsman and Baskin [1981]), but forecasted growth rates are important (Zarowin [1999]). Other measures, including risk-basedmetrics (leverage and size) do not seem to provide much additional explanatory power for E/P ratios. Our study is distinct from these prior studies in several respects. First, our approach is more general, and relies more heavily on valuation theory. This theory guides us in developing a regression model that estimates a warranted multiple for each firm. We then define a firms peers as those firms with the closest warranted market multiple to the target firm, as identified by our model. The advantage of a regression-based approach is that it allows us to simultaneously control for the effect of various explanatory variables. For example, some firms might have higher current profitability, but lower future growth prospects, and higher cost-of-capital. This approach allows us to consider the simultaneous effect of all these variables, and to place appropriate weights on each variable based on empirical relations established in large samples. Our empirical results illustrate the advantage of this approach. Contrary to the mixed results in prior studies, we find that factors related to profitability, growth, and risk, are strongly and consistently correlated with the EVS 412 S. BHOJRAJ C. M. C. LEE AND and PB ratios. Collectively, factors that relate to profitability, growth, and risk, play an important role in explaining cross-sectional variations of these multiples. In fact, we find that variables related to firm-specific profitability, forecasted growth and risk are more important than industry membership and firm size in explaining a firms future EVSand PB ratios. Second, we employ recent advances in the empirical estimation of cost-ofcapital (i. e. , Gebhardt et al. [2001]) to help identify potential explanatory variables for estimating our model of warranted market multiples. The risk metrics examined in prior studies are relatively simple, and the results are mixed. We follow the technique in Gebhardt et al. [2001] to secure additional explanatory variables that are associated with cross-sectional determinants of a firms implied cost-of-capital. Several of these factors turn out to be important in explaining EVSand PB ratios. Third, we do not assume that the current stock price of a firm is the best estimate of firm value. Prior studies compare the valuation derived by the multiples to a stocks current price to determine the valuation error. In effect, these studies assume that the current stock price is the appropriate normative benchmark by which to judge a multiples performance. Under this assumption, it is impossible to derive an independent valuation using multiples that is useful for identifying over- or under-valued stocks. Our less stringent assumption of market efficiency is that a firms current price is a noisy proxy for the true, but unobservable intrinsic value, defined as the present value of expected dividends. Moreover, due to arbitrage, price converges to value over time. As a result, price and various alternative estimates of value based on accounting fundamentals will be co-integrated over time. 6 Under this assumption, we estimate a warrantedmultiple that differs from the actual multiple implicit in the current price. Consistent with this philosophy, we test the efficacy of alternative estimated multiples by comparing their predictive power for a firms future multiples (e. g. , its one-, two-, or three-year-ahead EVSand PB ratios). Finally,our approach can be broadly applied to loss firms, including many new economy stocks. Prior studies that examine comparable firms (e. g. , Alford [1992], Boatsman and Baskin [1981], and Zarowin [1999]) focus solely on the EP ratio. A limitation of these studies is that they do not pertain to loss firms. This limitation has become more acute in recent years, as many technology, biotechnology, and telecommunication firms have reported negative earnings. 6 For a more formal statistical model of this co-integrated relationship between price and alternative estimates of fundamental value, see, Lee, Myers, and Swaminathan [1999]. 7 Note that forecasting future multiples is different from forecasting future prices or returns. In the current context, forecasting future price involves two steps: forecasting future multiples, and forecasting future fundamentals (e. g. , sales or book value per share). Our main interest is in the stability of the multiples relation, and not in forecasting fundamentals. An example of fundamental analysis that focuses on forecasting future fundamentals is Ou and Penman [1989]. WHO IS MY PEER? 413 Appendix A provides an indication of the magnitude of the problem. This appendix reports descriptive statistics for a sample of 3,515 firms from NYSE/AMEX/NASDAQ as of 5/29/2000. To be included, a firm must be U. S. domiciled (i. e. , not an ADR), have a market capitalization of over $100 million, and fundamental data for the trailing 12 months (i. . , not a recent IPO). Based on aggregate net income from the most recent four quarters, we divide the sample into profitable firms (78% of sample) and loss firms (22% of sample). Panel A reports the percentage of these firms that have positive EBIT,Operating Income, EBITDA, Gross Margin, Sales, One-year-ahead forecasted earnings (FY1), and book value. This panel shows that only 40% of the loss firms have positive operating income, only 47% have positive EBITDA, and only 34% have positive FY1forecasts. In fact, only 87% of the loss firms have positive gross margins. The only reliably positive accounting measures are sales (100%) and book value (94%). Clearly, these loss firms are difficult to value. However, they are also difficult to ignore. Panel B reports the distribution of realized returns in the past six months (11/31/99 to 5/29/00) separately for the profit firms and loss firms. The returns for the loss firms have higher mean (19. 6% versus 7. 8%), higher standard deviation (111. 3% versus 42. 3%), and fatter tails. As a group, the loss firms appear to be a high-stake game that constitutes a substantial proportion of the universe of traded stocks in the United States. Our study uses the two most reliably positive multiples (EVSand PB). Liu, Nissim, and Thomas [1999] show that these two ratios are relatively poor performers in terms of their valuation accuracy. We demonstrate that by choosing an appropriate set of comparable firms, the accuracy of these ratios can be improved sharply. In particular, we demonstrate the incremental usefulness of the technique for a sub-population of new economy stocks from the technology, telecom, and biotechnology sectors. 3. Development the Theory of The valuation literature discusses two broad approaches to estimating shareholder value. The first is direct valuation, in which firm value is estimated directly from its expected cash flows without appeal to the current price of other firms. Most direct valuations are based on projected dividends and/or earnings, and involve a present value computation of future cash flow forecasts. Common examples are the dividend discount model (DDM), the discounted cash flow (DCF) model, the residual income model (RIM), or some other variant. 8 The second is a relative valuation approach in We do not discuss liquidation valuation, in which a firm is valued at the breakup value of its assets. Commonly used in valuing real estate and distressed firms, this approach is not appropriate for most going concerns. 414 s. BHOJRAJAND C. M. C. LEE which firm value estimates are obtained by examining the pricing of comparableassets. This approach involves applying an accounting-based market multiple (e. g. , price-to-earnings, price-to-book, or price-to-sales ratios) from the comparable firm(s) to our accounting number to secure a value estimate. In relative valuation, an analyst applies the market multiple from a comparable firm to a target firms corresponding accounting number: Our estimated price = (Their market multiple) X (Our accounting number). In so doing, the analyst treats the accounting number in question as a summary statistic for the value of the firm. Assuming our firm in its current state deservesthe same market multiple as the comparable firm, this procedure allows us to estimate what the market would pay for our firm. Which firm(s) deservethe same multiple as our target firm? Valuation theory helps to resolve this question. In fact, explicit expressions for most of the most commonly used valuation multiples can be derived using little more than the dividend discount model and a few additional assumptions. For example, the residual income formula allows us to re-express the discounted dividend model in terms of the price-to-book ratio:10 * PB, Et[(ROEt+i re)Bt+i-l] (1 + re)i Bt i=1 (1) Bt where Pt* is the present value of expected dividends at time t, B, = book value at time t; Et [. ] = expectation based on information available at time t; re = cost of equity capital; and ROEt+i = the after-taxreturn on book equity for period t + i. This equation shows that a firms price-to-book ratio is a function of its expected ROEs, its cost-of-capital, and its future growth rate in book value. Firms that have similar price-to-book ratios should have present values of future residual income (the infinite sum in the right-hand-side of equation (1)) that are close to each other. In the same spirit, it is not difficult to derive the enterprise-value-to-sales ratio in terms of subsequent profit margins, growth rates, and the cost of capital. In the case ofa stable growth firm, the enterprise-value-to-salesratio can be expressed as: EV7 Et(PMxkx(1 + g)) _ (r- g) St where EVZ is total enterprise value (equity plus debt) at time t, St = total sales at time t; Et[. ] = expectation based on information available at 9 A third approach, not discussed here, is contingent claim valuation based on option pricing theory. Designed for pricing traded assets with finite lives, this approach encounters significant measurement problems when applied to equity securities. See Schwartz and Moon [2000] and Kellogg and Charnes [2000] for examples of how this approach can be applied to new economy stocks. 10See Feltham and Ohlson [1995] or Lee [1999] and the references therein for a discussion of this model. See Damodaran [1994; page 245] for a similar expression. WHO IS MYPEER? 415 time t; PM is operating profit margin (earnings before interest); k is a constant payout ratio (dividends and debt servicing costs as a percentage of earnings; alternatively, it is sometimes called one minus the plow-back rate); r = weighted average cost of capital; and g is a constant earnings growth rate. In the more general case, we can model the firms growth in terms of an initial period (say n years) of high growth, followed by a period of more stable growth in perpetuity. Under this assumption, a firms enterprise-valueto-sales ratio can be expressed as: (1+ EVt St EtPMxkx rL? gl)(1- ((1 + gg)n/(l r + r)n)) (1 + gi) n(l + g2) 1 (1+g1)n(1+ g2) nir- (1+r g ]ii (3) where EV7 is the total enterprise value (debt plus equity) at time t, St = total sales at time t; Et[. = expectation based on information available at time t; PM is operating profit margin; k is a constant payout ratio; r = cost of capital; gi is the initial earnings growth rate, which is applied for n years; and g2 is the constant growth rate applicable from period n+ 1 onwards. Equation (3) shows that a firms warranted enterprise-value-to-sales ratio is a function of its expected operating profit margin (PM), payout ratio (k), expected growth rates (gi and g2), and cost of capital (re). If the market value of equity and d ebt approximates the present value of expected cash flows, these variables should explain a ignificant portion of the cross-sectional variation in the EVS ratio. In the tests that follow, we employ a multiple regression model to estimate the warranted EVSand PB ratios for each firm. The explanatory variables we use in the model are empirical proxies for the key elements in the right-hand side of equations (1) and (3). 4. Research Design In this section, we estimate annual regressions that attempt to explain the cross-sectional variation in the EVSand PBratios. Our goal is to develop a reasonably parsimonious model that produces a warrantedmultiple (WEVS or WPB)for each firm. These warranted multiples reflect the large sample relation between a firms EVS (or PB) ratio and variables that should explain cross-sectional variations in the ratio. The estimated WEVS(or WPB) becomes the basis of our comparable firm analysis. 4. 1 ESTIMATING THE WARRANTED RATIOS We use all firms in the intersection of (a) the merged COMPUSTATindustrial and research files, and (b) the I/B/E/S historical database of analyst earnings forecasts, excluding ADRs and REITs. We conduct our analysis as of June 30th of each year for the period 1982-1998. To be included 416 AND s. BHOJRAJ C. M. C. LEE n the analysis a firm must have at least one consensus forecast of longterm growth available during the 12 months endedJune 30th. In the event that more than one consensus forecast was made in any year, the most recent forecast is used. We use accounting information for each firm as of the most recent fiscal year end date, and stock prices as of the end of June. To facilitate estimation of a r obust model, we drop firms with prices below $3 per share and sales below $100 million. We eliminate firms with negative book value (defined as common equity), and any firms with missing price or accounting data needed for the estimation regression. 2We require that all firms belong in an industry (based on two-digit SIC codes) with at least five member firms. In addition we eliminate firms in the top and bottom one percent of all firms ranked by EVS, PB, Rnoa, Lev, Adjpm,and Adjgroeach year (these variables are defined below). The number of remaining firms in the sample range from 741 (in 1982) to 1,498 (in 1998). For each firm, we secure nine explanatory variables. We are guided in the choice of these variables by the valuation equations discussed earlier, and several practical implementation principles. First, we wish to construct a model that can be applied to private as well as public firms, we therefore avoid using the market value of the target firm in any of the explanatory variables. Second, in the spirit of the contextual fundamental analysis (e. g. , see Beneish, Lee, and Tarpley [2000]), we anchor our estimation procedure on specific industries. In other words, we use the mean industry market multiples as a starting point, and adjust for key firm-specific characteristics. 3 Finally, to the extent possible, we try to use similar variables for estimating EVSand PB. Our goal is to generate relatively simple models that capture the key theoretical constructs of growth, risk, and profitability. Specifically, our model includes the following variables, which are also summarized and described in more detail in Appendix B: IndevsThe harmonic mean of the enterprise-value-to-salesmultiple for all the firms with the same two-digit SIC code. For example, for the 1982 regression, this variable is the harmonic mean industry EVS as of June 1, 1982. Enterprise value is defined as total market capitalization of equity, plus book value of long-term debt. This variable controls for industrywide factors, such as profit margins and growth rates, and we expect it to be positively correlated with current year firm-specific EVS and PB ratios. Indpb-The harmonic mean of the price-to-book ratio for all firms in the same industry. This variable controls for industry-wide factors that affect the PB ratio. In addition, Gebhardt et al. [2001] show firms with higher PB 12 The two exceptions are research and development expense and long-term debt. Missing data in these two fields are assigned a value of zero. More specifically, we use the harmonic means of industry EVSand PB ratios, that is, the inverse of the average of inversed ratios (see Baker and Ruback [1999]). WHO IS MYPEER? 417 ratios have lower implied costs of capital. To the extent that industries with lower implied costs-of-capital have higher market multiples, we expect this variable to be positively correlated with EVSand PB ratios. AdjpmThe industry-adjusted profit margin. We comput e this variable as the difference between the firms profit margin and the median industry profit margin. In each case, the profit margin is defined as a firms operating profit divided by its sales. Theory suggests this variable should be positively correlated with current year EVSratios. where Dum is 1 if Adjpm LosspmThisvariable is computed as Adjpm*Dum, is less than or equal to zero, and 0 otherwise. Used in conjunction with Adjpm,this variable captures the differential effect of profit margin on the P/S ratio for loss firms. Prior studies (e. g. , Hayn [1995]) show that prices (and returns) are less responsive to losses than to profits. In univariate tests, this variable should be positively correlated with EVSand PB. However, controlling for Adjpm,this variable should be negatively correlated with EVSand PB ratios. AdjgroIndustry-adjusted growth forecasts. This variable is computed as the difference between a firms consensus earnings growth forecast (from IBES) and the industry median of the same. Higher growth firms merit higher EVSand PB ratios. LevBook leverage. This variable is computed as the total long-term debt scaled by the book value of common equity. In univariate tests, Gebhardt et al. [2001] shows that firms with higher leverage have higher implied costsof-capital. However, controlling for market leverage, they find that book leverage is not significant in explaining implied cost-of-capital. We include this variable for completeness, in case it captures elements of cross-sectional risk not captured by the other variables. Rnoa-Return on net operating asset. This variable is a firms operating profit scaled by its net operating assets. Penman [2000] recommends this variable as a measure of a firms core operation profitability. In our context, having already controlled for profit margins, this variable also serves as a control for a firms asset turnover. We expect it to be positively correlated with the EVSand PB ratios. RoeReturn on equity. This variable is net income before extraordinary items scaled by the end of period common equity. Conceptually, this variable should provide a better profitability proxy in the case of the PB ratio. We use this variable in place of Rnoa as an alternative measure of profitability when conducting the PB regression. Rd-Total research and development expenditures divided by sales. Firms with higher RD expenditures tend to have understated current profitability relative to future profitability. To the extent that this variable captures profitability growth beyond the consensus earnings forecast growth rate, we expect it to be positively correlated with the EVSand PB ratios. In addition to these nine explanatory variables, we also tested three other variables-a dividend payout measure (actual dividends scaled by 418 S. BHOJRAJ AND C. M. C. LEE total assets), an asset turnover measure, and a measure of the standard deviation of the forecasted growth rate. The first two variables add little to the explanatory power of the model. The standard deviation measure (suggested by Gebhardt et al. 2001] as a determinant of the cost-ofcapital) contributed marginally, but was missing for a significant number of observations. Moreover, this measure would be unavailable for private firms. For these reasons, we excluded all three variables from our final model. To recap, our research design involves estimating a series of annual cross-sectional regressions of either the EVSor PB ratio on ei ght explanatory variables. The estimated coefficients from last years regressions are used, in conjunction with each firms current year information, to generate a prediction of the firms current and future ratio. We refer to this prediction as a firms warrantedmultiple (WEVSor WPB). This warranted multiple becomes the basis for our identification of comparable firms in subsequent tests. STATISTICS 4. 2 DESCRIPTIVE Table 1 presents annual summary statistics on the two dependent and nine explanatory variables. The overall average EVS of 1. 20 (median of 0. 94) and average PB of 2. 26 (median of 1. 84) are comparable to prior studies (e. g. , LNT, BB), although our sample size is considerably larger due to the inclusion of loss firms. This table also reveals some trends in the key variables over time. Consistent with prior studies (e. g. Frankel and Lee [1999]) we observe an increase over time in the accounting-based multiples (EVS, PB, Indps, and Indpb) and total RD expenditures (Rd). This non-stationarity in the estimated coefficients could be attributable to systematic changes in the composition of firms over time. For example, the increased importance of the RD variable could reflect the ris ing prominence of technology firms in the sample. The accounting-based rates of return (Rnoa and Roe) and book leverage (Lev) are relatively stable over time. As expected, the industry-adjustedvariables (Adjpm,Losspm,and Adjgro) have mean and median measures close to zero. Overall, this table indicates that the key input variables for our analysis make economical sense. Table 2 presents the average annual pairwise correlation coefficients between these variables. The upper triangle reports Spearman rank correlation coefficients; the lower triangle reports Pearson correlation coefficients. As expected, EVSis positively correlated with the industry enterprise-value-tosales ratio (Indevs) and price-to-book ratio (Indpb). It is also positively correlated with industry-adjusted measures of a firms profit margin (Adjpm) and expected growth rate (Adjgro). It is negatively correlated with book leverage (Lev), and positively correlated with accounting rates of return (Rnoa and Roe), as well as RD expense (Rd). To a lesser degree, EVS is also positively correlated with profit margin among loss firms (Losspm). The results are similar for the PB ratio. All the correlation coefficients WHO IS MY PEER? TABLE 1 StatisticsofEstimationVariables Summary 419 This table provides information on the mean and median of the variables used in the annual estimation regressions. All accounting variables are from the most recent fiscal year end publicly available byJune 30th. Market values are as of June 30th. EVSis the enterprise value to sales ratio, computed as the market value common equity plus long-term debt, divided by sales. PB is the price to book ratio. Indevsis the industry harmonic mean of EVSbased on two-digit SIC codes. Indpbis the industry harmonic mean of PB. Adjpmis the difference between the firms profit margin and the industry profit margin, where profit margin is defined as operating profit divided by sales. Losspmis Adjpm* indicator variable, where the indicator variable is 1 if profit is margin 0 and 0 otherwise. Adjgro the difference between the analysts consensus forecast of the firms long-term growth and the industry average. Lev is the total long-term debt scaled by book value of stockholders equity. Rnoa is operating profit scaled by net operating assets. Rd is the firms RD expressed as a percentage of net sales. year 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 mean median mean median mean median mean median mean median mean median mean median mean median mean median mean median mean median mean median mean median mean median mean median mean median mean median EVS 0. 3 0. 50 0. 98 0. 77 0. 84 0. 69 0. 88 0. 73 1. 07 0. 88 1. 22 1. 00 1. 09 0. 90 1. 07 0. 89 1. 09 0. 89 1. 10 0. 87 1. 15 0. 94 1. 22 1. 02 1. 20 1. 00 1. 36 1. 07 1. 49 1. 13 1. 51 1. 20 1. 59 1. 24 PB 1. 11 0. 93 1. 82 1. 48 1. 46 1. 26 1. 72 1. 46 2. 14 1. 82 2. 31 1. 92 1. 97 1. 70 2. 02 1. 70 1. 99 1. 64 1. 93 1. 54 2. 13 1. 76 2. 48 2. 04 2. 31 1. 98 2. 49 2. 08 2. 75 2. 24 2. 87 2. 41 3. 06 2. 55 Indevs Indpb Adjpm 0. 50 0. 006 0. 92 0. 000 0. 50 0. 92 1. 57 0. 76 0. 002 1. 59 0. 77 0. 000 1. 34 0. 69 0. 001 0. 000 1. 30 0. 72 0. 70 1. 45 0. 004 1. 30 0. 000 0. 72 0. 001 0. 85 1. 7 0. 000 0. 86 1. 69 0. 95 1. 95 -0. 002 0. 95 0. 000 1. 82 1. 69 0. 85 0. 002 0. 80 1. 61 0. 000 0. 84 1. 79 0. 003 0. 76 1. 63 0. 000 0. 83 1. 69 0. 002 0. 79 1. 49 0. 000 1. 65 0. 003 0. 80 1. 39 0. 000 0. 69 0. 87 1. 71 0. 005 0. 78 0. 000 1. 52 0. 90 1. 91 0. 002 0. 000 0. 86 1. 76 0. 89 0. 006 2. 02 0. 86 1. 91 0. 000 0. 95 0. 007 2. 06 0. 93 0. 000 2. 02 1. 01 0. 009 2. 18 0. 98 1. 99 0. 000 0. 005 1. 02 2. 12 1. 07 0. 000 2. 01 1. 09 0. 004 2. 20 0. 000 1. 08 2. 05 Losspm 0. 000 0. 000 -0. 003 0. 000 -0. 004 0. 000 -0. 002 0. 000 -0. 004 0. 000 -0. 007 0. 000 -0. 004 0. 000 -0. 03 0. 000 -0. 004 0. 000 -0. 002 0. 000 -0. 004 0. 000 -0. 002 0. 000 -0. 002 0. 000 -0. 001 0. 000 -0. 002 0. 000 -0. 003 0. 000 -0. 004 0. 000 Adjgro 0. 50 0. 00 0. 21 -0. 05 0. 44 -0. 01 0. 66 0. 00 0. 30 -0. 04 0. 18 -0. 10 0. 29 0. 00 0. 69 0. 00 0. 58 -0. 08 0. 45 -0. 12 0. 23 -0. 19 0. 55 -0. 09 0. 49 -0. 15 0. 73 0. 00 0. 40 -0. 13 0. 36 -0. 17 0. 43 0. 00 Lev 0. 45 0. 36 0. 49 0. 38 0. 43 0. 33 0. 44 0. 32 0. 50 0. 34 0. 54 0. 40 0. 56 0. 43 0. 57 0. 41 0. 61 0. 44 0. 59 0. 45 0. 59 0. 42 0. 58 0. 39 0. 58 0. 36 0. 56 0. 38 0. 58 0. 37 0. 61 0. 36 0. 63 0. 38 Rnoa 20. 85 19. 62 17. 8 16. 18 17. 85 16. 93 19. 96 18. 82 17. 58 16. 41 17. 27 16. 00 19. 05 17. 68 19. 90 18. 54 19. 77 17. 97 19. 00 16. 93 17. 86 15. 97 19. 80 17. 22 20. 08 17. 47 21. 66 18. 72 22. 19 18. 93 21. 56 18. 97 22. 84 20. 24 Roe 14. 39 14. 77 11. 88 12. 82 12. 04 13. 00 13. 49 14. 32 11. 45 12. 92 10. 63 12. 22 12. 61 12. 93 13. 90 14. 71 13. 29 13. 51 11. 91 12. 55 10. 31 11. 29 11. 87 12. 39 11. 57 12. 37 13. 48 13. 18 12. 57 13. 08 12. 46 12. 89 12. 31 12. 76 Rd 1. 23 0. 14 1. 33 0. 09 1. 51 0. 08 1. 66 0. 05 1. 75 0. 00 1. 94 0. 00 1. 83 0. 00 1. 94 0. 00 1. 86 0. 00 1. 96 0. 00 2. 03 0. 00 1. 9 0. 00 1. 90 0. 00 1. 77 0. 00 2. 01 0. 00 2. 01 0. 00 2. 25 0. 00 Pooled mean 1. 20 2. 26 median 0. 94 1. 84 0. 88 0. 81 1. 83 1. 72 0. 004 -0. 003 0. 44 0. 000 0. 000 -0. 05 0. 56 20. 00 12. 35 1. 86 0. 38 17. 96 13. 01 0. 00 are in the expected direction. Except for the correlation between Rnoa and Roe (which do not appear in the same estimation regression), none of the average pairwise correlation coefficients exceed 0. 60. These results suggest that the explanatory variables are not likely to be redundant. 420 S. BHOJRAJAND C. M. C. LEE TABLE 2 Correlation between EstimationVariables This table provides the correlation between the variables. The upper triangle reflects the Spearman correlation estimates; the lower triangle reflects the Pearson correlation coefficients. All accounting variables are based on the most recent fiscal year end information publicly available byJune 30th. Market values are as of June 30th. EVSis the enterprise value to sales ratio, computed as the market value common equity plus long-term debt, divided by sales. PB is the price to book ratio. Indevsis the industry harmonic mean of EVSbased on two-digit SIC codes. Indpbis the industry harmonic mean of PB. Adjpmis the difference between the firms profit margin and the industry profit margin, where profit margin is defined as operating profit divided by sales. Losspmis Adjpm*indicator variable, where the indicator variable is 1 if profit is margin 0 and 0 otherwise. Adjgro the difference between the analysts consensus forecast of the firms long-term growth and the industry average. Lev is the total long-term debt scaled by book value of stockholders equity. Rnoa is operating profit scaled by net operating assets. Rd is the firms RD expressed as a percentage of net sales. Average Correlation (Pearson/Spearman) EVS EVS PB Indevs PB 0. 52 Indevs Indpb 0. 51 0. 16 0. 09 0. 33 0. 35 0. 35 -0. 06 -0. 02 0. 04 0. 02 -0. 01 -0. 05 0. 08 -0. 09 -0. 02 0. 25 0. 03 0. 14 0. 10 0. 06 Adjpm Losspm Adjgro Lev Rnoa Roe 0. 54 0. 08 0. 21 -0. 07 0. 21 0. 28 0. 38 0. 14 0. 60 0. 59 0. 29 -0. 20 -0. 07 0. 04 -0. 01 0. 06 -0. 01 0. 05 0. 15 -0. 03 0. 06 -0. 04 -0. 14 0. 26 0. 06 -0. 17 0. 54 0. 55 0. 26 0. 06 -0. 03 0. 32 0. 28 0. 26 0. 04 0. 04 -0. 01 0. 10 0. 09 -0. 35 -0. 16 0. 02 -0. 12 -0. 02 0. 51 0. 07 -0. 24 0. 75 0. 32 0. 50 0. 38 0. 07 -0. 12 0. 66 0. 06 -0. 10 0. 09 -0. 23 -0. 03 -0. 6 Rd 0. 17 0. 08 0. 19 0. 11 0. 03 -0. 05 -0. 02 -0. 27 0. 03 -0. 03 0. 47 0. 50 0. 04 0. 15 0. 28 Indpb 0. 33 Adjpm 0. 59 0. 09 Losspm 0. 06 0. 29 Adjgro 0. 22 Lev -0. 03 -0. 07 Rnoa 0. 54 0. 22 0. 48 Roe 0. 23 Rd 0. 09 0. 24 5. Empirical Results 5. 1 MODEL ESTIMATION Table 3 presents the results of annual cross-sectional regressions for each year from 1982 to 1998. The dependen t variable is the enterprise-value-tosales ratio (EVS). The eight independent variables are as described in the previous section. Table values represent estimated coefficients, with accompanying p-values presented in parentheses. Reported in the right columns are adjusted r-squares and the number of observations per year. The last two rows report the average coefficient for each variable, as well as a Newey-West autocorrelation adjusted t-statisticon the mean of the time series of annual estimated coefficients. The results from this table indicate that a consistently high proportion of the cross-sectional variation in the EVS ratio is captured by the eight explanatory variables. The annual adjusted r-squares average 72. 2%, and range from a low of 66. 1% to a high of 76. 5%. The strongest six explanaRnoa, nd RD) have the same tory variables (Indevs,Adjpm,Losspm, Adjgro, directional sign in each of 17 annual regressions, and are individually significant at less than 1%. Indpbis positively correlated with EVS in 11 out of 17 years, and is significant at the 5% level. Controlling for Indpb,book WHO IS MY PEER? TABLE 3 Annual EstimationRegressions Warranted for Enterprise-Value-to-Sales This table reports the res ults from the following annual estimation regression: 8 421 EVSi,t = at + j=1 jtCj,i,t + Li,t where the dependent variable, EVS,is the enterprise value to sales ratio as ofJune 30th of each year. The eight explanatory variables are as follows: Indevs is the industry harmonic mean of EVSbased on two-digit SIC codes; Indpbis the industry harmonic mean of the price-to-book ratio; Adjpmis the difference between the firms profit margin and the industry profit margin, is where profit margin is defined as operating profit divided by sales; Losspm Adjpm indicator variable, where the indicator variable is 1 if profit margin 0 and 0 otherwise; Adjgrois the difference between the analysts consensus forecast of the firms long-term growth rate and the industry average; Lev is long-term debt scaled by book equity; Rnoa is operating profit as a percent of net operating assets; and Rd is RD expense as a percentage of sales. P-values are provided in parentheses. The last row represents the time-series average coefficients along with Newey-Westautocorrelation corrected t-statistics. The adjusted r-square (r-sq) and number of firms (# obs) are also reported. Year Intercept 1982 -0. 0623 (0. 13 5) 1983 -0. 0883 (0. 121) 1984 0. 0192 (0. 699) 1985 0. 1337 (0. 002) 1986 0. 0225 (0. 706) 1987 0. 1899 (0. 007) 1988 0. 1774 (0. 0) 1989 -0. 0455 (0. 347) 1990 0. 1083 (0. 027) 1991 0. 2321 (0. 00) 1992 0. 2064 Indevs 1. 2643 (0. 00) 1. 3531 (0. 00) 1. 2778 (0. 00) 1. 2231 (0. 00) 1. 3202 (0. 00) 1. 0908 (0. 00) 1. 0759 (0. 00) 1. 1264 (0. 00) 1. 1263 (0. 00) 1. 0740 (0. 00) 0. 8277 1. 0169 (0. 00) 1. 0027 (0. 00) 1. 0355 (0. 00) 1. 1690 (0. 00) 1. 1714 (0. 00) 1. 0157 (0. 00) 1. 1277 (0. 00) Indpb 0. 1648 (0. 00) -0. 0301 (0. 342) -0. 0015 (0. 964) -0. 0152 (0. 604) 0. 0047 (0. 856) -0. 0324 (0. 339) -0. 0097 (0. 63) 0. 0828 (0. 00) 0. 0322 (0. 019) 0. 0256 (0. 079) 0. 1150 0. 0579 (0. 097) 0. 0027 (0. 913) -0. 0211 (0. 512) 0. 0430 (0. 141) 0. 0366 (0. 264) 0. 1561 (0. 0) 0. 0360 (0. 031) Adjpm 6. 3052 (0. 00) 8. 1343 (0. 00) 6. 9266 (0. 00) 7. 9394 (0. 00) 9. 4308 (0. 00) 9. 8090 (0. 00) 8. 6458 (0. 00) 8. 4475 (0. 00) 9. 3485 (0. 00) 10. 4789 (0. 00) 10. 2810 Losspm -2. 8510 ( 0. 119) -5. 3800 (0. 00) -4. 2894 (0. 00) -4. 0951 (0. 00) -6. 2424 (0. 00) -6. 8296 (0. 00) -6. 9959 (0. 00) -5. 3691 (0. 00) -6. 0607 (0. 00) -6. 9779 (0. 00) -7. 9414 Adjgro 0. 0117 (0. 00) 0. 0392 (0. 00) 0. 0209 (0. 00) 0. 0177 (0. 00) 0. 0316 (0. 00) 0. 0363 (0. 00) 0. 0267 (0. 00) 0. 0225 (0. 00) 0. 0346 (0. 00) 0. 0316 (0. 00) 0. 0329 Lev 0. 0665 (0. 007) 0. 1414 (0. 00) 0. 0707 (0. 012) 0. 0238 (0. 351) -0. 0246 (0. 325) 0. 608 (0. 035) 0. 0228 (0. 27) 0. 0143 (0. 409) -0. 0381 (0. 065) -0. 0430 (0. 06) -0. 0567 Rnoa -0. 0091 (0. 00) -0. 0049 (0. 004) -0. 0088 (0. 00) -0. 0089 (0. 00) -0. 0080 (0. 00) -0. 0041 (0. 014) -0. 0054 (0. 00) -0. 0032 (0. 01) -0. 0037 (0. 005) -0. 0053 (0. 00) -0. 0037 Rd 0. 0194 (0. 00) 0. 0463 (0. 00) 0. 0197 (0. 00) 0. 0153 (0. 00) 0. 0118 (0. 01) 0. 0319 (0. 00) 0. 0281 (0. 00) 0. 0127 (0. 00) 0. 0191 (0. 00) 0. 0134 (0. 00) 0. 0157 0. 0253 (0. 00) 0. 0254 (0. 00) 0. 0680 (0. 00) 0. 0244 (0. 00) 0. 0313 (0. 00) 0. 0229 (0. 00) 0. 0253 (0. 00) R-sq # Obs 74. 40 741 70. 80 73. 45 74. 66 71. 11 66. 84 75. 44 74. 58 73. 54 76. 45 71. 63 71. 1 748 771 797 799 856 787 813 829 855 902 978 (0. 00) 1993 1994 1995 1996 1997 1998 All 0. 1811 (0. 004) 0. 2698 (0. 00) 0. 3148 (0. 00) 0. 0713 (0. 249) 0. 1192 (0. 048) -0. 0269 (0. 683) 0. 1072 (0. 007) (0. 00) (0. 00) (0. 00) (0. 00) (0. 00) (0. 004) (0. 008) (0. 00) 11. 4266 -6. 4058 (0. 00) (0. 00) 10. 6165 -7. 1717 (0. 00) (0. 00) 11. 9432 -9. 2245 (0. 00) (0. 00) 11. 3311-10. 6464 (0. 00) (0. 00) 12. 5771 -7. 5521 (0. 00) (0. 00) 13. 0309-10. 1430 (0. 00) (0. 00) 9. 8043 -6. 7162 (0. 00) (0. 00) 0. 0333 -0. 0129 -0. 0045 (0. 00) (0. 515) (0. 00) 0. 0312 0. 0219 -0. 0060 (0. 00) (0. 202) (0. 00) 0. 0419 0. 0100 -0. 0069 (0. 00) (0. 618) (0. 0) 0. 0623 0. 0001 -0. 0023 (0. 00) (0. 996) (0. 121) 0. 0452 0. 0201 -0. 0032 (0. 00) (0. 278) (0. 011) 0. 0421 0. 0362 -0. 0006 (0. 00) (0. 069) (0. 637) 0. 0330 0. 0184 -0. 0052 (0. 00) (0. 235) (0. 00) 73. 19 1102 75. 37 1190 66. 05 1341 71. 75 1440 66. 65 1498 72. 19 16447 422 AND C. M. C. LEE s. BHOJRAJ leverage (Lev) is not significantly correlated with EVS. Collectively, these results show that growth, profitability, and risk factors are incrementally important in explaining EVSratios, even after controlling for industry means. Note that the estimated coefficients on several of the key explanatory variables change systematicallyover time. For example, the estimated coefficient on the industry adjusted profit margin (Adjpm)and forecasted growth rate (Adjgro)both trend upwards over time, while the coefficient on the industry enterprise-value-to-sales ratio (Indevs) shows some decline in recent years. These patterns imply that, in forecasting future EVSratios, the estimated coefficients from the most recent year is likely to perform better than a rolling average of past years. In subsequent analyses, we use the estimated coefficients from the prior years regression to forecast current years warranted multiple. Table 4 reports the results of annual cross-sectional regressions for the PB ratio. The explanatory variables are the same as for the EVS regression in table 3, except for the replacement of Rnoa with Roe. Table 4 shows that all the variables except Lev contribute significantly to the explanation of PB. The coefficient on Indps is reliably negative. Otherwise, the variables are correlated with PB in the same direction as expected. Overall, the model is less successful at explaining PB than at explaining EVS. Nevertheless, the average adjusted r-square is still 51. 2%, ranging from a low of 32. 8% to a high of 61. 0%. FUTURE RATIOS 5. 2 FORECASTING Recall that our goal is to identify comparable firms that will help us to forecast a target firms future price-to-sales multiples. In this section, we examine the efficacy of the warranted multiple approach in achieving this goal. Specifically, we examine the relation between a firms future EVS and PB ratios, and a number of ex ante measures based on alternative definitions of comparable firms. The key variables in this analysisare defined below. EVSn and PBn, where n = 0, 1, 2, and 3-The current, one-, two-, and three-year-ahead EVSand PB ratios. These are our dependent variables. IEVS and IPBThe harmonic mean of the industry EVS and PB ratios, respectively. Industry membership is defined in terms of two-digit SIC codes. ISEVSand ISPBThe harmonic mean of the actual EVSand PB ratios for the four firms from the same industry with the closest market capitalization. and WPBThe warranted EVSand PB ratios. These variables are WEVS computed using the estimated coefficients from the prior years regression (tables 3 and 4), and accounting or market-based variables from the current year. COMPActual EVS (or PB) ratio for the closest comparable firms. This variable is the harmonic mean of the actual EVS (or PB) ratio of the four closest firms based on their warranted multiple. To construct this variable, WHO IS MY PEER? 423 TABLE 4 Price-to-Book Annual EstimationRegressions Warranted for This table reports the results from the following annual estimation regression: 8 PBi,t = at + E j=1 j,tCj,i,t + ti,t where the dependent variable, PB, is the price-to-book ratio as ofJune 30th of each year. The eight explanatory variables are as follows: Indevsis the industry harmonic mean of EVSbased on two-digit SIC codes; Indpbis the industry harmonic mean of the price-to-book ratio; Adjpm is the difference between the firms profit margin and the industry profit margin, where profit margin is defined as operating profit divided by sales; Losspmis AdjpmeDum, where Dum is 1 if profit margin 0 and 0 otherwise; Adjgrois the difference between the analysts consensus forecast of the firms long-term growth rate and the industry average; Lev is long-term debt scaled by book equity; Roe is net income before extraordinary items as a percent of book equity; and Rd is RD expense as a percentage of sales. The p-values are provided below each of the coefficients in parentheses. The last row represents the time-series average coefficients along with Newey-Westautocorrelation corrected t- statistics. The adjusted r-square (r-sq) and number of firms (# obs) are also reported. Year Intercept Indevs 1 982 -0. 2990 -0. 6056 (0. 00) (0. 00) 1983 -0. 3434 -0. 5129 (0. 00) (0. 001) 1984 -0. 1065 -0. 1806 (0. 143) (0. 099) 1985 -0. 3518 -0. 2882 (0. 00) (0. 09) 1986 0. 0998 -0. 3548 (0. 319) (0. 005) 1987 0. 0632 -0. 6468 (0. 584) (0. 00) 1988 0. 0568 -0. 5150 (0. 566) (0. 00) 1989 -0. 3306 -0. 5790 (0. 001) (0. 00) 1990 -0. 4592 -0. 9002 (0. 00) (0. 00) 1991 0. 0459 -0. 9010 (0. 613) (0. 00) 0. 1797 -0. 6645 1992 (0. 098) (0. 00) 1993 0. 2426 -0. 5925 (0. 111) (0. 00) 1994 -0. 0187 -0. 4753 1995 -0. 3095 (0. 008) 1996 -0. 0713 (0. 569) 1997 0. 1104 (0. 402) 1998 0. 0247 (0. 87) All -0. 0863 (0. 169) -0. 2491 (0. 00) -0. 3475 (0. 00) -0. 3565 (0. 00) -0. 3666 (0. 00) -0. 5021 (0. 00) Indpb 1. 1601 (0. 00) 1. 1696 (0. 00) 0. 9401 (0. 00) 1. 0448 (0. 00) 0. 9866 (0. 00) 1. 0956 (0. 00) 0. 8393 (0. 00) 1. 269 (0. 00) 1. 3508 (0. 00) 1. 0963 (0. 00) 1. 0051 (0. 00) 0. 7907 (0. 00) 1. 0234 0. 9481 (0. 00) 1. 0319 (0. 00) 0. 8816 (0. 00) 1. 0553 (0. 00) 1. 0321 (0. 00) Adjpm Losspm 2. 0331 -6. 2544 (0. 00) (0. 00) 3. 2891-11. 9301 (0. 00) (0. 00) 2. 0887 -5. 9880 (0. 00) (0. 00) 3. 0154 -8. 6571 (0. 00) (0. 00) 3. 6912 -6. 4419 (0. 00) (0. 00) 6. 0189 -9. 8553 (0. 00) (0. 00) 2. 0184 -9. 9218 (0. 00) (0. 00) 2. 6023-15. 3872 (0. 00) (0. 00) 1. 9280-10. 8096 (0. 00) (0. 00) 3. 0820-10. 7620 (0. 00) (0. 00) 3. 5272-12. 3146 (0. 00) (0. 00) 1. 6280-13. 7791 (0. 005) (0. 00) 3. 1253 -9. 8989 4. 3329 -9,7318 (0. 00) (0. 00) 4. 0730-13. 0282 (0. 00) (0. 0) 3. 8790-13. 5652 (0. 00) (0. 00) 3. 7902 -7. 1481 (0. 00) (0. 00) 3. 1837-10. 3220 (0. 00) (0. 00) Adjgro 0. 0371 (0. 00) 0. 1147 (0. 00) 0. 0527 (0. 00) 0. 0568 (0. 00) 0. 0883 (0. 00) 0. 0881 (0. 00) 0. 0694 (0. 00) 0. 0576 (0. 00) 0. 0815 (0. 00) 0. 0744 (0. 00) 0. 0781 (0. 00) 0. 0939 (0. 00) 0. 0834 Lev Roe -0. 2245 0. 0402 (0. 00) (0. 00) -0. 1545 0. 0541 (0. 01) (0. 00) -0. 2302 0. 0397 (0. 00) (0. 00) 0. 0585 -0. 2694 (0. 00) (0. 00) -0. 3075 0. 0542 (0. 00) (0. 00) 0. 0583 0. 0459 (0. 221) (0. 00) -0. 0675 0. 066 6 (0. 083) (0. 00) -0. 0474 0. 0574 (0. 176) (0. 00) -0. 0663 0. 0644 (0. 073) (0. 00) 0. 0683 -0. 1227 (0. 001) (0. 00) 0. 018 0. 0593 (0. 969) (0. 00) 0. 1131 0. 0828 (0. 02) (0. 00) 0. 1650 0. 0521 0. 0735 (0. 00) 0. 0649 (0. 00) 0. 0837 (0. 00) 0. 0674 (0. 00) 0. 0608 (0. 00) Rd 0. 0418 (0. 00) 0. 0627 (0. 00) 0. 0314 (0. 00) 0. 0013 (0. 845) 0. 0053 (0. 528) 0. 0323 (0. 001) 0. 0266 (0. 001) 0. 0111 (0. 122) 0. 0144 (0. 08) -0. 0052 (0. 477) 0. 0203 (0. 007) 0. 0468 (0. 00) 0. 0436 0. 0742 (0. 00) 0. 0147 (0. 133) 0. 0248 (0. 006) 0. 0341 (0. 00) 0. 0282 (0. 00) R-sq # Obs 55. 78 832 60. 99 57. 83 59. 15 56. 55 852 319 956 954 52. 97 1019 54. 15 52. 19 940 999 53. 16 1023 54. 88 1041 48. 51 1089 46. 82 1188 44. 96 1349 53. 52 1447 42. 76 1628 43. 00 1723 32. 2 1828 51. 18 19187 (0. 881) (0. 00) (0. 00) (0. 00oo)(0. 00) (0. 00) (0. 00) (0. 00) (0. 00) 0. 0908 0. 0409 (0. 284) (0. 00) 0. 1221 0. 1303 (0. 00) (0. 006) 0. 0948 0. 1596 (0. 00) (0. 00) 0. 0852 0. 2276 (0. 00) (0. 00) 0. 0805 -0. 0349 (0. 00) (0. 511) 424 s. 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As an illustration, Appendix C presents selection details for Guidant Corporation (GDT), a manufacturer of medical devices. This appendix illustrates the set of firms in the same two-digit SIC code, which are identified as peers of Guidant based on data as of April 30, 2001. Panel A reports the Panel B reports the closest firms based six closest firms based on WEVS, on WPB. We reviewed this list with a professional analyst who covers this sector. She agreed with most of the selections but questioned the absence of St. Jude Medical Devices (STJ), which she regarded as a natural peer. She agreed with our choices, however, after we discussed the profitability, growth, and risk characteristics of STJ in comparison to those of the firms listed. Table 5 reports the results for a series of forecasting regressions. In panel A, the dependent variable is EVSn, and in panel B, the dependent variable is PBn; where n = 0, 1, 2, 3, indicates the number of years into the future. In each case, we regress the future market multiple on various ex ante measures based on alternative definitions of comparable firms. 14 The table values represent the estimated coefficient for each variable averaged across 14 (n= 3) to 17 (n= 0) annual cross-sectional regressions. The bottom row reports the average adjusted r-square of the annual regressions for each model. These results show that the harmonic mean of the industry-matched firms explains 17. 5% (three-year-ahead) to 22. 9% (current year) of the crosssectional variation in future EVSratios. Including the mean EVS ratio from the closest four firms matched on size increases the adjusted r-squaresonly marginally, so that collectively IEVSand ISEVSexplain 18% to 23% of the variation in future EVSratios. These results confirm prior evidence on the usefulness of industry-based comparable firms. However, they also show that 14Even for the current year (n= 0), the warranted multiples are based on estimated coefficients from the prior years regression. Therefore, the models that involve warranted multiples are all forecasting regressions. TABLE 5 Prediction Regressions This table provides average estimated coefficients from the following prediction regressions: + EVSi,t+k = at + s j= j, tCji,t + I-i,t ES PBi,t+k = at + j=1 where k =0, 1, 2, 3. In Panel A, the dependent variable is the enterprise-value-to-sales ratio (EVS). I ratio (PB). The expanatory variables are: IEVS,the harmonic mean of the industry EVSbased on cur the harmonic mean of the actual EVS for the four closest firms matched on size after controlling for using the coefficients derived from last years estimation regressions and current year accounting and and ICOMP,the harmonic mean of the the actual EVS for the four closest firms matched on WEVS; after controlling for industry. The variables for Panel B are defined analogously, replacing EVSwith P coefficients from annual cross-sectional regressions. The bottom row reports the average adjusted r-sq Panel A: Enterprise-value-to-sales Currentyear EVS 0. 00 Inter 0. 24 0. 06 0. 00 0. 22 IEVS 1. 19 0. 08 -0. 27 -0. 26 1. 02 0. 16 0. 14 0. 16 0. 13 ISEVS COMP 0. 89 0. 16 0. 98 0. 83 WEVS 0. 33 ICOMP r-sq 22. 94 23. 46 54. 71 61. 68 62. 99 Panel B: Book-value-to-sales Current year PB 0. 07 -0. 06 -0. 07 Inter 0. 40 0. 5 IPB 1. 04 1. 19 0. 26 -0. 09 -0. 07 0. 07 ISPB 0. 16 0. 11 0. 10 0. 81 0. 35 COMP 0. 77 0. 71 WPB 0. 44 ICOMP r-sq 11. 80 12. 34 35. 21 41. 94 43. 20 One year ahead EVS 0. 01 0. 01 0. 07 0. 23 1. 05 0. 16 -0. 17 -0. 16 0. 14 0. 14 0. 12 0. 12 0. 83 0. 13 0. 80 0. 93 0. 27 21. 24 46. 14 51. 97 53. 23 One year ahead PB 0. 40 0. 15 0. 04 1. 00 0. 38 0. 12 0. 18 0. 14 0. 13 0. 65 0. 29 0. 59 8. 02 19. 91 22. 94 0. 24 1. 19 0. 27 1. 18 Two year ah 0. 0. 25 1. 06 0. 0. 0. 13 0. 20. 75 18. 37 18. 79 40. 0. 46 1. 17 0. 05 0. 12 0. 10 0. 51 0. 40 23. 38 0. 57 1. 16 Two year a 0. 50 0. 0. 96 0. 0. 0. 21 0. 7. 62 5. 01 5. 47 12. 426 S. BHOJRAJAND C. M. C. LEE he valuation accuracy of industry-based EVS ratios leaves much to be desired. In fact, industry-size based comparable firms explain less than 20% of the variation in two-year-aheadEVSratios. The predictive power of the model increases sharply with the inclusion of variables based on the warranted EVSratio (WEVS). average, a model that On includes IEVS,ISEVS,and COMPexplains over 40% of the cross-sectional variation in two-ye ar-ahead EVS ratios. Including WEVSin the model increases the average adjusted r-square on the two-year-aheadregressions to the actual WEVS ratio 45. 5%. Moreover, even after controlling for WEVS, of the closest comparable firms (COMPor ICOMP)is incrementally useful in predicting future EVSratios. It appears that comparable firms selected on the basis of their WEVS adds to the prediction of future EVSratios even after controlling for WEVS itself. COMPand ICOMPyield similar results. A model that includes IEVS,ISEVS,WEVS, ICOMPexplains between 63. 0% and (current year) and 43. 1% (three-year-ahead) of the variation in future EVS ratios. 5 Panel B reports forecasting regressions for PB. Compared to EVS,a much smaller proportion of the variation in PB is captured by these models. In the current year, the combination of IPB and ISPB explains only 12. 3% of the variation in PB. The inclusion of WPBand ICOMPincreases the adjusted r-square to 43. 2%. In future years, the explanatory power of all the models declines sharply. However, over all forecast horizons, models based on warranted multiples explain more than twice the variation in future PB ratios as compared to the industry-size matched model. The rapid decay in the explanatory power of the PB model is a possible concern with these results. Either PB ratios are difficult to forecast, or our model is missing some key forecasting variables. To shed light on this issue, we report below the serial correlation in annual EVSand PB ratios. Table values in the chart below are average Pearson correlation coefficients between the current years ratio, and the same ratio one, two, or three years later. Average Correlation Coefficient EVS1 EVSO PBO 0. 87 EVS2 0. 79 EVS3 0. 73 PB1 0. 72 PB2 0. 56 PB3 0. 44 These results show that with a one-year lag, EVSis serially correlated at 0. 7, suggesting an r-square of around 76%. With a three-year lag, EVSis serially correlated at 0. 73, suggesting an r-square of 53%. Similarly,with a one-year lag, PB is serially c orrelated at 0. 72, suggesting an r-square of 52%. With 5 We also conducted year-by-year analysis to examine the stability of these results over time. We find that a model that includes IEVS,ISEVS,WEVS, and ICOMPis extremely consistent in predicting future EVSratios. All four variables are incrementally important in predicting future EVSratios in each fore

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How employee wellbeing can become a core value Leadership is the ability to effectively use strategic competencies and influence to accomplish organizational goals. It is a partnership between mangers followers and external constituencies and one of the main differences between leaders and managers is power and authority. Leaders yield power which cannot be transferred however managers yield authority due to their position and can be delegated. When organizational requires a fast changing as a result of rapid fluctuation a Transformational leader is called for. Transformational leadership is defined as a relationship between a leader and follower(s) based on a set of leader behaviors perceived by subordinates as exhibiting idealized influence, motivational inspiration, intellectual stimulation, and individual consideration. In todays modern complex organizations, which are going through constant change, it required the roles of management and leadership be intertwined. An organisation that has an environment which promotes a state of contentment allowing employees to flourish and achieve their full potential for the benefit of themselves and the organisation can be considered as employers who puts high emphasis on their employee wellbeing. The concept of wellbeing includes concepts of psychological and physical health. Today increasingly companies are focused more about their employees wellbeing as organisations are seeing its benefits. Organisations such as IBM, established Well-Being Management System (WBMS), the companys holistic approach to managing the health and safety of employees wherever they work. This enables physical and psychological fitness of its employees. When employee wellbeing becomes a shared organisational value deeply rooted in the organisational culture it is evident from the following companies that productivity of the organisations increases as a result of high moral and satisfaction as well as organisations are able to retain their talents. Values influence attitudes and behaviour therefore for transformational leaders to create employee wellbeing as a shared value they need to create trust between leader and employees. This is possible when the leader practice high emotional and social intelligence and honour the psychological contract between the management and employees. Contents Executive Summary 1 Today increasingly companies are focused more about their employees wellbeing as organisations are seeing its benefits. Organisations such as IBM, established Well-Being Management System (WBMS), the companys holistic approach to managing the health and safety of employees wherever they work. This enables physical and psychological fitness of its employees. 2 Contents 3 1.0 INTRODUCTION 4 8.0 CONCLUSION 16 7.0 Appendix 17 1.0 INTRODUCTION This report presents discussions on how employee wellbeing can become a core value shared among the employees of an organization that can be deeply rooted in the organizational culture and the role of transformational leadership in facilitating this change. Transformational leaders are called for when traditional leadership fails to bring about a change aligning the organizational culture to the changes in the environment. As these form of leadership focus on employees in achieving their goal it suits well in an era of fast globalization. In the second part of the report concepts of leadership and their difference from management will be introduced .as well as an explanation of transformational leadership and what it is all about. Third part of the report explains the concept employee wellbeing and its benefits. Companies who are initiating employee wellbeing and the response will be highlighted. The main part of the report will discuss how transformational leadership help facilitate employee wellbeing in the organizational culture that would eventually improve performance. Concepts of how leaders create trusts among employees and towards themselves, how leaders practice emotional and social intelligence and the concepts of psychological contract will be discussed. Finally case evidence will be provided when employees wellbeing is taken care off their performance in terms of absenteeism etc will rise significantly. 1.2 Scope and limitation The scope of the report is limited to the examination of how employee wellbeing can become the cultural norm within the organization and how transformational leadership facilitates in embedding it to the organizational culture as a shared core value. Little robust research exists on the relationship between health and individual job performance. 2.0 leadership Leadership defined by Weiss (2001, p.194) states that it is the ability to effectively use strategic competencies and influence to accomplish organizational goals. It is a partnership between mangers followers and external constituencies and one of the main differences between leaders and managers is power and authority. Leaders yield power which cannot be transferred however managers yield authority due to their position and can be delegated. (Mullins 2002). Whetten et.al (1995, p. 17) states that traditional definition of management is outmoded and irrelevant today. Their argument is based on the similarity between leaders and managers in terms of how they function. And that a good manager functions as an effective leader .a similar view was held by Hodgetts (1990, p.3) and defines management as getting things done through people and leadership influence people towards particular goal. Weiss(2001) referencing Bass (1985,1990 )states that when organizational requires a fast changing as a result of rapid fluctuation a transformational leader is called for. Transformational leadership exhibits behaviors such as idealized influence, motivational inspiration, intellectual stimulation, and individual consideration. And can be defined as a relationship between a leader and a follower based on this behavior (Flood 2008) A number of studies have shown a strong positive relationship between this leadership style and desirable outcomes including organizational commitment, job satisfaction, and decreased employee turnover intentions (Flood 2008) As an example of the magnitude of leadership influence on the attitudes and behaviors of employees, a study of 25,000 workers across a variety of firms by Wilson Learning, a US based management training company; found that 69% of employees job satisfaction related to the leadership skills of their bosses (Davids 1995). A significant productivity lag was associated with a lack of or poor leadership. On the basis of this finding, Davids (1995) asserts that the days of the heroic leader who gets things done by people are numbered, to be replaced by the post-heroic leaders who get things done with people. Hence, mutual understanding, trust and strong communication skills have been growing in importance as factors in the leader follower relationship.(Flood, 2008) 3.0EMPLOYEE WELLBEING An organisation that has an environment which promotes a state of contentment allowing employees to flourish and achieve their full potential for the benefit of themselves and the organisation can be considered as employers who puts high emphasis on their employee wellbeing.(Tehrani et.al 2010) The concept of wellbeing includes concepts of psychological and physical health. According Arnold (2007) they can be distinguished between subjective and psychological wellbeing à ¢Ã¢â€š ¬Ã‚ ¦Subjective wellbeing focuses on the affective (hedonic balance; balance between pleasant and unpleasant affect) and cognitive (life satisfaction) components of well-being (Arnold et.al 2007). Psychological well-being draws on various conceptualizations of mental health (Arnold et.al 2007).Tehrani et .al (2010) described wellbeing as a subjective experience that may involve practical measures such as introducing healthy food or a gym at work, or perhaps less tangible initiatives such as working to match the values and beliefs held by employees with those of their organization. It could be argued that a change in the way employees are engaged in discussions about how their work is organized could have more of an impact on an individuals well-being than the introduction of a corporate gym. Today increasingly companies are focused about their employees wellbeing as organisations are seeing its benefits .Organisations such as IBM, established Well-Being Management System (WBMS) the companys holistic approach to managing the health and safety of employees wherever they work. This enables physical and psychological fitness of its employees. This integrated approach to employee well being ranges from the more traditional aspects of occupational health and safety ,such as industrial hygiene, safety, medical issues and ergonomics-to innovative and proactive wellness initiatives, including a broad array of health promotion options and disease prevention benefits for employees.(IBM , 2010 ) Marks Spencer is known as an employer who values employees. Historically, well-being was delivered through a traditional benefits package, including flexible working and family-friendly policies. In addition, a good physical working environment was provided, including good catering facilities. Other ancillary benefits for some or all employees included hairdressing, chiropody, dentistry and holistic services. As the business moved through a change management program it was realized that more focus was needed on the health and well-being areas that were directly affected by the workplace. (Tehrani et.al 2010) Scota Chropractc Ltd is another company that focused on employee wellbeing . Established in 1986 and currently employs 17 employees working across two sites the growing public awareness of alternative and complementary therapies has been a major driver of business growth. The company works with the NHS and has recently branched out into sports therapy, rehabilitation and ergonomic-based heath and safety interventions. The organization has increasingly concentrated on employee well-being for its own staff during the past five years. (Tehrani et.al 2010) 3.1 Employee wellbeing and culture. Organisational Culture is defined as a set of shared values beliefs which interact with an organisations people ,structureand system to produce behavioural norms. in a strong organisational culture the organisation core values are both intensly held and widely shared. core values are the primary or dominant values that are accepted thoughout the organisation.the more members who accept the core values andthe greater their commitment to those values is , the stronger the culture gets.A strong organisation culture will have a great influence on the bevaiour of its members((Robbins 1998) when employee wellbeing becomes a shared organisational value deeply rooted in the organisational culture it is evident from the following companies that productivity of the organisations increases as a result of high moral and satisfaction as well as organisations are able to retain their talents. According to Tehrani et.al(2010) Marks Spencers three-month wellbeing trial programme demonstrated an 8% reduction in its employee sickness absence for musculoskeletal health issues. Store management teams reported improved morale of the departmental team and the general store, all of which are difficult to estimate in financial terms, further improvement in customer service and improved efficiencies. Nike company employees share a core value of enhancing peoples lives through sports and fitness.nike has created a strong sports oriented culture and promoted it through company practices such as paying employees extra for biking to work instead of driving .( Robbins 1998) Companies adopting such measures aimed at promoting health and well-being among their employees influence several aspects of their employees physical and psychological well-being in ways which result in improved productivity, commitment and attendance. This includes providing good quality jobs which allow employees more control, autonomy and involvement in the way their work is done (Coats et.al 2008) Recently an OEM manufacturer, FOXCONN of china supplies to companies like DELL, APPLE and Hewlett-Packard came under scrutiny for its below standard employee relations and maltreatment.Some estimates put the companys labour turnover in to 50000 a month and reports claim a drop in recruitment standards to just about anyone with an ID. (Engadget 2010) 4.0 Transformational leadership and culture Organisational Culture is defined as a set of shared values beliefs which interact with an organisations people ,structureand ststem to produce behavioural norms. Values influence attitudes and behaviour (Robbins 1998)therefore For transformational leaders to create employee wellbeing as a shared value they need to create trust between leader and employees. This is possible when the leader practice high emotional and social intelligence and honour the psychological contract between the management and employees. Leaders facilitate in creation of value for employee wellbeing and facilitate in embedding it in to the organisational culture. They achieve this by creating trust among employees towards the leader as well as between each other and develop emotional and social intelligence of employees and honouring the psychological contract. 4.1Trust trust is a characteristics of high performance teams where the relationship between members is based on integrity ,competence, consistency, loyalty and openness (Robbins 1998 ) According to Rosen (1996 ) trust is one of the major principles in leading people.it binds people together creating a strong resilient organisation. It cannot be achieved over night according to Kouzes et.al ( 1987) but takes years to learn however an instance to loose it. Idealised influence dimension of transformational leadership helps create such relationship of trust and confidence through its attribution charisma. They are thought to display certain attributes (eg.percieved power, focus on higher order ideals and values. When this happens the followers develop an emotional tie to their leader which ultimate results in the trust specified above.(Arnold 2007) Transformational leaders gain follower trust by maintaining their integrity and dedication, by being fair in their treatment of followers, and by demonstrating their faith in followers by empowering them. It has been suggested that one way that charismatic and transformational leaders can demonstrate their dedication and build follower trust is through self sacrificial behaviours.Leaders can self sacrifice by taking on a proportionately larger workload, by foregoing the trappings of power (e.g. Gandhis peasant lifestyle), or by postponing rewards, such as Chryslers Leelacocca and Apples Steve Jobs deciding to work for 1$ a yr ..(Bass et.al 2006) Transformational leaders build trust by practicing open communication between employees and leaders. mistrust comes from what people dont know as from what they do know (Robbins 1998 ).They are excellent team players ,and they lead by example, support the teams through words and actions and demonstrating loyalty as well as treating them with respect and considering their perception in terms of objectivity and fairness in decision making. They show consistency in basic values that guide their decision making and they maintain confidences and become someone who teams can rely on. 3.2Emotional intelligence Goleman (1995) describes emotional Intelligence as self-awareness, managing our emotions effectively, motivation, empathy, reading other peoples feelings accurately, social skills like team work, persuasion, leadership and managing relationships. Transformational leaders have high emotional intelligence. They properly manage emotions that drive trust, loyalty, and commitment. The leader enables People to recognize their own emotions as well as others ,differentiates those emotions to make choices for thinking and action.(Cooper et.al 1997).It is an intelligence that may be learned, developed and improved (Perkins 1994). Emotional intelligence is the ability of a person to use his awareness and sensitivity to detect, identify or understand the feelings underlying interpersonal communication and avoiding to respond on impulse and thoughtlessly, Instead to act from receptivity , authenticity ad candor.(Ryback 1998). Emotional intelligence is about influence without manipulation or abuse of authority. It is about perceiving, learning, relating, innovating, prioritizing and acting in ways that take into account and legitimize emotions, rather than relying on logic or intellect or technical analysis alone (Ryback, 1998). Transformational Leaders are highly self aware facilitating them to read ones emotions and recognize their impact while using gut feelings to guide decisions. Their social awareness enables them to sense, understand and react to others emotions while comprehending social networks. As a result of these qualities and the ability of transformational leaders to manage self inspire employees influence them and develop these qualities among them by managing conflict. This successfully results in resolved issues of workplace bullying and increase in employee satisfaction leading to higher performance. Goleman, (2010 )have shown that high levels of emotional intelligence can create climates in which information sharing, trust, healthy risk taking and learning flourish. as well as how workplace competencies based on emotional intelligence can exert greater impact on performance than do intellect or technical skills. 4.3Psychological contract When an employer distributes a performance bonus every year since it inception, employees within that particular company will expect to receive it in the coming years as well, because of the fact that every year the company distributes it. The point here is that Humans beings are very adaptable to situations .the more leaders practice employee wellbeing initiatives within the organisation the likely it will become an expectation in the psychological contract between employers and employees. Psychological contract defined by Robbins (1998) states that it is an unwritten agreement that sets out what management expects from the employee and vice versa. It sets out mutual expectations. Everyone performs different roles both at work place and society. And each role demands attitudes and behaviours consistent to that particular role. Therefore both the organisation and the employee have certain expectations of that role which could mean acceptable working conditions, clear communication and a fair days work etc for employees and for organisations demonstration of a good attitude following instructions and showing loyalty to the organisation.(Robbins 1998) However when these expectations are not met trust between the employees and employers is lost, resulting in negative effects on employee performance and satisfaction. Globalisation and work force diversity further complicates the situation. Although Members of different groups share common within their group common values, attitudes and perceptions much diversity exists within each of these categories. (Bateman et.al 2009).similarly in multi racial societies such as the United States for example values shared among Asian Americans differ from values shared among Asians living in Asia. Since values influence the attitudes and behaviours of employees attaining a common shared value among the multi ethnic groups and multinational groups within an organisation is a challenge to the transformational leaders. As each of these groups will have different expectations from employers. 4.4 Social intelligence However the transformational leaders ability to connect with individuals at a personal level enables them to build a relationship of trust and influence their behaviour and attitude to share a common value which is emotional and psychological wellbeing of employees. Many leaders are appointed because of their drive, ambition and business expertise but often they are unable to work with or get along their director colleagues, colleagues and direct reports, or with others on whom their own success depends. Building on his work on emotional intelligence, Daniel Goleman coined the phase Social Intelligence, in which he enlarges his focus to encompass our capacity to connect with one another.We are wired to connect Neuroscience has discovered that our brains very design makes it sociable, inexorably drawn into an intimate brain-to-brain linkup whenever we engage with another person. (Goleman 2010) Therefore in order to identify the attitudes , values and perceptions of people the leader needs to have social intelligence skills to connect to the individuals or initiate emotions in order to gain trust which ultimately leads to acceptance of ideologies and behaviours the leader intends to communicate which in turn becomes the norm of the organisation. 5.0 Employee wellbeing, Culture and performance Harter etal (2002 ) believes that worker quality of life and performance originates with the behavioural, cognitive , and health benefits of positive feelings and positive perceptions.according to him proponents of the well being perspective argue that the presence of positive emotional states and positive appraisals of the worker and his or her relationships within the people seek out interesting , meaningful, and challenging tasks.when demands match or slightly exceed resources, individuals experience positive emotional states (e.g. pleasure ,joy, energy) and they perceive themselves as growing , engaged, and productive .From the wellbeing perspective, a healthy workforce means the presence of positive feelings in the worker that should result in happier and more productive workers. a survey carried out by Aon Consulting on UK workers found out that more than 35 million sick days a year are taken for personal reasons rather than for a genuine illness and one in three UK workers (33 per cent ) say that the last time they took a day off from work as sick leave were addressing personal issues including looking after a family member, letting repairmen into their home, grieving a dead relative or pet, or feeling down after breaking up with a partner and they did not have anything wrong with them . 52 per cent of British say they would not feel forced to take a day as sick leave if they could just be honest and have access to flexible working hours or social days, said Peter Abelskamp, director of health and benefits EMEA, at Aon Consulting. Of course, employers should also not ignore the fact that 16 per cent of people say that more interesting work would keep them in the office (PM Online, 2010) In 2003 the Royal Mail sickness absence levels were 7 per cent (an average of 16 days per employee per year) As a result their Customer service standards were affected and incurred a daily cost of  £1m.the company had been experiencing issues of long-term absence for many years which was commonly due to musculoskeletal health. Therefore Royal Mail introduced a range of integrated measures to counter the problem such as as à ¢Ã¢â€š ¬Ã‚ ¢ Health screening à ¢Ã¢â€š ¬Ã‚ ¢ Health clinics at 90 sites à ¢Ã¢â€š ¬Ã‚ ¢ Fast access to occupational health services à ¢Ã¢â€š ¬Ã‚ ¢ Access to physiotherapy à ¢Ã¢â€š ¬Ã‚ ¢ Employee assistance programme (EAP) à ¢Ã¢â€š ¬Ã‚ ¢ Incentive scheme à ¢Ã¢â€š ¬Ã‚ ¢ Rehabilitation centres focusing on improving back, neck and shoulder injuries à ¢Ã¢â€š ¬Ã‚ ¢ Phased and partial return to work (RTW) à ¢Ã¢â€š ¬Ã‚ ¢ Case management four years later, it was reported that sickness absence levels had fallen to 4 per cent (10 days per employee) and saved Royal Mail almost  £230m. Up to 3,600 more staff were available to work each day as a result of these measures (Scribed,2010) 8.0 CONCLUSION In this fast changing global environment organisations are increasing needing leaders who concentrates on building relationships with their employees in order to increase productivity and meet the global demands. Emphasis on employee wellbeing has become a trend in todays companies which is seeing its benefits. However in order to produce a full impact leaders need to create wellbeing as a shared core value deep rooted in the culture of the organisation. This can be achieved only when the leader creates trust among employees as well as between him. This is only possible if the leader has high emotional and social intelligence and promotes it within employees and finally honouring the psychological contract between them. Therefore transformational leadership can bring about a change in organisational culture by creating values which will in turn influence the attitude and behaviour of the employees in this case employee wellbeing as a core value in the organisational culture.