Research Paper for Computer Game Addicted free essay sample

From the time PC games advanced into family homes, guardians have thought about whether brutal computer games may contrarily influence kids and young people. Today, guardians despite everything stress over the impacts of brutal PC games and obviously it likewise stays a well known subject for the media. In any case, with a developing number of kids and youngsters investing unnecessary energy playing PC games, maybe guardians ought to be progressively stressed over adolescents dependent on PC games than the impacts of rough computer games. An assessment of the writing uncovers that the individuals who study computer game fixation at times differ on the extent of kids and young people dependent on PC games. Nonetheless, most of studies recommend that roughly 5 to 10% of youth who play PC games become dependent. Contrasted with other mental troubles, (for example, wretchedness and nervousness), high school PC game dependence is clearly a generally new issue looked by families. All things considered, guardians may need exact or potentially supportive data on the indications of PC game dependence, the hazard factors for computer game habit, and procedures for helping youngsters dependent on PC games after the issue creates. We will compose a custom article test on Research Paper for Computer Game Addicted or on the other hand any comparable subject explicitly for you Don't WasteYour Time Recruit WRITER Just 13.90/page As more specialists work with adolescents dependent on PC games and more scientists study the issue, they are getting better at recognizing the indications of fixation, testing for computer game dependence, and offering assistance to guardians with young people dependent on PC games. All things considered, there is a lot of disarray about precisely what PC game enslavement is and how guardians can help an adolescent who appears to be unquestionably increasingly keen on playing in a virtual world than living in reality. Young people Addicted To Computer Games Advice for Parents 1. For the present, PC game dependence isn't an authority mental confusion. The chance of incorporating PC game compulsion in future releases of the Diagnostic and Statistical Manual of Mental Disorders is being discussed, yet right now it only an approach to portray somebody whose life gives off an impression of being contrarily affected by exorbitant gaming and is certifiably not a perceived fixation. . Sound judgment proposes that the more a youngster plays PC games, the almost certain his play has traversed into the undesirable degrees of play classification. In any case, since PC game habit isn't an official finding there is no set number of hours out of each day implying a dependence. In this way, notwithstanding estimating the normal number of hours he/she plays every day, it is maybe increasingly essential to inspect how PC gaming is meddling with a youngsters social connections, school execution, mind-set, and improvement of relational abilities. . Guardians who are concerned that their adolescent is investing an excessive amount of energy playing PC games and disregarding different exercises ought not expect that their youngster will in the long run get exhausted of computer games and that gaming is essentially a stage. Valid, a few young people dependent on PC games do in the end create different interests and their gaming subsides†¦but there are likewise the individuals who grow significantly increasingly extraordinary gaming propensities as they get more established. Guardians ought not rely upon the difficult dealing with itself. PC game enslavement should be gone to when guardians perceive that it is causing critical hindrance in other significant territories of the teenss life. 4. Most children and youngsters can and do play PC games without building up an enslavement. Notwithstanding, for certain adolescents there is no doubt that their PC use is undesirable and unreasonable by anyones principles. For these youngsters, PC games take need over every other action, and improvement in different zones (for instance, school, connections, clubs, sports) is relinquished with the goal that additional time can be spent before the PC screen. It truly doesn't make a difference if this is called a compulsion or not. On the off chance that he keeps on playing regardless of encountering huge negative results in different aspects of his life (e. g. , overlooking school or companions) his PC gaming is an issue and it needs consideration. 5. For guardians, a most concerning aspect regarding young people dependent on PC games is the impact it has on their childs scholastic execution. On the off chance that an understudy consistently accomplished As and Bs preceding getting snared on PC games however is presently just bringing home Cs and Ds, guardians ought to build up clear guidelines around gaming (counting the chance of a boycott) until marks have improved. Moreover, it is important that any guidelines around gaming are set, yet reliably implemented. Setting confinements on PC games however not finishing by upholding the new standards is conceivably more destructive than having no constraints by any stretch of the imagination. 6. When all is said in done, rewarding adolescents dependent on PC games necessitates that all PCs as well as game consoles are expelled from their room. Obviously, this is surely by all account not the only intercession, however it is practically inconceivable for a parent to effectively put restrains on gaming if the kid can at present access the game in the security of a room. 7. The most well known treatment approach for PC game enslavement is psychological social treatment (CBT). CBT for gaming enslavement includes testing and supplanting undesirable musings (insights) about PC games (e. . , my gaming doesnt hurt anybody) and acquainting progressive conduct changes with diminish the time spent playing computer games (e. g. , compensations for keeping new principles, fitting ramifications for time limit infringement, arranged suggestions to quit playing, programming arrangements, inclusion in different exercises, recognizing and decreasing empowering practices, maintaining a strateg ic distance from natural triggers, and so on ). 8. Guardians looking for help from a specialist or clinician ought to consider meeting with the person in question preceding the main meeting with their kid. Despite the fact that the issue of young people dependent on PC games is being paid attention to additional by emotional wellness experts, there are the individuals who excuse the chance of computer game option altogether and spotlight just on finding the underlying driver of the issue. The advisor ought to at any rate think about how conceivable it is that PC game compulsion is the essential issue and offer direct treatment for this issue if fundamental. Identified with this point†¦ 9. On occasion, PC game compulsion is an indication of another mental, passionate, or relational issue. Valid, PC game dependence can be the essential introducing problem†¦but there is significant proof that exorbitant PC use likewise can be activated or exacerbated by troubles, for example, sorrow, nervousness, and poor social abilities. For instance, an adolescent who is battling with melancholy may grasp PC games since he doesn't feel equipped for managing true issues. Obviously, going to internet games is probably not going to make the difficulties he faces in reality leave, and possibly exacerbates them even. In this model, the advisor must treat the unfortunate gaming, yet additionally the downturn which might be taking care of the dependence. 10. In spite of the difficulties and inescapable disappointments of managing high schooler PC game dependence, guardians must recollect never to surrender adolescents dependent on PC games. Albeit changing unfortunate computer game propensities isn't in every case simple, high school PC game dependence can be dealt with particularly if guardians have the basic data and systems they requirement for arranging a fruitful computer game enslavement mediation.

Dubaya :: Essays Papers

Dubaya He brought his dad's authentic name, degrees from Yale and Harvard, some $13,000 left in his trust reserve, and his most grounded individual resource †an overflowing appeal spiked with zingers. Bramble never discovered a lot of oil in Texas, yet he gradually discovered his direction. He wedded and fathered twin young ladies, quit drinking, started contemplating Scripture, and made his a fruitless invasion into the privately-owned company by running for Congress. He figured out how to court companions and political supporters of his dad, the VP. What's more, he snared with the oil speculators who might in the long run assist him with turning out to be overseeing accomplice of the Texas Rangers baseball crew. Shrub utilized the Rangers post to develop big name status and plan for a gutsy, winning test to Democratic Gov. Ann Richards in 1994. The Rangers bargain additionally made him a multimillionaire. George Walker Bush was brought into the world July 6, 1946 in New Haven, Conn., where his dad, effectively a flying legend of World War II, was charging through Yale. At the point when he was 2, his folks moved West to pursue the oil blast. Be that as it may, youthful George additionally persevered through incredible distress at age 7, when his younger sibling Robin passed on of leukemia. The following kid, presently Florida Gov. Jeb Bush, was seven years more youthful. Three others followed: Neil, stung by the S&L outrage of the 1980s and now a business advisor; Marvin, an investor; and Doro, spouse of a Washington lobbyist and mother of four. None appears to have felt the heaviness of their dad's victories as much as the oldest, frequently called ''Junior'' despite the fact that he's one name shy of George Herbert Walker Bush. He followed his dad's way to private academy in Andover, Mass., and afterward Yale, yet neglected to satisfy his heritage in scholastics or sports. Rather, he's recollected at Andover for sorting out stickball competitions and luxurious pre-game events that lit up an in any case inflexible grounds. At Yale, similar to his dad, he was tapped for the mystery Skull and Bones society and became leader of Delta Kappa Epsilon. Organization siblings recall him as ''the life of the gathering'' among a gathering engrossed by lager, sports, soul music and, obviously, young ladies. Companions state Bush dodged the early Vietnam War fights at Yale and didn't creek analysis of his dad, at that point a Texas congressman supporting the war. Quickly before graduation in 1968, Bush pursued pilot preparing in the Texas Air National Guard, where it was impossible he would be sent to Vietnam.

White Noise A Real Dystopia - Literature Essay Samples

Don DeLillo’s post-modern novel White Noise examines the relativity of meaning in a consumer and media-controlled society. A classic dystopia comments on society’s reliance on the media, and in White Noise, it creates character identity instability and hyperreality. However, White Noise does not completely portray the conventional dystopia; the lack of a dystopian hero fighting to expose the malfunctioning society, in addition to the absence of a controlling power illustrate the hopelessness of a modern culture revolving around the media. White Noise fits the dystopian model in one aspect with Jack’s construction of his identity by the surrounding culture; this persona emphasizes his desire to find identifying legacy that will prevent him from dying. Jack’s malleable identity is strongly influenced by his surrounding society and his peers. When Jack is called â€Å"indistinct† (83) by a colleague, he creates an academic facade that will immortaliz e his image. As a Hitler innovator, â€Å"Jack wanted to be taken seriously â€Å"(16). He realizes that he has to create his own persona; he adds an extra initial to his name and always wears â€Å"thick black heavy frames and dark lenses† (17). The now J. A. K. Gladney wears his new identity â€Å"like a borrowed suit† (17). This persona that Jack has created for himself as a Hitler innovation emphasizes his desire to create a legacy that will live on forever. Jack’s evident fear of death is relived in his association with Hitler. He feels immortal when correlating with the unforgettable image of Hitler, even though he knows he is â€Å"the false character that follows the name around† (17). Jack wraps himself in the man’s gruesome, yet powerful and historical image; Hitler’s genocide of millions of people makes Jack’s own inevitable death seem insignificant. Murray calls Hitler â€Å"larger than death,† expressing the eve rlasting image of his legacy. The sheer name of â€Å"Hitler† and his immortal legacy attracts Jack—he is fascinated with molding his own personal identity. Jack hopes immersing himself in Hitler’s persona will make him greater than death and erase it altogether. Nevertheless, the creation of this alter-ego unavoidably becomes ambiguous—Jack cannot differentiate between the reality and imaginary of his existence, revealing one dystopian element of White Noise. The hyperreality of the White Noise dystopia, especially in the TV and radio, reveals how the media has the ability to radically shape and filter an original event or experience. Jack isn’t the only character who has trouble differentiating between reality and the imaginary in this partially dystopian society. One of the most prominent examples of this hyperreality is SIMUVAC, or Simulated Evacuation. This group consistently practices for disasters, yet they are unaware of how to deal in a real situation. Their first â€Å"practice† happens to be in the airborne toxic event, a real disaster. Jack questions this situation: â€Å"A form of practice? Are you saying you saw a chance to use the real event in order to rehearse the simulation?† (139) Their status as a simulation takes precedence over a real disaster, satirizing human inability to discern between reality and simulations. DeLillo’s humor is shown when the SIMUVAC worker states during the real disaster that they â€Å"don’t have our victims laid out where we’d want them if this was an actual simulation.† It is evident that the simulations are more important in the eyes of the population, illustrating how simulacra has replaced reality. Perhaps these simulated events were created to give the community a sense of control. These evacuations allow them to plan out a natural disaster in every possible detail which supposedly prepares them for a real disaster. When a real dis aster comes, however, they are unprepared—they are only comfortable in their own, created simulations. Furthermore, the simulations are often mistaken for reality by use of the media. An example of the media using these simulations is when Babette appears on TV. As they watched, out of their â€Å"mouths came a silence as wary and deep as an animal growl. Confusion, fear, astonishment spilled from [their] faces† (107). This is the family’s first experience with a family member being projected through the simulation media. At first, they have a difficult time distinguishing Babette through the distorted pixels on the TV. Besides, when they do recognize her, they only believe her to be a collection of pixels and light. Jack and his family watched Babette â€Å"shining a light on us, she was coming into being, endlessly being formed and reformed as the muscles in her face worked at smiling and speaking, as the electronic dots swarmed† (107). The picture wit h a distorted sound is confusing to the family. This appearance of Babette on TV illustrates the hyperreality of Jack’s family and how they cannot differentiate between reality and simulation. The family, except for Wilder, believes Babette is a different person when on TV—she was seen â€Å"as some distant figure from the past, some ex-wife and absentee mother, a walker in the midst of the dead† (104). Jack cannot immediately comprehend Babette’s appearance as a simulation; she appears as some unknown character, a figure of the past. Wilder is the only one who believes the TV to actually be Babette, as he has not been exposed to this type of simulation. Wilder cries when the screen turns black, illustrating his confusion in the â€Å"real† Babette and in the electronic dots creating her Babette. What sets White Noise apart from other dystopian novels is its lack of a dystopian hero; Jack’s fear of death and fear of being exposed as an insi gnificant man illustrates his stark difference from the normal dystopian hero and the inevitable failure of a media-driven society. Jack’s fear of death seems to overwhelm him—the discovery of the Nyodene D. and its vague diagnosis only adds to his paranoia. The chemical remains in his body for 30 years and he cannot be diagnosed for 15 years, which is another aspect of death that Jack cannot control. Although he establishes Hitler studies to study the everlasting life and death of a famous character, Jack still manages to fear his own death. Furthermore, this protagonist does not want to be exposed as an incompetent teacher. He has not mastered the basic skills of German, even though he is the head of the Hitler department. Jack created this persona to appear as an intellectual, yet the masking of his true identity does not embody the actions of a true hero. Jack’s worries about death only accumulate in his life; he eventually chases after Willie Mink to obt ain Dylar for suppressing his fear of death. Is this how a dystopian â€Å"hero† would act?—succumbing to a placebo drug to mask his fear? Jack, in the end, does not expose the media-centered society to its citizens nor does he fix the society. In fact, it seems as though this society accepts the false stories of the media to disguise their imminent fear of death. This promising hero merely accepts his fear of death, illustrating the inevitable failure of this media-driven society. The letdown of this consumer and media society is mainly a result from the dystopian character’s instability and hyperreality. Although White Noise exhibits many dystopian characteristics, its lack of a modern hero reveals the portentous hopelessness of a society controlled by the media. DeLillo’s novel is hardly fiction—it is an ominous truth of what are society could easily become. The failure of the White Noise society questions our need for a modern hero to expose a nd save our own similar dystopia.

Code Napoleon and “Declaration of the Rights of Man”...

Code Napoleon and â€Å"Declaration of the Rights of Man† Comparison The longest lasting effect of Napoleon Bonapartes rule over France was his overseeing the implementation of a series of national laws collectively known as the Civil Code, or Code Napoleon. Code Napoleon was the successor to the idea’s stated in The Declaration of the Rights of Man and Citizen, While at first, Napoleon generally adhered to the philosophies of the French Revolutionist as created in The Declaration of the Rights of Man and Citizen, as time progressed, his absolute power allowed for corruption at the expense of the French people. Napoleon violated almost every principle in the Declaration of the Rights of Man and Citizen in order to benefit his own†¦show more content†¦This went directly against the Declaration of Rights law that stated â€Å"The free communication of ideas and opinions is one of the most precious of the rights of man. Every citizen may, accordingly, speak, write, and print with freedom, but shall be responsible for such abuses of this freedom as shall be defined by law† He also helped relations achieve positions of power in Europe, ignoring the principles of the meritocracy which he had earlier instilled. Possibly the most important article of the Declaration of the Rights of Man and Citizen is the first one, which summarizes all the other articles by demanding equal rights for all citizens. The cry for equal rights was one of the major demands the Third Estate wanted from the monarchy even at the beginning of the French Revolution. Early in Napoleons’ campaign he stated that he supported the equality of citizens in France, but in 1802 he created the Legion of Honor, a new status in society which granted members special privileges. The legion was a superior group, similar to nobility, but Napoleon of course never called it that because he wanted to convince the general public that the idea of equality was still relevant. The Declaration of the Rights of Man stated that â€Å"Men are born and remain free and equal in rights’† and that social distinctions may be founded only upon the general good.† In this instance Napoleonic Code both lives up to the ideals o f, and also fails to live up to the ideals of its predecessorsShow MoreRelatedEffects Of The French Revolution On The World1907 Words   |  8 Pagesof the French Revolution (Furet 49-51). The ideas and documents created as a result of the French revolution may have had a significant impact on human rights and modernized law and politics throughout the world. One of the important legacies of the French Revolution was Declaration of the Rights of Man and Citizen and its impact on human rights. During the summer of 1789 several revolutions rocked France that effected decisions made later. These revolutions included the revolution of the DeputiesRead MoreThe French Revolution And The Civil War2964 Words   |  12 PagesJuly 14, 1789 and July 28th, 1794. The word â€Å"civil war† did not mean only the bourgeois revolution. It included a broader sense than only bourgeois, because it was a whole national revolution for all the people to establish himself as free man, and to hold equal right. The origin of French Revolution are complex (584). There are involve so many connected factors such as political, economic, social, and ideological reasons. One of the reasons for the French Revolution was new political ideas that wasRead MoreThe Election Of The 2016 Presidential Election1537 Words   |  7 Pagesan ordered procedure of appointment or dismissal... The leader knows of no abstract codes and statutes†¦ Charisma knows only inner determination,† ruling by declarations of personal will. 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An earlier version of the book was published by Wadsworth Publishing Company, Belmont, California USA in 1993 with ISBN number 0-534-17688-7. When Wadsworth decided no longer to print the book, they returned their publishing rights to the original author, Bradley Dowden. The current version has been significantly revised. If you would like to suggest changes to the text, the author would appreciate your writing to him at dowden@csus.edu. iv Praise Comments on the earlierRead MoreStrategic Marketing Management337596 Words   |  1351 PagesHouse, Jordan Hill, Oxford OX2 8DP 200 Wheeler Road, Burlington, MA 01803 First published 1992 Second edition 1997 Reprinted 1998, 1999, 2001, 2003 Third edition 2005 Copyright  © 1992, 1997, 2005, Richard M.S. Wilson and Colin Gilligan. All rights reserved The right of Richard M.S. Wilson and Colin Gilligan to be identified as the authors of this work has been asserted in accordance with the Copyright, Designs and Patents Act 1988 No part of this publication may be reproduced in any material form (including

Analysis of the Inferno of Dante Alighieris Divine...

Analysis of the Inferno of Dante Alighieris Divine Comedy The Divine Comedy by Dante Alighieri is considered by many as the first great poem in the Italian language and perhaps the greatest poem written in Medieval Europe. The poem is so famous that one of the minor characters, Capaneus the great blasphemer, has his name on a mesa on one of Jupiters moon Io (Blue, 1). Also, the poem is divided into three canticles, or sections, Inferno, Purgatorio, and Paradisio. For the purposes of this paper, only Inferno will be discussed. In Inferno, Dante the Pilgrim is lost. In his wanderings he encounters three specters, the leopard, the lion, and the she-wolf. Dante runs†¦show more content†¦However, again, for the purpose of this paper, only their occurrences in Inferno will be discussed. These symbols are important in understanding Dantes real meaning and in understanding how the different ways the story can be interpreted fit together. Dante, in Divine Comedy, makes it clear to us when he wrote it. His subtle hints at dating the story allow us to pinpoint the story to Easter Week, 1300. Even though he never comes out and says it, his language gives it away. In the first line of the entire poem, Dante tells us how old he is when this occurs. In the midpoint of our mortal lives, I find myself in a gloomy wood, here Dante tells us that he is 35 years old when this occurs (Cary 8). In another part of the story, Dante tells us about astronomical occurrences that occur in his journey. Here Dante is describing the constellation Pieces setting. From this we know that Canto VIII occurs at 6:52 am on the Thursday before Easter. At the beginning of Canto IX, Dante describes another astronomical occurrence. From this description of the moon setting, we know that Dante crosses the bridge in 8 minutes because the moon sets that day at 7 am in Florence. Why is this important? John Carlyle tells us that the chronology of Divine Comedy is one ofShow MoreRelated Analysis of Robert Frosts Fire and Ice Essay1087 Words   |  5 PagesAnalysis of Robert Frosts Fire and Ice      Ã‚  Ã‚   For Robert Frost, poetry and life   Ã‚  Ã‚  Ã‚  Ã‚   were one and the same.   In an interview he said, One thing I care about,   Ã‚  Ã‚  Ã‚  Ã‚   and wish young people could care about, is taking poetry as the first form   Ã‚  Ã‚  Ã‚  Ã‚   of understanding.   Each Robert Frost poem strikes a chord somewhere, each   Ã‚  Ã‚  Ã‚  Ã‚   poem bringing us closer to life with the compression of feeling and   Ã‚  Ã‚  Ã‚  Ã‚   emotion into so few words.   This essay will focus on one particularRead MoreCanto Xx of Dantes Inferno1074 Words   |  5 PagesAn Analysis of The Souls Damned in Canto XX from Dante Alighieri’s Inferno Introduction Virgil and Dante find themselves in Circle Eight, Bolgia Four. The damned in this circle are all diviners and soothsayers, viewed by Dante as practitioners of impious and unlawful arts who attempt to avert God’s designs by their predictions. Virgil implies that those who do prophesy believe that God Himself is â€Å"passive† in the face of their attempts to foresee, and possibly change, the future. For such impietyRead MoreAnalysis Of `` Inferno And Thomas More s Satirical Dialogue `` Utopia ``1366 Words   |  6 Pagescharacters, and theme.. Dante’s Inferno and Thomas More’s Utopia are perfect examples of the use of irony as they utilized the various techniques throughout their stories. There are a plethora of accounts where irony is apparent, including the sceneries, dialogue, and titles that are portrayed in their work. This essay will examine and compare the uses of irony in Dante Alighieri’s narrative poem, Inferno and Thomas More’s sa tirical dialogue, Utopia. Dante’s Inferno describes distinctive uses of

Reflective Discourse Managing Projects

Question: Discuss about the case study Reflective Discourse for Managing Projects. Answer: Introduction: After the completion of the first term of my operations Management course, I was able to bring out the different new things from my study and experience. I have gathered the vast amount of knowledge on managing projects which will be helpful for me in my future career or future jobs. The study of different disciplines of operation management critically reflects the information that the use of these disciplines is considered to be of large significance for managing any of the projects in future jobs or future careers. I am a keen learner and have gathered potential knowledge from the study on managing different projects successfully. There are certain elements which are considered to be of great importance within Project Management. The concept of managing project includes time management, cost management, quality management, and management of activities and monitoring and controlling the entire project. Moreover, teamwork is needed for the concerned workplace in order to bring out the positive outcomes for the development and the execution of the activities for the respective projects. There are certain problems which come between the execution of the project and I have not developed the full potential to get solutions for these issues but effective piece of teamwork helps in enhancing the complete project. I feel that, in order to increase the efficiency of the concerned project, the most crucial is to maintain the quality and to deliver the project within the time provided. The overall course e nhanced my skills related to managing different types of projects which are important for my future career jobs. It became very crucial for me to understand the different aspects and disciplines of operations management as it would help in bring out the desired idea for managing the different Projects. Different employees execute most crucial role in enhancing the projects allocated to them within their concerned firm. Therefore, there are some of the crucial points which need to look upon as to maintain the effectiveness as well as the efficiency of the project. It is very important to evaluate the different factors and the elements which affect the ongoing project in any of the respective organization. I came across different facts that the manager or the leader plays the most significant role in evaluating the desired execution and evaluation of the ongoing project within the time-frame provided. There comes the crucial need for looking on the entire project. The monitoring of the different tasks allocated to the team members within the particular projects helps in identifying the particular issues or difficulty of the staff members after which it can be sought out. Collaboration and cooperation are supposed to be the two of most crucial strengths of the team members executing the respective project. Moreover, I feel that the while completing the project there are many other areas which will help in providing the vast amount of experience in managing projects in future career jobs. Overall, I think that the performances and the efforts of the team members matter for the successful completion of the concerned projects. It becomes most imperative for the respective leads or the mana gers to look upon two of the critical areas such as the social effective interaction with the squad and the progress that the team is achieving. Use of the effectual strategies merely work to increase the satisfaction level of the customers as well as the firm and therefore here, the use of the best strategies needs to be considered as this will help in achieving the goals of the organization. While studying the entire concepts of operations management along with managing projects, I find it useful to identify the importance of this study as applicable in my future jobs. It has been seen that the effective team works bring out the desired success for any of the project and it is also useful in maintaining the quality of the project. There are certain basic metrics which are crucial for the evaluation of the concerned management of any project; they are price, quality, flexibility, time and the stock availability. Project management is regarded as the most significant part of operations management and each of the team members should understand its basic concepts as this will bring out the positive results of the whole project for the particular organization. The implementation of these disciplines and ideas will help to enhance the overall reliability of the project and will also provide relevant experience for the future jobs and careers.

Police Department Organization Paper free essay sample

Some very small communities hire only one officer, who fills the roles of chief, Investigator, and night watch-?as well as everything In between. (Frank Assemblages 2007) But on the other hand, there are state police agencies that have their own roles and functions state law enforcement agency whose principal functions usually include maintaining statewide police communications, aiding local police in criminal investigations, training police, and guarding state property. The state police may include the highway patrol.Most states have police agencies in addition to agencies within specific municipalities, townships or counties. The power of most state agencies Includes the ability to arrest an individual for an offense committed In the presence of the officer, as well as the ability to execute a search warrant. In addition to the state police, some states have established a Highway Patrol with Jurisdiction over traffic laws on Interstate roads. These patrols have the authority to enforce traffic laws as well as investigate traffic accidents on highways and freeways. We will write a custom essay sample on Police Department Organization Paper or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page Hawaii s the only state without a state law enforcement agency. Furthermore, there are Federal agencies the first agency to be established by the U. S. Government was the U. S. Marshall Service, which was founded in 1789. Since that time, the U. S. Government has created eight additional government departments with twenty-one agencies dealing with issues of law enforcement. It is important to note that federal agencies only have the power to enforce federal laws and mandates.The Attorney General, for example, cannot simply call a governor to dictate a certain policy on the art of the state police unless a constitutional violation of some kind has occurred. Otherwise, the Tenth Amendment of the Constitution reserves powers over local matters to the various local law enforcement agencies. Prior to September 1 1, two federal departments were most Involved In law enforcement: the Department of Justice and the Department of the Treasury. The Homeland Security Act of 2002 Homeland Security.Presently, the Department of Homeland Security and the Department of Justices are the two most important government department involved tit law enforcement. However, other federal departments like the Food and Drug Administration have certain law enforcement functions within their mandates. (Frank Assemblages 2007) In closing, law enforcement agencies across the U. S. Involving local, state, or federal form levels of protection to create a safer America. With these agencies at work daily we can actually say we have a form of protection that is not match in any country in the world.

Christianity And Pagansim In Beowulf essays

Christianity And Pagansim In Beowulf essays The epic poem Beowulf, written in the Eighth Century, is predominantly written based on pagan beliefs. It is evident, as the story traveled by word of mouth, many Christian beliefs were added. Christianity, at the time this epic was written, was on a steady incline. Many missionaries were traveling all over England preaching the word and leaving their mark. Beowulf can be analyzed for both its pagan motifs- fate, superhuman behavior, reparation, and many gods- as well as its Christian overtones- Christian characterizations, Adam and Eve, and Resemblances to Jesus. The pagan motifs symbolize and represent the culture of the Anglo-Saxon people. Much like the writings of today, the Anglo-Saxon people tell stories of what they know and believe. Knowing this, one must agree Beowulf is a direct reflection of the Anglo-Saxon society. Fate is a key pagan concept mentioned many times throughout the epic poem. In a pagan society, fate determines all. Living short lives, the pagan people believe in destiny and everything happening for a reason. As one can see, even in battle ...fate decides/ Which of us wins (677-678). This line shows Beowulf believes fate is the higher power, and fate alone will decide the outcome of the battle. This view is very typical of the Anglo-Saxon people because they believe what happens is meant to be. Throughout the poem, Beowulf shows many superhuman or god-like qualities. These god-like qualities or superhuman personifications show that the people of that era believe in powers greater than the ordinary man, suc h as magic. The reader sees many examples of magic or blessed items such as swords and monsters, showing that the pagan people are very sacrificial and superstitious. They believe in order to defeat beasts blessed by evil, one must have weapons blessed by all that is good. This is an understandable concept, showing that the pagans in the po...

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. BHOJRAJAND C. M. C. LEE we rank all the firms each year on the basis of their WEVS(or WPB), and compute the harmonic mean of the actual EVS (or PB) for these firms. ICOMPActual EVS(or PB) ratio for the closest comparable firms within the industry. This variable is the harmonic mean of the actual EVS (or PB) ratio of the four firms within the industrywith the closest warranted multiple. Essentially, this is the COMP variable with the firms constrained to come from the same industry. In short, we compute five different EVS (or PB) measures for each firm based on alternative methods of selecting comparable firms. IEVS and ISEVS(or, IPB and ISPB) correspond to prior studies that control for industry membership and firm size. The other measures incorporate risk, profitability, and growth characteristics beyond industry and size controls. We then examine their relative power in forecasting future EVS and PB ratios. 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