Kite Runner Pomegranate Tree Essay

In Khaled Hosseini’s novel, The Kite Runner, the changing depiction of the pomegranate tree symbolizes the changes in Amir and Hassan’s relationship, and is woven into the novel’s central theme of sin and redemption. Throughout the novel Hosseini depicts Amir’s struggle to redeem himself ever since he witnessed the rape of Hassan and stood by as a silent bystander. Amir and Hassan shared a very close friendship doing everything together yet the loyalty between each other was lopsided. Amir could never match Hassan’s unconditional love and loyalty towards him and this sets up the internal struggle in Amir’s mind, because he was sensitive enough to realize the unfairness of the situation. Hosseini uses the pomegranate tree throughout the book as the backdrop for describing key events that influence Amir and Hassan’s relationship. The first depiction of the tree portrays a safe haven but subtle details in the passage point to the events that unfold later. As children, Amir and Hassan spent many hours under the shade of a pomegranate tree up on a hilltop where Amir would read stories to Hassan. Here the pomegranate tree is a symbol of comfort, a place where he and Hassan could be alone sharing the simple pleasure of storytelling. Amir’s description of the â€Å"shadows of pomegranate leaves dancing† on Hassan’s face depicts the protective aspect of the tree, a sanctuary for the two friends (28). The tree and hill are symbolic of Amir and Hassan’s friendship; the tree is rooted in the hill but as the seasons change both the hill and the tree change and so does their friendship. The mention of seasons foreshadows how over time Amir and Hassan’s friendship will be destroyed, in the same way that the rain had turned the â€Å"iron gate rusty† and caused the â€Å"white stone walls to decay† (27). When Amir and Hassan return to the pomegranate tree after the rape, Amir says to Hassan he will read him a new story as they walk up the hill and a sense of hopefulness is conveyed. Amir points out that the â€Å"grass was still green†. Here the green is symbolic of hope and renewal and it connotes Amir’s effort to fix his damaged relationship with Hassan (91). However, when Amir describes that the green grass atop the hill will soon be â€Å"scorched yellow† it also foreshadows Hassan and Ali’s abrupt departure from Kabul, and the devastating impact this has on Amir and Baba (91). Hosseini’s use of the word scorched connotes an event that happens suddenly and is a premonition of worse things to come. Amir is not able to deal with his memories of their happier days under the tree, and instead of storytelling he decides to provoke Hassan to reproach him for his own inaction when the rape occurred. Amir’s ulterior motives – to provoke Hassan and not tell stories – are revealed when he â€Å"picked up an overripe pomegranate† (92) and throws it at Hassan. The overripe, rotting pomegranate is symbolic of a wound that has been left alone too long, the guilt of Amir not helping Hassan when he was raped. The pomegranate fruit itself represents the complexity of their relationship; it is a fruit with a hard skin that is difficult to peel and inside there are beehive-like segments hiding hundreds of red pulpy seeds. Amir is not able to come to terms with his guilt and tries to avoid Hassan at first, but later when he tries to make amends he realizes that for Hassan it will never be the same. The pomegranate also alludes to the forbidden apple from the Bible, symbol of the original sin, and thus it serves to foreshadow the events that are just about to unfold. As Amir hurls pomegranates at Hassan, he repeatedly calls Hassan a coward, but in reality he is letting out his own frustration in the hopes that Hassan will retaliate. He is trying to cover up his guilt for not intervening when Hassan was raped, almost as if Amir is trying to justify that Hassan is the coward and not himself. Once Amir stops pelting the pomegranates he sees Hassan â€Å"smeared in red like he’d been shot by a firing squad† (93). The imagery here represents how deeply Amir’s actions and words had wounded Hassan. Ironically, it also foreshadows the eventual death of Hassan, later in the novel, when he is shot by a Taliban firing squad. When Amir returns to Afghanistan after receiving Rahim Khan’s letter, he finds Kabul under the Taliban regime totally changed. As Amir walks up the old â€Å"craggy hill† from his past he realizes that nothing is the same (264). The craggy hill now represents the destroyed Afghanistan. Amir describes that while walking up the hill every breath felt â€Å"†¦like inhaling fire† (264). This simile illustrates how much pain walking up the hill causes an lder Amir now, although it was something he did almost every day with Hassan when they were carefree children. When he reaches the pomegranate tree, he recalls Hassan’s letter saying â€Å"the pomegranate tree hadn’t borne fruit in years† (264). The barren tree is symbolic of how their friendship was ruined twenty years ago in the winter of 1975. But when Amir locates the faded carving of his and Hassan’s name on the tree, the fact that â€Å"it was still there† makes the pomegranate tree a symbol of hope once again and shows Amir a way to atone for his sin (264). After so many years and so many struggles their friendship was tattered but upon seeing it, Amir finally resolves to redeem himself for the guilt of betraying Hassan; a betrayal that became a heavy burden on his shoulders for twenty long years through his silence and inaction. The changes of the pomegranate tree depict the changes in Amir and Hassan’s relationship. We first see it as the lush shady tree from Amir’s childhood where he and spent countless hours reading stories. Next it appears as the scene where Amir destroys his friendship with Hassan. And finally, it is at the same but now barren pomegranate tree where Amir returns and locates the fading reminder of his long lost friend. Each conflict in Amir and Hassan’s friendship was always on Amir’s part. It was Amir who stayed silent when Hassan was assaulted, it was Amir who tried to provoke Hassan’s reproach by throwing pomegranates at him, but it was also Amir who made the effort at the end to rescue Hassan’s son and his nephew, Sohrab. Like the faded carving, Amir’s friendship with Hassan had faded but never completely disappeared. Amir made the worst mistake of his life but he still had an opportunity for redemption, and that was by rescuing Sohrab from the Taliban and acknowledging him as his own flesh and blood If he didn’t, he knew he would go to his grave with the guilt of the sin he committed in the winter of 1975. While atonement for one’s sin is the central theme of The Kite Runner, the pomegranate tree is one of the main symbols used by the author to show Amir’s journey for atonement and redemption in the book. Hosseini’s repeated use of the pomegranate tree serves as a useful symbol to understand the evolving relationship of Amir and Hassan.

Whether Or Not Armed Security Guards Should Be In Schools?

Whether or not armed security guards should be in schools has been a debate for years but has really been brought into the limelight since the most recent tragedies of Columbine, Virginia Tech and Sandy Hook. The supporters of having armed security in our school districts believe that it is a needed layer of security and helps to diminish response time if necessary while keeping children, parents and teachers feeling safer to be in school. The ones against having guns in the schools believe that we have had armed guards in schools and it did nothing to stop it.They also believe that the cost is too high while also worrying about their children’s psyche from being around loaded weapons every day. No matter which side you are on, there are great arguments for both sides that are logical as well as emotional and it is everyone’s job to listen to all of the information that is available and make their decision based on what they feel is the best choice. That choice could be one of the most important decisions that they ever make because it could mean the difference between life and death.Sandy Hook Elementary School, Columbine High School, Virginia Tech, and the University of Texas at Austin were some of the most televised and the deadliest school shootings that have ever occurred in the United States. Since 1992, there have been over 387 school shootings which are way too many for the United States which poses the question, why did these tragedies happen in the first place? If we had armed security guards or a military/police presence at our institutions of development and higher learning would these tragedies have been prevented or could there have been less loss of life?The answer is that having an armed guard would not have prevented these attacks from individuals whose entire intention was to commit murder. Look at Columbine and Virginia Tech, both of these facilities had armed guards on their campus and it did nothing to stop the attack. The att ackers observed the guards behaviors and learned their routines for patrolling and made their moves when they could get past the guards without detection.This also raises the question is if the school is liable for the deaths of these students since they had trained professionals on site and they did nothing to stop the attack could the school be charged with negligence? So with the evidence of having two of the most brutal school shootings of all time happen while armed security was present, what makes you think that they could stop an incident somewhere else? Deciding whether or not armed security would actually stop an attack is important but so is figuring out how to pay such high costs to have these armed personnel on site to protect our children.Most schools are struggling as it is to keep viable programs such as the arts running while also trying to keep their schools staffed with teachers and other personnel. How could they even fathom paying more money for security when the y cannot even keep an art class going? The average salary for armed security guards across the country is around $55,000 per year then you have to factor in training, equipment, uniforms, and benefits so, on average a school would have to pay $100,000 per year for one security guard for their school.Now some school districts only have a few schools but others have a lot more and they would have a hard time paying for that. Of course, a lot of people would argue that you should not put a price tag on our children’s safety but in many cases the price of their educations would diminish because less money would be spent there to be able to pay for security. Could a school district along with the parents choose to take education away from the students in order to add armed guards in the chance that something goes wrong?Education is a key component in raising a smart, self-sufficient adult. The other factor that becomes a part of cost is having the money to pay for the lawyers and incidentals in the chance that the security guard acted inappropriately. What if there is an accidental shooting or a guard becomes over zealous in what they feel that their responsibilities are? These factors could lead to major lawsuits that the school would be responsible for by either having to represent that officer or in paying settlement and court costs for a lawsuit.These are added costs that the majority of the schools across the United States cannot afford to take on. The safety of schools have been tested more in the recent years with news coverage of the most recent incident in Sandy Hook Elementary School in Newtown, Connecticut, where twenty children and six staff members lost their lives. With the recent rise of school shootings it is no wonder parents are concerned for their children’s safety in schools.Where drugs and peer pressure were once major concerns, in today’s society, we worry about our children dying in the hands of armed shooters. While ther e is much controversy over how to protect the future of America’s children, lawmakers are supporting a bill to allow armed guards to stand and protect our children in the school systems by creating laws to allow armed security in the schools. â€Å"The Indiana amendment's sponsor, Rep. Jim Lucas (R), said he believes mass shootings like the one in Newtown could be prevented by more firearms. † (Resmovits, 2013).In support of armed guards, the National Rifle Association has paid for research and found among the study’s central conclusions is that ‘‘the presence of armed security personnel adds a layer of security and diminishes response time’’ in a shooting, (Asa) Hutchinson said. (Asa Hutchinson) cited a 1997 Mississippi incident in which an assistant principal ran to his truck to retrieve a . 45-caliber semiautomatic pistol and subdued a gunman who had already shot two students (Stolberg). † New legislation would allow armed teac hers, staff members, or on-duty patrols to respond if a situation were to occur.†Ã¢â‚¬Å"The (National Rifle Association's) model legislation would lift restrictions on guns in schools and require specific training for school employees who choose to carry guns. † (Resmovits, 2013). In this time of uncertainty, President Barack Obama, is trying to pass stricter gun laws. â€Å"As President Obama tries to persuade a reluctant Congress to pass new gun laws, the poll found that a majority of Americans -54 percent- think gun control laws should be tightened, up markedly from a CBS News poll last April that found that only 39 percent backed stricter laws.† (Cooper and Sussman, 2013).There is much speculation this increase could be as a result of the Newtown shooting. Meaning people are seeking security, parents are worried for their children, and there is widespread fear of a situation like Newtown ever happening again. Which brings us back to our controversial view, why is having an armed guard important? Having an armed guard might also dissuade any possible shooters from attempting to attack the school, avoiding the situation altogether. The armed guard would be a figure of authority and possible intimidation of any gunmen.As the saying goes ‘you can’t bring a knife to a gun fight’ therefore it would be hasty to think schools can stop armed intruders with anything other than a gun itself. Usually these gunmen are suicidal and seeking attention, where they understand their outcome is death, therefore restraining is not enough. A gunman at this level would not hesitate to shoot first, regardless of who is in their way, which is why having someone on site ready to respond and taken the gunman down is an ideal solution. As a parent there is nothing more important than keeping your kids safe and the same goes for a teacher and their students.Many parents feel that whatever a school needs to do to keep their children safe they are more than willing to agree with. Teachers feel more relaxed that they have someone there to help them in an emergency situation that can help protect the children and children feel safer knowing that they have someone to turn to when their parents are not around to protect them. Some people might argue that having a loaded gun in the school might mess with a child’s mental state because it makes them feel that guns are the only form of protection so they would prefer them not to be there.The reality of the situation is that we live in a world that can be tragic and devastating at times and a lot of kids are forced to see things that are way beyond their years and they are circumstances that no child should have to deal with. But, the great thing about kids is that they are resilient and very smart and with the proper education could be taught about right and wrong and why they need to have armed security guards at school. It is just like explaining to your kids how to dial 91 1 or where in the house the smoke detectors and fire extinguishers are.You also make your kids wear seatbelts and bike helmets to protect them so that they do not get hurt in case of an accident. Parents do these things not to scare their kids but to prepare them in case of emergencies. Children are not equipped to handle an active shooter in school nor should they ever be expected to just like you would not teach a child how to fight a fire but teaching them that this person(s) on campus are there to protect them in that small chance that someone comes into their classroom wishing to do them harm.Parents and teachers would love nothing more than to let their children believe that the world is filled with rainbows and teddy bears but the reality is that it is not and no matter how much people try to protect children from evil you are doing them a disservice by not telling them the truth. It would be morally wrong to lie to children and to make them feel safe when there is a potentia l for danger. Ethically it is the responsibility as parents and teachers to teach children that there may be evil in the world but there is also a lot of good and that there are always people here to protect us.Making a child feel safe and secure while also making the parents and teachers that are responsible for these kids is clearly more important than what it might cost or the possibility that having proper security might possibly fail. There have been too many shooting incidents in the United States but who knows the number of shootings that were prevented because a school chose to have security on campus. Proper knowledge and training is key to making sure that these security guards can do there jobs to protect the innocent.Another option for a school that may not have the financial resources is to hire off-duty police officers to work details on there days off because this would cost a lot less and you are guaranteed to have highly trained and dedicated people willing to put t heir life on the line for your children. As a society why are we more willing to pay for name brand clothes for our kids then add security that can save their lives?During a situation as horrific as an active shooter in a school is a serious issue that unfortunately is our reality today and we have to prepare that it could happen and having armed and trained security personnel on site could mean the matter between life and death and is this something we really want to chance because of money or the possibility that it is for nothing? We do not get into car accidents every day but we always make sure that are children are buckled up so why should adding security measures to schools be any different?

Miscommunication Between Teachers In Terms Of Culture, Language, Case Study

Miscommunication Between Teachers In Terms Of Culture, Language, Generation Or Age Differences - Case Study Example This has led to a developing a wide gap between teachers, most of whom belong to the previous generation, and their students, who embrace the new communication technology thereby creating a divide between the two. This leads to multiple misunderstandings which go as afar as affecting how teachers teach and students learn. The data obtained will be analyzed through a number of ways. It will be statistically analyzed through quantitative as well as qualitative techniques. Besides, primary and secondary documents will also be evaluated through thorough scrutiny and extensive reading to authenticate similarity of facts. Change is inevitable; the changes in communication mechanisms have made the world a global village thereby making communication easier. However, this has had a number of influences with the education sector being affected the

Career management in business Essay Example | Topics and Well Written Essays - 2500 words

Career management in business - Essay Example Please identify ONE strength and ONE weakness only, and provide detailed supporting evidence.† 1.1 Career aspirations Short term career aspirations: -I have working skills in sales, supply chain, finance training and effective customer services. - I have certification in human resource management and I am beginning to build my successful career as professional human resource manager in banking services and activities. - I learn constantly; hence I believe that an organization that offers internal and external opportunities will contribute to effective organizational development. -I like interacting with everyone on professional issues; thus I believe that a better career in HRM will offer me this opportunity to explore my skills. Therefore, I will like to recognize all facets including pension services, recruitment process, employee relations and benefits in an organization. Medium: -I will utilize my knowledge or experience and available resources in order to find the innovati ve or creative solutions to business issues. -I will continue improving and expanding my skills in the expertise field through finding the linkages or relationships among various fields. -I will train and mentor the followers in a manner that can enable them to become productive in the organization. -I will learn and understand the cultural beliefs of other people in order to understand the way business activities and use of technology will be impacted by the cultural beliefs. Understanding organizational culture is essential because it will contribute to successful organizational performance. Long term: -I hope in the 4 years from now, I will achieve managerial position and develop a strong team to lead. Therefore, I believe that I can achieve this through hard work and continuous self development with laying a strong emphasis on improving my professional profile. -I will work with the creative and innovative team in the future in order to develop passionate on the way they handle various task in the organization. This is significant because they will enable me to develop effective skills for achieving my career objectives successfully. Singh (2010, p.34) argues that carrying out research and learning innovative or creative ideas is significant. This is because they contribute towards organizational success. - I will develop an action plan for tracking changes in the workplace environment through learning and talent development; thus creating organizational change. Management action plan is significant because it will enable the organization to create transformation and improve organizational performance in the future. 1.2 Skills required/demanded in your chosen occupation/role -Develop experience in Human resource management, training skills, financial management skills, sales management skills and logistics as well as good leadership skills. Bass, Bass and Bass (2008, p. 56) argues that successful HRM should develop effective earning skills and adopt succes sful leadership theories for management. Employing effective leadership skill is effective because it will enable the HRM to meet the organization objectives effectively; thus creating business change or value. - Developing creative strategic leadership skills is required in the organization. There have been extraordinary heave of concern in business leadership activities in the current banking industries. This is specially the effective skills that

Environmental Science Essay Example | Topics and Well Written Essays - 500 words - 2

Environmental Science - Essay Example Denver has taken many initiatives in the past in an effort to become self-sufficient in terms of energy needs. For example, Greenprint Denver promoted the development of a joint group of businesses, and city departments. This group was called the Neighborhood Energy Action Partnership (NEAP). The objective of NEAP is to make use of the local non-profits so that community outreach can be organized. Providing the residents with energy audits is a potential way to increase their awareness and motivation to save energy. Denver should install subsidized smart meters to lower their cost for the residents and hence, enhance their adoption. The best way for Denver to reduce its carbon footprint as well as the demand for fuel is by introducing the public bike-sharing system, regulation of traffic jams and reduction of carbon emissions being two of the major requirements of sustainable development. The bike system saves on gasoline through its link with buses and trains so that a whole web of substitute transportation is spinned in Denver. This builds resilience into the transport system so that the reliance on one type of transportation is reduced. Presently, over 400 bikes have been located at 50 bike stations in the public areas to ensure maximal usability. Success of the Denver Bike Sharing program can be estimated from the fact that more than 96000 single rides have been recorded along with a procurement of over 1765 yearly memberships (Peterson, Matthews and Weingard 17). A significant population of the residents of Denver acquires the bikes on per-day basis and pays the fee accordingly. In order to increase the popularity of energy-conservation programs among the residents, there needs to be a concerted effort made by Denver. This can be achieved by creating awareness in the masses through demonstration of the usability of such programs on TV, schools and all

Present situation analysis Assignment Example | Topics and Well Written Essays - 2500 words

Present situation analysis - Assignment Example The demand for aged care services is driven by strength of health facilities in a country. It has been evaluated that by the year 2026, demand for aged care services is to go up by 12000 to 20000 citizens (Grant Thorton, 2010). This is triggered by the expected rise in population by 20% between 2006 and 2026 (Grant Thorton, 2010). It has been estimated that population above the age of 65 shall go up to 944000 from 512000, showing a rise of about 84% (Grant Thorton, 2010). Such massive increment in aged population shall require a commensurate rise on the supply side and it is estimated that by 2026, there shall be a requirement of about 78-110% in aged care services in New Zealand (Grant Thorton, 2010). In the present scenario, aged care services generate insufficient revenues to support the projected infrastructure demand. The financial returns have been highly subsidized and there is a huge demand for increasing the existing facilities to build new capacities and replace outdated stock. The paper is aimed at making a comprehensive analysis of the present situation and future scope of the aged care service sector in New Zealand. It makes a service profile analysis, an environmental study and a Budget analysis to present a view of the current situation of aged care division in the country. The future strategies and models of care are devised on the basis of such analysis of dismal situation of older population care in the country. New Zealand is in need of a large scale revolution in the aged care division. The sector was highly unregulated with presence of untrained workforce in the division. The aged population needs a plan for individualised care, instead of the residential care model that had been followed until presently. The demand for residential care has as a result gone down. The aim of aged care in New Zealand is to lower usage of institutionalised care and move forward to expand alternatives in development

Interview Questions, Protocol and Activity Essay

Interview Questions, Protocol and Activity - Essay Example The data analysis will be carried out using three iterative steps (Mills & Sperling, 2012). The first step will going through the notes and summarizing the main points. The second step will be the descriptive step where there will be in depth description of the participants, the activities that took place during the interview and the views of the participants in relation to the research questions. The third and final step will be the organization and categorization stage. In this stage, information or data that tend to show a similarity in terms of content and views will be grouped together for easier synthesis (Mills & Sperling, 2012). Thank you for participating in this research. The research is being done to explore the effects of class size on the teachers’ teaching performance. Confidentiality in this interviewee is guaranteed. †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â ‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ Blatchford, P., Bassett, P., & Brown, P. (2005). Teachers and pupils behavior in large and small classes: A systematic observation study of pupils aged 10 and 11 years. Journal of Educational Psychology, 97 (3), 454-467. Blatchford, P., Russell, A.,

Strategic Planning Essay Example | Topics and Well Written Essays - 1750 words

Strategic Planning - Essay Example The entrepreneur of the organization strongly believes that launching transportation service, incorporating leadership skills, personal development and healthy living training will offer these people a foundation for independent living. To fulfill these objectives the entrepreneur needs a strategic plan and an action plan for proper strategic plan implementation to develop this organization. This paper will make an attempt to form such a strategic plan and its implementation to achieve the objectives. Strategic planning TDL Ventures is an entrepreneurial non-profitable organization specially supporting the visually challenged persons. To develop business, this organization needs a strong strategic intent which will enable the organization to provide clarity about its actions to realize its future aspiration. This strategic intent will provide clarity, focus and inspiration at a time of hindrance to re-energize the staffs of this organization and rebuild the programs to achieve their vision and mission. Thus this organization needs to develop a plan with long-term view and take steps to implement this plan for effective and efficient functioning of the firm. To successfully achieve these long-term objectives TDL Ventures must develop strategic plans, operating plans, business plans and case statements. At first, a strategic plan has to be adopted which will guide the organization to effectively and efficiently fulfill its mission. It should mention the objectives of the organization and goals to be achieved. Then the prioritization of objectives along with mentioning the necessary steps of action and needful resources to achieve them are worked out. These strategic plans should be adopted for a longer time frame i.e. 3-5 years. Secondly, an operational plan needs to be formed to accomplish the goals mentioned precisely in the strategic plan. This plan will mention the time frames of the operations and the roles of individuals associated with it. This plan is gen erally for a short term period i.e. a fiscal year. After all these above mentioned plans are settled, the matter dealing with the performance and investments to generate income from the organizational services and products will get priority. To resolve this issue a business plan has to be formed which will include information about the services and products provided by TDL Ventures. It will also enable it to analyze the external environment and make assumption of the future revenue generation. The last and the most vital thing to be done is the formulation of case statement. It will help this organization in the fundraising and marketing issues. This case statement will mention the goals, strengths, capabilities and the benefits the organization provided to its clients. One of the major issues the case statement addresses is fund mobilization for TDL Ventures. It will enable it to secure funds from the NGOs, foundations, charitable trusts, corporate donations, charitable donors and the philanthropists. (Mittenthal, 2002; Sloner, Shepard, & Podolny, 2008, pp. 27-31) Implementation of the plans: Objectives of the organization TDL Ventures: To be a non-profitable organization providing social support to visually challenged people. To provide support for spiritual, physical and mental awakening of their clients. To develop the healthy and independent lifestyle within their clients. To guide their clients to achieve success in every aspects of life. Tasks and task ownership: In this

The Impact of Technology on Today's Society Research Paper

The Impact of Technology on Today's Society - Research Paper Example The research is based on data gathered from secondary sources. Technology is the application of science to solve a problem. However, there is no definite definition of technology. As technology advances its definition changes. No one definition can describe technology without limiting it. This is because technology evolves each and every day to become more complex and sophisticated. Different types of technology work in different ways for example, we use information technology like the internet for creating and securing data, learning and communication. The scope of this paper is to establish the impact of technology on society.  As the report declares  the goal is to find out the impact of technology on society socially, politically, economically and its influence on health and the environment. The general thing about the different types of technology is that people use them to make life better.  Technology has existed for as long as man. The technology that existed in the pas t may not be as sophisticated and complex as it is today. The introduction of this paper defines technology as the use of science to solve problems and make life better. Computers and the internet may not have existed in the past but science was used to progress man, for example; pyramids of the Egyptians existed in the past.  The technology that exists today may not have existed in the past but there were innovations and inventions then that can also be termed as technology.

Roman, Julio-Claudian ImperialPortrait of Caligula, God, and Ruler Research Paper

Roman, Julio-Claudian ImperialPortrait of Caligula, God, and Ruler - Research Paper Example Primitive in the Stone Age, sculpture developed to bear full resemblance of the object of art expression. Development of crafts, materials and instruments allowed Greek artists to reach perfection in their art works. Roman art was highly influenced and followed the traditions of Etruria and Greece (Pollitt 6) Sculpture was used as a mean to preserve images of the ancestors. Therefore portrait sculpture emerged and developed. Typical material for sculpture was bronze, marble, or terracotta. Monuments and sculptures were also erected to commemorate military or political achievements. Roman Cesar Gaius Germanicus, better known by his pet name Caligula was born in 12 AD. He was confirmed by Senate as an emperor at the age on twenty-five. (Pollitt 102) The people welcomed young emperor. He shown mercy to those in exile, reinstalled his family as imperial. However, soon his relationship with the Senate was strained. The new emperor had poor health and became mentally unstable. His reign wa s full of violence, sadism and perversion. Simple men suffered his cruelty along with the nobility. He was assassinated by his praetorians only four into his rule. Caligula was megalomaniac. From the beginning of his reign tenaciously was destroying statues of great men. According to Suetonius, â€Å"He (Caligula) broke them in pieces to such an extent that it has not been possible to restore them with their inscription intact.†(Pollitt 136) Instead, he proclaimed himself a god. Monuments were erected and imperial portraits created to feature his human image as divine nature. The imperial portrait was usually displayed in the imperial temple. The Emperor was to be worshiped during his lifetime, and Caligula was even worshiped to the excess to the opinion of the Senate. The imperial image established by Augustus was upheld – the face shape designed hairstyle and carefully carved hair locks. The sculptures were powerful tools in political propaganda, along with the coina ge. After Caligula’s death his images were destroyed the same way he was destroying those of others. Not many portraits are preserved. One of them is an imperial Portrait of the Emperor Caligula, God and Ruler which is displayed in the Museum of Fine Arts in Houston. Clearly defined and well-known features also replicated in the coins issued during his reign. It is likely that this bronze head was meant to be a focal point of a temple, however, it is difficult to say for certain. If the purpose and the meaning of Roman sculpture are clearly defined, it is not so with the art works from the ancient times, such as the Woman from Willendorf statuette. A statuette discovered by the archeologist Josef Szombathy in 1908 near Austrian town Willendorf in loess deposit during railroad construction. The excavations on the site had started well over 20 years prior to figurine discovery and many artifacts were found, first by the land owner, then by archeologists. The flint tools, human skeletons, tools made out of animal bones, shells, decorations were found at the site. Many of them are displayed in the Venusium – a museum at Willendorf devoted to the discovery of Woman of Willendorf figurine and other finds from the near-by archeological sites. The Woman of Willendorf statuette or as it more often called, Venus of Willendorf, is displayed at The Vienna Natural History Museum. When the statuette was found, it was dated approximately 15  000 to 10  000 BC. With the technology development and new methods of

Personal Conflict Essay Example for Free

Personal Conflict Essay It was on January 2006 when I first set my feet in USA, a day I will live to remember. I was eighteen years old then and had just graduated from high school. I was the best student in my previous school so I got a scholarship to further my studies. Since childhood ,I was fascinated by people especially their cultures, religions and other aspects of their lives but what really amazed me was the way they communicated and the different languages that they used. My life had revolved around one language that was Arabic and therefore when this chance came I was very excited and without second thoughts took the opportunity to study English as my second language. I chose English because it was an international language and I knew it would help me communicate with many people from different parts of the World. More so, it was to be of great assistance to me especially in my stay in the USA. When this day came, I was amidst mixtures of feelings. Even though I was very excited, terror and confusion took the better part of me. The thought of leaving my parents and my younger siblings really terrified me and all of a sudden, I felt lonely. Earlier on after waking up, prayers had been arranged for me and all people wished me a safe journey to America. I was escorted by my friends and family members to the airport where they bid me farewell. This was my first time to travel from my home country and the first to travel in an airplane. While in the plane many thoughts crossed my mind. I thought about the people I would meet and wondered how they would receive me. I also thought about the college I would be enrolled to and my classmates to be . I wondered if there were people from my home place, how many will they be and above all how I would communicate since I was not familiar with English by then. I was in this state when suddenly an air hostess brought me some snacks . Tasty as they looked, I never took a bite. What I could not understand about the air hostess was that she appeared composed and friendly and completely unaware of my predicament. She was young, energetic and cheerful and nothing seemed to trouble her. Contrary, I was deep in confusion and uncertainty . I lost my appetite something which seldom happens to me. In fact , I rarely choose food and anything edible is good to me provided it is not harmful . More so, being the first time to travel by plane I was really uncomfortable and the experience was horrifying. At one time a thought of the aeroplane crushing crossed my mind and it really got me scared. Sleep never crossed my eyes during the whole journey and it took exactly fourteen hours to reach my destination. I arrived at the JFK airport in New York at around 10 pm . The night was chilly and many people wore heavy clothing to keep their bodies warm . I had carried a light jacket which I wore to protect myself from the cold . The environment was new and everything about this place seemed new to me. I was really getting more and more confused and thought it was even better when I was in the plane. My light jacket did not seem to be of much help to me because the cold weather was getting the better of me. Though out of place, a thought crossed my mind. I wondered of how it used to be unusually hot at home and how I had adapted to that kind of climate. I wondered how long would it take for me to get used to this new environment. I do not know exactly how long I had been standing there but what I recall is that I found myself all alone, confused, scared and lost and attracting some policemen. I felt out of place and the way they were looking at me was scaring. Did they see me as a terrorist or something? I never got to answer that question because one of them came directly to where I was and asked whether I was Mr. Mohammed. Though at first I could not understand what he was driving at, the mention of my name made me realize he was out to find me. Upon receiving my answer, he took my passport and asked me to follow him. They took me to one isolated room where they begun interrogating me. Little did they know that I could not understand whatever they were talking about. From their faces I could tell they were suspicious of something. One police officer who was taller than the rest and had a pointed nose with glaring eyes called the others and got outside of the room. I heard them whispering but could not tell what they were discussing. I do not know exactly what transpired but the moment they came in I sensed danger. I was forced to take off my clothes which apart from being humiliating experience it really made me nervous. They might have thought that I had some atomic bomb with me. What made me extremely terrified was the way they were pointing guns at me. For a moment I thought I was going to die. Here I was, people back at home hoping the best for me but not having a clue of the deadly situation I was in at that particular time. The thought of my mum losing me, made me collapse and when I came back to my senses, I was bed ridded in a hospital. At the hospital I got acquinted to the nurse who was taking care of me . She was a very caring and pleasant lady. She did not like it when I told her (she understood me inspite the language barrier) how the policemen had treated me . She failed to understand how people sometimes could be so inhumane. It was from her that I learnt of how I ended up in an hospital and she also told me that our embassy had been contacted. All this she claimed was through the concerted effort of the management of the hospital . Through her kindness and assurance that all will be well, I was beginning to have hope that things would be better for me. I failed to understand how things could contradict themselves. It was simply not easy to relate the caring and treatment I received from the hospital with the ‘reception’ I received from the policemen. I had been at the hospital overnight and I got discharged at around 10 am in the morning. This happened when the Saudi Arabian embassy sent one of its agents to intervene . All was set right and I could not believe it when one policeman was sent to apologize on the behalf of the others and he even offered us a ride to a hotel. The ride was short but I did not fail to notice how the roads were smooth and carefully constructed. If it were home the journey would have taken a little bit longer because most of them need to be tarmacked and they are dusty. When I arrived at the hotel, I took a bathe, had lunch and then slept after 28 good hours of unrest. I woke up at around 10 pm and realized I was all alone, the agent from the Saudi Arabian embassy had promised to drop by the following morning to pick me up. He had informed me that he would take me to his house and where I would be staying for awhile while we sort matters out. I could not get sleep that night. All my thoughts directed to the kind of life I was to lead in the USA. Foremost, being an Arab and a Muslim I wondered where mosques could be found and whether I would learn to communicate in English. Also I thought about the encounter with the policemen and realized the matter had been made complex due to ineffective communications. All these and other thoughts ran across my mind through out the night. In spite uncertainty facing me, I kept on hoping believing and having faith that everything will turn out right for me and I would enjoy my stay in the USA and eventually make it my home. It has been three years now since I arrived in the USA. A lot of things have changed mostly with me trying to catch up with the American way of life. I have made new friends from different backgrounds and we assist each other in times of troubles. Even though I am different in my culture, religion and way of thinking, I get along with others through their support and understanding.

The Effects of War and Peace on Foreign Aid Essay Example for Free

The Effects of War and Peace on Foreign Aid Essay

CAPM and Three Factor Model in Cost of Equity Measurement

CAPM and Three Factor Model in Cost of Equity Measurement 1.0 INTRODUCTION AND OBJECTIVES Central to many financial decisions such as those relating to investment, capital budgeting, portfolio management and performance evaluation is the estimation of the cost of equity or expected return. There exist several models for the valuation of equity returns, prominent among which are the dividend growth model, residual income model and its extension, free cash flow model, the capital asset pricing model, the Fama and French three factor model, the four factor model etc. Over the past few decades, two of the most common asset pricing models that have been used for this purpose are the Capital Asset Pricing Model (a single factor model by Sharpe 1964, Lintner 1965) and the three factor model suggested by Fama and French (1993). These two models have been very appealing to both practitioners and academicians due to their structural simplicity and are very easy to interpret. There have however been lots of debates and articles as to which of these two models should be used when est imating the cost of equity or expected returns. The question as to which of these two models is better in terms of their ability to explain variation in returns and forecast future returns is still an open one. While most practitioners favour a one factor model (CAPM) when estimating the cost of equity or expected return for a single stock or portfolio, academics however recommend the Fama and French three factor model (see eg. Bruner et al, 1998). The CAPM depicts a linear relationship between the expected return on a stock or portfolio to the excess return on a market portfolio. It characterizes the degree to which an assets return is correlated to the market, and indirectly how risky the asset is, as captured by beta. The three-factor model on the other hand is an extension of the CAPM with the introduction of two additional factors, which takes into account firm size (SMB) and book-to-market equity (HML). The question therefore is why practitioners prefer to use the single factor model (CAPM) when there exist some evidence in academics in favour of the Fama and French three factor model. Considering the number of years most academic concepts are adopted practically, can we conclude that the Fama and French three factor model is experiencing this so-called natural resistance or is it the case that the Fama and French model does not perform significantly better than the CAPM and so therefore not worth the time and cost? The few questions I have posed above form the basis for this study. It is worth noting that while the huge academic studies on these models produce interesting results and new findings, the validity of the underlying models have not been rigorously verified. In this paper, while I aim to ascertain which of the two models better estimates the cost of equity for capital budgeting purposes using regression analysis, I also will like to test whether the data used satisfy the assumptions of the method most academicians adopt, i.e. the Ordinary Least Squares (OLS) method. I will in particular be testing for the existence or otherwise of heteroscedasticity, multicollinearity, normality of errors serial correlation and unit roots, which may result in inefficient coefficient estimates, wrong standard errors, and hence inflated adjusted R2 if present in the data. I will then correct these if they exist by adopting the Generalised Least Squares (GLS) approach instead of the widely used Ordinary Least Squares (OLS) before drawing any inference from the results obtained. My conclusion as to which of the models is superior to the other will be based on which provides the best possible estimate for expected return or cost of equity for capital budgeting decision making. Since the cost of capital for capital budgeting is not observed, the objective here, therefore, is to find the model that is most effective in capturing the variations in stock returns as well as providing the best estimates for future returns. By running a cross sectional regression using stock or portfolio returns as the dependent variable and estimated factor(s) based on past returns as regressors, R2 measures how much of the differences in returns is explained by the estimation procedure. The model that produces the highest adjusted R2 will therefore be deemed the best. The Fama-French (1993, 1996) claimed superiority of their model over CAPM in explaining variations in returns from regressions of 25 portfolios sorted by size and book-to-market value. Their conclusion was based on the fact that their model produced a lower mean absolute value of alpha which is much closer to the theoretical value of zero. Fama and French (2004, working paper) stated that if asset pricing theory holds either in the case of the CAPM (page 10), or the Fama and French three-factor model (page 21), then the value of their alphas should be zero, depicting that the asset pricing model and its factor or factors explain the variations in portfolio returns. Larger values of alpha in this case are not desirable, since this will imply that the model was poor in explaining variation in returns. In line with this postulation, the model that yields the lowest Mean Absolute Value of Alpha (MAVA) will therefore be considered the best. But since alpha is a random variable, I will pro ceed to test the null hypothesis H0: ÃŽ ±i = 0 for all i, by employing the GRS F-statistic postulated by Gibbons, Ross and Shanken (1989). My third and fourth testing measures are based on postulates by econometricians that, the statistical adequacy of a model in terms of its violations of the classical linear regression model assumptions is hugely irrelevant if the models predictive power is poor and that the accuracy of forecasts according to traditional statistical criteria such as the MSE may give little guide to the potential profitability of employing those forecasts in a market trading strategy or for capital budgeting purposes. I will therefore test the predictive power of the two models by observing the percentage of forecast signs predicted correctly and their Mean Square Errors (MSE). One other motivation for this study is also to ascertain whether the results of prior studies are sample specific, that is, whether it is dependent on the period of study or the portfolio grouping used. Theoretically, the effectiveness of an asset pricing model in explaining variation in returns should not be influenced by how the data is grouped. Fama and French (1996) claimed superiority of their model over the CAPM using the July 1963 to December 1993 time period with data groupings based on size and book-to-market equity. I will be replicating this test on the same data grouping but covering a much longer period (from July 1926 to June 2006) and then on a different data grouping based on industry characteristics. Testing the models using the second grouping of industry portfolios will afford me the opportunity to ascertain whether the effectiveness of an asset pricing model is sample specific. I will also carry out the test by employing a much shorter period (5 years) and compari ng it to the longer period and then using the one with the better estimate in terms of alpha and R2 to carry out out-of-sample forecasts. The rest of this paper is structured as follows. Chapter 2 will review the various models available for the estimation of equity cost with particular emphasis on the two asset-pricing models and analysing some existing literature. Chapter 3 will give a description of the data, its source and transformations required, with Chapter 4 describing the methodology. Chapter 5 will involve the time series tests of hypothesis on the data and Chapter 6 will involve an empirical analysis of the results for the tests of the CAPM and the Fama and French three-factor model. Finally, Chapter 7 contains a summary of the major findings of my work and my recommendation as well as some limitations, if any, of the study and recommended areas for further studies. 2.0 RELEVANT LITERATURE The estimation of the cost of equity for an industry involves estimation of what investors expect in return for their investment in that industry. That is, the cost of equity to an industry is equal to the expected return on investors equity holdings in that industry. There are however a host of models available for the estimation of expected returns on an industrys equity capital including but not limited to estimates from fundamentals (dividends and earnings) and those from asset pricing models. 2.1 Estimations from Fundamentals Estimation of expected returns or cost of equity in this case from fundamentals involves the use of dividends and earnings. Fama and French (2002) used this approach to estimate expected stock returns. They stated that, the expected return estimates from fundamentals help to judge whether the realised average return is high or low relative to the expected value (pp 1). The reasoning behind this approach lies in the fact that, the average stock return is the average dividend yield plus the average rate of capital gain: A(Rt) = A(Dt/Pt-1) + A(GPt) (1) where Dt is the dividend for year t, Pt-1 is the price at the end of year t 1, GPt = (Pt Pt-1)/Pt-1 is the rate of capital gain, and A( ) indicates an average value. Given in this situation that the dividend-price ratio, Dt/Pt , is stationary (mean reverting), an alternative estimate of the stock return from fundamentals is: A(RDt) = A(Dt/Pt-1) + A(GDt) (2) Where GDt = (Dt Dt-1)/Dt-1is the growth rate of dividends and (2) is known as the dividend growth model which can be viewed as the expected stock return estimate of the Gordon (1962) model. Equation (2) in theory will only apply to variables that are cointegrated with the stock price and may not hold if the dividend-price ratio is non-stationary, which may be caused by firms decision to return earnings to stockholders by moving away from dividends to share repurchases (Fama and French 2002). But assuming that the ratio of earnings to price, (Yt/Pt), is stationary, then an alternative estimate of the expected rate of capital gain will be the average growth rate of earnings, A(GYt) = A((Yt Yt-1)/Yt-1). In this case, the average dividend yield can be combined with the A(GYt) to produce a third method of estimating expected stock return, the earnings growth model given as: A(RYt) = A(Dt/Pt-1) + A(GYt) (3) It stands to reason from the model in Lettau and Ludvigson (2001) that the average growth rate of consumption can be an alternative mean of estimating the expected rate of capital gain if the ratio of consumption to stock market wealth is assumed stationary. Fama and French (2002) in their analysis concluded that the dividend growth model has an advantage over the earnings growth model and the average stock return if the goal is to estimate the long-term expected growth of wealth. However, it is a more generally known fact that, dividends are a policy variable and so subject to changes in management policy, which raises problems when using the dividend growth model to estimate the expected stock returns. But this may not be a problem in the long run if there is stability in dividend policies and dividend-price ratio resumes its mean-reversion (although the reversion may be at a new mean level). Bagwell and Shoven (1989) and Dunsby (1995) have observed that share repurchases after 1983 has been on the ascendancy, while Fama and French (2001) have also observed that the proportion of firms who do not pay dividends have been increasing steadily since 1978. The Fama and French (2001) observation implies that in transition periods where firms who do not pay dividends increases steadily, the market dividend-price ratio may be non-stationary; overtime, it is likely to decrease, in which case the expected return will likely be underestimated when the dividend growth model is used. The earnings growth model, although not superior to the dividend growth model (Fama and French (2002)), is not affected by possible changes in dividend policies over time. The earnings growth model however may also be affected by non-stationarity in earnings-price ratio since it ability to accurately estimate average expected return is based on the assumption that there are permanent shifts in the expected value of the earnings-price ratio. 2.2 Estimations from Asset-Pricing Models One of the most fundamental concepts in the area of asset-pricing is that of risk versus reward. The pioneering work that addressed the risk and reward trade-off was done by Sharpe (1964)-Lintner (1965), in their introduction of the Capital Asset Pricing Model (CAPM). The Capital Asset Pricing Model postulates that the cross-sectional variation in expected stock or portfolio returns is captured only by the market beta. However, evidence from past literature (Fama and French (1992), Carhart (1997), Strong and Xu (1997), Jagannathan and Wang (1996), Lettau and Ludvigson (2001), and others) stipulates that the cross-section of stock returns is not fully captured by the one factor market beta. Past and present literature including studies by Banz (1981), Rosenberg et al (1985), Basu (1983) and Lakonishok et al (1994) have established that, in addition to the market beta, average returns on stocks are influenced by size, book-to-market equity, earnings/price and past sales growth respecti vely. Past studies have also revealed that stock returns tend to display short-term momentum (Jegadeesh and Titman (1993)) and long-term reversals (DeBondt and Thaler (1985)). Growing research in this area by scholars to address these anomalies has led to the development of alternative models that better explain variations in stock returns. This led to the categorisation of asset pricing models into three: (1) multifactor models that add some factors to the market return, such as the Fama and French three factor model; (2) the arbitrage pricing theory postulated by Ross (1977) and (3) the nonparametric models that heavily criticized the linearity of the CAPM and therefore added moments, as evidenced in the work of Harvey and Siddique (2000) and Dittmar (2002). From this categorization, most of the asset-pricing models can be described as special cases of the four-factor model proposed by Carhart (1997). The four-factor model is given as: E(Ri) Rf = ÃŽ ±i + [ E(RM) Rf ]bi + si E(SMB) + hi E(HML) + wiE(WML) + ÃŽ µi (4) where SMB, HML and WML are proxies for size, book-to-market equity and momentum respectively. There exist other variants of these models such as the three-moment CAPM and the four-moment CAPM (Dittmar, 2002) which add skewness and kurtosis to investor preferences, however the focus of this paper is to compare and test the effectiveness of the CAPM and the Fama and French three-factor model, the two premier asset-pricing models widely acknowledged among both practitioners and academicians. 2.3 Theoretical Background: CAPM and Fama French Three-Factor Model There exist quite a substantial amount of studies in the field of finance relating to these two prominent asset pricing models. The Capital Asset Pricing Model (CAPM) of Sharpe (1964) and Lintner (1965) has been the first most widely recognized theoretical explanation for the estimation of expected stock returns or cost of equity in this case. It is a single factor model that is widely used by Financial Economists and in industry. The CAPM being the first theoretical asset pricing model to address the risk and return concept and due to its simplicity and ease of interpretation, was quickly embraced when it was first introduced. The models attractiveness also lies in the fact that, it addressed difficult problems related to asset pricing using readily available time series data. The CAPM is based on the idea of the relationship that exists between the risk of an asset and the expected return with beta being the sole risk pricing factor. The Sharpe-Lintner CAPM equation which describes individual asset return is given as: E(Ri) = Rf + [ E(RM) Rf ]ÃŽ ²iM i = 1,,N (5) where E(Ri) is the expected return on any asset i, Rf is the risk-free interest rate, E(RM) is the expected return on the value-weighted market portfolio, and ÃŽ ²iM is the assets market beta which measures the sensitivity of the assets return to variations in the market returns and it is equivalent to Cov(Ri, RM)/Var(RM). The equation for the time series regression can be written as: E(Ri) Rf = ÃŽ ±i + [ E(RM) Rf ]ÃŽ ²iM + ÃŽ µi i = 1,,N; (6) showing that the excess return on portfolio i is dependent on excess market return with ÃŽ µi as the error term. The excess market return is also referred to as the market premium. The model is based on several key assumptions, portraying a simplified world where: (1) there are no taxes or transaction costs or problems with indivisibilities of assets; (2) all investors have identical investment horizons; (3) all investors have identical opinions about expected returns, volatilities and correlations of available investments; (4) all assets have limited liability; (5) there exist sufficiently large number of investors with comparable wealth levels so that each investor believes that he/she can purchase and sell any amount of an asset as he or she deems fit in the market; (6) the capital market is in equilibrium; and (7) Trading in assets takes place continually over time. The merits of these assumptions have been discussed extensively in literature. It is evident that most of these assumptions are the standard assumptions of a perfect market which does not exist in reality. It is a known fact that, in reality, indivisibilities and transaction costs do exist and one of the reasons assigned to the assumption of continual trading models is to implicitly give recognition to these costs. It is imperative to note however that, trading intervals are stochastic and of non-constant length and so making it unsatisfactory to assume no trading cost. As mentioned earlier, the assumptions made the model very simple to estimate (given a proxy for the market factor) and interpret, thus making it very attractive and this explains why it was easily embraced. The CAPM stipulates that, investors are only rewarded for the systematic or non-diversifiable risk (represented by beta) they bear in holding a portfolio of assets. Notwithstanding the models simplicity in estimation and interpretation, it has been criticized heavily over the past few decades . Due to its many unrealistic assumptions and simple nature, academicians almost immediately began testing the implications of the CAPM. Studies by Black, Jensen and Scholes (1972) and Fama and MacBeth (1973) gave the first strong empirical support to the use of the model for determining the cost of capital. Black et al. (1972) in combining all the NYSE stocks into portfolio and using data between the periods of 1931 to 1965 found that the data are consistent with the predictions of the Capital Asset Pricing Model (CAPM). Using return data for NYSE stocks for the period between 1926 to 1968, Fama and MacBeth (1973) in examining whether other stock characteristics such as beta squared and idiosyncratic volatility of returns in addition to their betas would help in explaining the cross section of stock returns better found that knowledge of beta was sufficient. There have however been several academic challenges to the validity of the model in relation to its practical application. Banz (1981) revealed the first major challenge to the model when he provided empirical evidence to show that stocks of smaller firms earned better returns than predicted by the CAPM. Banzs finding was not deemed economically important by most academicians in the light that, it is unreasonable to expect an abstract model such as the CAPM to hold exactly and that the proportion of small firms to total market capital is insignificant (under 5%). Other early empirical works by Blume and friend (1973), Basu (1977), Reinganum (1981), Gibbons (1982), Stambaugh (1982) and shanken (1985) could not offer any significant evidence in support of the CAPM. In their paper, Fama and French (2004) noted that in regressing a cross section of average portfolio returns on portfolio beta estimates, the CAPM would predict an intercept which is equal to the risk free rate (Rf) and a beta coefficient equal to the market risk premium (E(Rm) Rf). However, Black, Jensen and Scholes (1972), Blume and Friend (1973), Fama and MacBeth (1973) and Fama and French (1992) after running series of cross-sectional regressions found that the average risk-free rate, which is proxied by the one month T-bill, was always less that the realised intercept. Theory stipulates that, the three main components of the model (the risk free, beta and the market risk premium) must be forward-looking estimates. That is they must be estimates of their true future values. Empirical studies and survey results however show substantial disagreements as to how these components can be estimated. While most empirical researches use the one month T-bill rate as a proxy to the risk-fr ee rate, interviews depicts that practitioners prefer to use either the 90-day T-bill or a 10-year T-bond (normally characterised by a flat yield curve). Survey results have revealed that practitioners have a strong preference for long-term bond yields with over 70% of financial advisors and corporations using Treasury-bond yields with maturities of ten 10 or more years. However, many corporations reveal that they match the tenor of the investment to the term of the risk free rate. Finance theory postulates that the estimated beta should be forward looking, so as to reflect investors uncertainty about future cash flows to equity. Practitioners are forced to use various kinds of proxies since forward-looking betas are unobservable. It is therefore a common practice to use beta estimates derived from historical data which are normally retrieved from Bloomberg, Standard Poors and Value Line. However, the lack of consensus as to which of these three to use results in different betas for the same company. These differences in beta estimates could result in significantly different expected future returns or cost of equity for the company in question thereby yielding conflicting financial decisions especially in capital budgeting. In the work of Bruner et al. (1998), they found significant differences in beta estimates for a small sample of stocks, with Bloomberg providing a figure of 1.03 while Value Line beta was 1.24. The use of historical data however requires th at one makes some practical compromises, each of which can adversely affect the quality of the results. Forinstance, the statistically reliability of the estimate may improve greatly by employing longer time series periods but this may include information that are stale or irrelevant. Empirical research over the years has shown that the precision of the beta estimates using the CAPM is greatly improved when working with well diversified portfolios compared to individual securities. In relation to the equity risk premium, finance theory postulates that, the market premium should be equal to the difference between investors expected returns on the market portfolio and the risk-free rate. Most practitioners have to grapple with the problem of how to measure the market risk premium. Survey results have revealed that the equity market premium prompted the greatest diversity of responses among survey respondents. Since future expected returns are unobservable, most of the survey participants extrapolated historical returns in the future on the assumption that future expectations are heavily influenced by past experience. The survey participants however differed in their estimation of the average historical equity returns as well as their choice of proxy for the riskless asset. Some respondents preferred the geometric average historical equity returns to the arithmetic one while some also prefer the T-bonds to the T-bill as a proxy for the riskless asset. Despite the numerous academic literatures which discuss how the CAPM should be implemented, there is no consensus in relation to the time frame and the data frequency that should be used for estimation. Bartholdy Peare (2005) in their paper concluded that, for estimation of beta, five years of monthly data is the appropriate time period and data frequency. They also found that an equal weighted index, as opposed to the commonly recommended value-weighted index provides a better estimate. Their findings also revealed that it does not really matter whether dividends are included in the index or not or whether raw returns or excess returns are used in the regression equation. The CAPM has over the years been said to have failed greatly in explaining accurate expected returns and this some researchers have attributed to its many unrealistic assumptions. One other major assumption of the CAPM is that there exists complete knowledge of the true market portfolios composition or index to be used. This assumed index is to consist of all the assets in the world. However since only a small fraction of all assets in the world are traded on stock exchanges, it is impossible to construct such an index leading to the use of proxies such as the SP500, resulting in ambiguities in tests. The greatest challenge to the CAPM aside that of Banz (1981) came from Fama and French (1992). Using similar procedures as Fama and MacBeth (1973) and ten size classes and ten beta classes, Fama and French (1992) found that the cross section of average returns on stocks for the periods spanning 1960s to 1990 for US stocks is not fully explained by the CAPM beta and that stock risks are multidimensional. Their regression analysis suggest that company size and book-to-market equity ratio do perform better than beta in capturing cross-sectional variation in the cost of equity capital across firms. Their work was however preceded by Stattman (1980) who was the first to document a positive relation between book-to-market ratios and US stock returns. The findings of Fama and French could however not be dismissed as being economically insignificant as in the case of Banz. Fama and French therefore in 1993 identified a model with three common risk factors in the stock return- an overall market factor, factors related to firm size (SMB) and those related to book-to-market equity (HML), as an alternative to the CAPM. The SMB factor is computed as the average return on three small portfolios (small cap portfolios) less the average return on three big portfolios (large cap portfolios). The HML factor on the other hand is computed as the average return on two value portfolios less the average return on two growth portfolios. The growth portfolio represents stocks with low Book Equity to Market Equity ratio (BE/ME) while the value portfolios represent stocks with high BE/ME ratio. Their three-factor model equation is described as follows: E(Ri) Rf = ÃŽ ±i + [ E(RM) Rf ]bi + si E(SMB) + hi E(HML) + ÃŽ µi (7) Where E(RM) Rf, , E(SMB) and E(HML) are the factor risk premiums and bi , si and hi are the factor sensitivities. It is however believed that the introduction of these two additional factors was motivated by the works of Stattman (1980) and Banz (1981). The effectiveness of these two models in capturing variations in stock returns may be judged by the intercept (alpha) in equations (6) and (7) above. Theory postulates that if these models hold, then the value of the intercept or alpha must equal zero for all assets or portfolio of assets. Fama and French (1997) tested the ability of both the CAPM and their own three-factor model in estimating industry costs of equity. Their test considered 48 industries in which they found that their model outperformed the CAPM across all the industries considered. They however could not conclude that their model was better since their estimates of industry cost of equities were observed to be imprecise. Another disturbing outcome of their study is that both models displayed very large standard errors in the order of 3.0% per annum across all industries. Connor and Senghal (2001) tested the effectiveness of these two models in predicting portfolio returns in indias stock market. They tested the models using 6 portfolio groupings formed from the intersection of two size and three book-to-market equity by examining and testing their intercepts. Connor and Senghal in this paper examined the values of the intercepts and their corresponding t-statistics and then tested the intercepts simultaneously by using the GRS statistic first introduced by Gibbons, Ross and Shanken (1989). Based on the evidence provided by the intercepts and the GRS tests, Connor and Senghal concluded generally that the three-factor model of Fama and French was superior to the CAPM. There have been other several empirical papers ever since, to ascertain which of these models is better in the estimation of expected return or cost of equity, most producing contrasting results. Howard Qi (2004) concluded in his work that on the aggregate level, the two models behave fairly well in their predictive power but the CAPM appeared to be slightly better. Bartholdy and Peare (2002) in their work came to the conclusion that both models performed poorly with the CAPM being the poorest. 3.0 DATA SOURCES T CAPM and Three Factor Model in Cost of Equity Measurement CAPM and Three Factor Model in Cost of Equity Measurement 1.0 INTRODUCTION AND OBJECTIVES Central to many financial decisions such as those relating to investment, capital budgeting, portfolio management and performance evaluation is the estimation of the cost of equity or expected return. There exist several models for the valuation of equity returns, prominent among which are the dividend growth model, residual income model and its extension, free cash flow model, the capital asset pricing model, the Fama and French three factor model, the four factor model etc. Over the past few decades, two of the most common asset pricing models that have been used for this purpose are the Capital Asset Pricing Model (a single factor model by Sharpe 1964, Lintner 1965) and the three factor model suggested by Fama and French (1993). These two models have been very appealing to both practitioners and academicians due to their structural simplicity and are very easy to interpret. There have however been lots of debates and articles as to which of these two models should be used when est imating the cost of equity or expected returns. The question as to which of these two models is better in terms of their ability to explain variation in returns and forecast future returns is still an open one. While most practitioners favour a one factor model (CAPM) when estimating the cost of equity or expected return for a single stock or portfolio, academics however recommend the Fama and French three factor model (see eg. Bruner et al, 1998). The CAPM depicts a linear relationship between the expected return on a stock or portfolio to the excess return on a market portfolio. It characterizes the degree to which an assets return is correlated to the market, and indirectly how risky the asset is, as captured by beta. The three-factor model on the other hand is an extension of the CAPM with the introduction of two additional factors, which takes into account firm size (SMB) and book-to-market equity (HML). The question therefore is why practitioners prefer to use the single factor model (CAPM) when there exist some evidence in academics in favour of the Fama and French three factor model. Considering the number of years most academic concepts are adopted practically, can we conclude that the Fama and French three factor model is experiencing this so-called natural resistance or is it the case that the Fama and French model does not perform significantly better than the CAPM and so therefore not worth the time and cost? The few questions I have posed above form the basis for this study. It is worth noting that while the huge academic studies on these models produce interesting results and new findings, the validity of the underlying models have not been rigorously verified. In this paper, while I aim to ascertain which of the two models better estimates the cost of equity for capital budgeting purposes using regression analysis, I also will like to test whether the data used satisfy the assumptions of the method most academicians adopt, i.e. the Ordinary Least Squares (OLS) method. I will in particular be testing for the existence or otherwise of heteroscedasticity, multicollinearity, normality of errors serial correlation and unit roots, which may result in inefficient coefficient estimates, wrong standard errors, and hence inflated adjusted R2 if present in the data. I will then correct these if they exist by adopting the Generalised Least Squares (GLS) approach instead of the widely used Ordinary Least Squares (OLS) before drawing any inference from the results obtained. My conclusion as to which of the models is superior to the other will be based on which provides the best possible estimate for expected return or cost of equity for capital budgeting decision making. Since the cost of capital for capital budgeting is not observed, the objective here, therefore, is to find the model that is most effective in capturing the variations in stock returns as well as providing the best estimates for future returns. By running a cross sectional regression using stock or portfolio returns as the dependent variable and estimated factor(s) based on past returns as regressors, R2 measures how much of the differences in returns is explained by the estimation procedure. The model that produces the highest adjusted R2 will therefore be deemed the best. The Fama-French (1993, 1996) claimed superiority of their model over CAPM in explaining variations in returns from regressions of 25 portfolios sorted by size and book-to-market value. Their conclusion was based on the fact that their model produced a lower mean absolute value of alpha which is much closer to the theoretical value of zero. Fama and French (2004, working paper) stated that if asset pricing theory holds either in the case of the CAPM (page 10), or the Fama and French three-factor model (page 21), then the value of their alphas should be zero, depicting that the asset pricing model and its factor or factors explain the variations in portfolio returns. Larger values of alpha in this case are not desirable, since this will imply that the model was poor in explaining variation in returns. In line with this postulation, the model that yields the lowest Mean Absolute Value of Alpha (MAVA) will therefore be considered the best. But since alpha is a random variable, I will pro ceed to test the null hypothesis H0: ÃŽ ±i = 0 for all i, by employing the GRS F-statistic postulated by Gibbons, Ross and Shanken (1989). My third and fourth testing measures are based on postulates by econometricians that, the statistical adequacy of a model in terms of its violations of the classical linear regression model assumptions is hugely irrelevant if the models predictive power is poor and that the accuracy of forecasts according to traditional statistical criteria such as the MSE may give little guide to the potential profitability of employing those forecasts in a market trading strategy or for capital budgeting purposes. I will therefore test the predictive power of the two models by observing the percentage of forecast signs predicted correctly and their Mean Square Errors (MSE). One other motivation for this study is also to ascertain whether the results of prior studies are sample specific, that is, whether it is dependent on the period of study or the portfolio grouping used. Theoretically, the effectiveness of an asset pricing model in explaining variation in returns should not be influenced by how the data is grouped. Fama and French (1996) claimed superiority of their model over the CAPM using the July 1963 to December 1993 time period with data groupings based on size and book-to-market equity. I will be replicating this test on the same data grouping but covering a much longer period (from July 1926 to June 2006) and then on a different data grouping based on industry characteristics. Testing the models using the second grouping of industry portfolios will afford me the opportunity to ascertain whether the effectiveness of an asset pricing model is sample specific. I will also carry out the test by employing a much shorter period (5 years) and compari ng it to the longer period and then using the one with the better estimate in terms of alpha and R2 to carry out out-of-sample forecasts. The rest of this paper is structured as follows. Chapter 2 will review the various models available for the estimation of equity cost with particular emphasis on the two asset-pricing models and analysing some existing literature. Chapter 3 will give a description of the data, its source and transformations required, with Chapter 4 describing the methodology. Chapter 5 will involve the time series tests of hypothesis on the data and Chapter 6 will involve an empirical analysis of the results for the tests of the CAPM and the Fama and French three-factor model. Finally, Chapter 7 contains a summary of the major findings of my work and my recommendation as well as some limitations, if any, of the study and recommended areas for further studies. 2.0 RELEVANT LITERATURE The estimation of the cost of equity for an industry involves estimation of what investors expect in return for their investment in that industry. That is, the cost of equity to an industry is equal to the expected return on investors equity holdings in that industry. There are however a host of models available for the estimation of expected returns on an industrys equity capital including but not limited to estimates from fundamentals (dividends and earnings) and those from asset pricing models. 2.1 Estimations from Fundamentals Estimation of expected returns or cost of equity in this case from fundamentals involves the use of dividends and earnings. Fama and French (2002) used this approach to estimate expected stock returns. They stated that, the expected return estimates from fundamentals help to judge whether the realised average return is high or low relative to the expected value (pp 1). The reasoning behind this approach lies in the fact that, the average stock return is the average dividend yield plus the average rate of capital gain: A(Rt) = A(Dt/Pt-1) + A(GPt) (1) where Dt is the dividend for year t, Pt-1 is the price at the end of year t 1, GPt = (Pt Pt-1)/Pt-1 is the rate of capital gain, and A( ) indicates an average value. Given in this situation that the dividend-price ratio, Dt/Pt , is stationary (mean reverting), an alternative estimate of the stock return from fundamentals is: A(RDt) = A(Dt/Pt-1) + A(GDt) (2) Where GDt = (Dt Dt-1)/Dt-1is the growth rate of dividends and (2) is known as the dividend growth model which can be viewed as the expected stock return estimate of the Gordon (1962) model. Equation (2) in theory will only apply to variables that are cointegrated with the stock price and may not hold if the dividend-price ratio is non-stationary, which may be caused by firms decision to return earnings to stockholders by moving away from dividends to share repurchases (Fama and French 2002). But assuming that the ratio of earnings to price, (Yt/Pt), is stationary, then an alternative estimate of the expected rate of capital gain will be the average growth rate of earnings, A(GYt) = A((Yt Yt-1)/Yt-1). In this case, the average dividend yield can be combined with the A(GYt) to produce a third method of estimating expected stock return, the earnings growth model given as: A(RYt) = A(Dt/Pt-1) + A(GYt) (3) It stands to reason from the model in Lettau and Ludvigson (2001) that the average growth rate of consumption can be an alternative mean of estimating the expected rate of capital gain if the ratio of consumption to stock market wealth is assumed stationary. Fama and French (2002) in their analysis concluded that the dividend growth model has an advantage over the earnings growth model and the average stock return if the goal is to estimate the long-term expected growth of wealth. However, it is a more generally known fact that, dividends are a policy variable and so subject to changes in management policy, which raises problems when using the dividend growth model to estimate the expected stock returns. But this may not be a problem in the long run if there is stability in dividend policies and dividend-price ratio resumes its mean-reversion (although the reversion may be at a new mean level). Bagwell and Shoven (1989) and Dunsby (1995) have observed that share repurchases after 1983 has been on the ascendancy, while Fama and French (2001) have also observed that the proportion of firms who do not pay dividends have been increasing steadily since 1978. The Fama and French (2001) observation implies that in transition periods where firms who do not pay dividends increases steadily, the market dividend-price ratio may be non-stationary; overtime, it is likely to decrease, in which case the expected return will likely be underestimated when the dividend growth model is used. The earnings growth model, although not superior to the dividend growth model (Fama and French (2002)), is not affected by possible changes in dividend policies over time. The earnings growth model however may also be affected by non-stationarity in earnings-price ratio since it ability to accurately estimate average expected return is based on the assumption that there are permanent shifts in the expected value of the earnings-price ratio. 2.2 Estimations from Asset-Pricing Models One of the most fundamental concepts in the area of asset-pricing is that of risk versus reward. The pioneering work that addressed the risk and reward trade-off was done by Sharpe (1964)-Lintner (1965), in their introduction of the Capital Asset Pricing Model (CAPM). The Capital Asset Pricing Model postulates that the cross-sectional variation in expected stock or portfolio returns is captured only by the market beta. However, evidence from past literature (Fama and French (1992), Carhart (1997), Strong and Xu (1997), Jagannathan and Wang (1996), Lettau and Ludvigson (2001), and others) stipulates that the cross-section of stock returns is not fully captured by the one factor market beta. Past and present literature including studies by Banz (1981), Rosenberg et al (1985), Basu (1983) and Lakonishok et al (1994) have established that, in addition to the market beta, average returns on stocks are influenced by size, book-to-market equity, earnings/price and past sales growth respecti vely. Past studies have also revealed that stock returns tend to display short-term momentum (Jegadeesh and Titman (1993)) and long-term reversals (DeBondt and Thaler (1985)). Growing research in this area by scholars to address these anomalies has led to the development of alternative models that better explain variations in stock returns. This led to the categorisation of asset pricing models into three: (1) multifactor models that add some factors to the market return, such as the Fama and French three factor model; (2) the arbitrage pricing theory postulated by Ross (1977) and (3) the nonparametric models that heavily criticized the linearity of the CAPM and therefore added moments, as evidenced in the work of Harvey and Siddique (2000) and Dittmar (2002). From this categorization, most of the asset-pricing models can be described as special cases of the four-factor model proposed by Carhart (1997). The four-factor model is given as: E(Ri) Rf = ÃŽ ±i + [ E(RM) Rf ]bi + si E(SMB) + hi E(HML) + wiE(WML) + ÃŽ µi (4) where SMB, HML and WML are proxies for size, book-to-market equity and momentum respectively. There exist other variants of these models such as the three-moment CAPM and the four-moment CAPM (Dittmar, 2002) which add skewness and kurtosis to investor preferences, however the focus of this paper is to compare and test the effectiveness of the CAPM and the Fama and French three-factor model, the two premier asset-pricing models widely acknowledged among both practitioners and academicians. 2.3 Theoretical Background: CAPM and Fama French Three-Factor Model There exist quite a substantial amount of studies in the field of finance relating to these two prominent asset pricing models. The Capital Asset Pricing Model (CAPM) of Sharpe (1964) and Lintner (1965) has been the first most widely recognized theoretical explanation for the estimation of expected stock returns or cost of equity in this case. It is a single factor model that is widely used by Financial Economists and in industry. The CAPM being the first theoretical asset pricing model to address the risk and return concept and due to its simplicity and ease of interpretation, was quickly embraced when it was first introduced. The models attractiveness also lies in the fact that, it addressed difficult problems related to asset pricing using readily available time series data. The CAPM is based on the idea of the relationship that exists between the risk of an asset and the expected return with beta being the sole risk pricing factor. The Sharpe-Lintner CAPM equation which describes individual asset return is given as: E(Ri) = Rf + [ E(RM) Rf ]ÃŽ ²iM i = 1,,N (5) where E(Ri) is the expected return on any asset i, Rf is the risk-free interest rate, E(RM) is the expected return on the value-weighted market portfolio, and ÃŽ ²iM is the assets market beta which measures the sensitivity of the assets return to variations in the market returns and it is equivalent to Cov(Ri, RM)/Var(RM). The equation for the time series regression can be written as: E(Ri) Rf = ÃŽ ±i + [ E(RM) Rf ]ÃŽ ²iM + ÃŽ µi i = 1,,N; (6) showing that the excess return on portfolio i is dependent on excess market return with ÃŽ µi as the error term. The excess market return is also referred to as the market premium. The model is based on several key assumptions, portraying a simplified world where: (1) there are no taxes or transaction costs or problems with indivisibilities of assets; (2) all investors have identical investment horizons; (3) all investors have identical opinions about expected returns, volatilities and correlations of available investments; (4) all assets have limited liability; (5) there exist sufficiently large number of investors with comparable wealth levels so that each investor believes that he/she can purchase and sell any amount of an asset as he or she deems fit in the market; (6) the capital market is in equilibrium; and (7) Trading in assets takes place continually over time. The merits of these assumptions have been discussed extensively in literature. It is evident that most of these assumptions are the standard assumptions of a perfect market which does not exist in reality. It is a known fact that, in reality, indivisibilities and transaction costs do exist and one of the reasons assigned to the assumption of continual trading models is to implicitly give recognition to these costs. It is imperative to note however that, trading intervals are stochastic and of non-constant length and so making it unsatisfactory to assume no trading cost. As mentioned earlier, the assumptions made the model very simple to estimate (given a proxy for the market factor) and interpret, thus making it very attractive and this explains why it was easily embraced. The CAPM stipulates that, investors are only rewarded for the systematic or non-diversifiable risk (represented by beta) they bear in holding a portfolio of assets. Notwithstanding the models simplicity in estimation and interpretation, it has been criticized heavily over the past few decades . Due to its many unrealistic assumptions and simple nature, academicians almost immediately began testing the implications of the CAPM. Studies by Black, Jensen and Scholes (1972) and Fama and MacBeth (1973) gave the first strong empirical support to the use of the model for determining the cost of capital. Black et al. (1972) in combining all the NYSE stocks into portfolio and using data between the periods of 1931 to 1965 found that the data are consistent with the predictions of the Capital Asset Pricing Model (CAPM). Using return data for NYSE stocks for the period between 1926 to 1968, Fama and MacBeth (1973) in examining whether other stock characteristics such as beta squared and idiosyncratic volatility of returns in addition to their betas would help in explaining the cross section of stock returns better found that knowledge of beta was sufficient. There have however been several academic challenges to the validity of the model in relation to its practical application. Banz (1981) revealed the first major challenge to the model when he provided empirical evidence to show that stocks of smaller firms earned better returns than predicted by the CAPM. Banzs finding was not deemed economically important by most academicians in the light that, it is unreasonable to expect an abstract model such as the CAPM to hold exactly and that the proportion of small firms to total market capital is insignificant (under 5%). Other early empirical works by Blume and friend (1973), Basu (1977), Reinganum (1981), Gibbons (1982), Stambaugh (1982) and shanken (1985) could not offer any significant evidence in support of the CAPM. In their paper, Fama and French (2004) noted that in regressing a cross section of average portfolio returns on portfolio beta estimates, the CAPM would predict an intercept which is equal to the risk free rate (Rf) and a beta coefficient equal to the market risk premium (E(Rm) Rf). However, Black, Jensen and Scholes (1972), Blume and Friend (1973), Fama and MacBeth (1973) and Fama and French (1992) after running series of cross-sectional regressions found that the average risk-free rate, which is proxied by the one month T-bill, was always less that the realised intercept. Theory stipulates that, the three main components of the model (the risk free, beta and the market risk premium) must be forward-looking estimates. That is they must be estimates of their true future values. Empirical studies and survey results however show substantial disagreements as to how these components can be estimated. While most empirical researches use the one month T-bill rate as a proxy to the risk-fr ee rate, interviews depicts that practitioners prefer to use either the 90-day T-bill or a 10-year T-bond (normally characterised by a flat yield curve). Survey results have revealed that practitioners have a strong preference for long-term bond yields with over 70% of financial advisors and corporations using Treasury-bond yields with maturities of ten 10 or more years. However, many corporations reveal that they match the tenor of the investment to the term of the risk free rate. Finance theory postulates that the estimated beta should be forward looking, so as to reflect investors uncertainty about future cash flows to equity. Practitioners are forced to use various kinds of proxies since forward-looking betas are unobservable. It is therefore a common practice to use beta estimates derived from historical data which are normally retrieved from Bloomberg, Standard Poors and Value Line. However, the lack of consensus as to which of these three to use results in different betas for the same company. These differences in beta estimates could result in significantly different expected future returns or cost of equity for the company in question thereby yielding conflicting financial decisions especially in capital budgeting. In the work of Bruner et al. (1998), they found significant differences in beta estimates for a small sample of stocks, with Bloomberg providing a figure of 1.03 while Value Line beta was 1.24. The use of historical data however requires th at one makes some practical compromises, each of which can adversely affect the quality of the results. Forinstance, the statistically reliability of the estimate may improve greatly by employing longer time series periods but this may include information that are stale or irrelevant. Empirical research over the years has shown that the precision of the beta estimates using the CAPM is greatly improved when working with well diversified portfolios compared to individual securities. In relation to the equity risk premium, finance theory postulates that, the market premium should be equal to the difference between investors expected returns on the market portfolio and the risk-free rate. Most practitioners have to grapple with the problem of how to measure the market risk premium. Survey results have revealed that the equity market premium prompted the greatest diversity of responses among survey respondents. Since future expected returns are unobservable, most of the survey participants extrapolated historical returns in the future on the assumption that future expectations are heavily influenced by past experience. The survey participants however differed in their estimation of the average historical equity returns as well as their choice of proxy for the riskless asset. Some respondents preferred the geometric average historical equity returns to the arithmetic one while some also prefer the T-bonds to the T-bill as a proxy for the riskless asset. Despite the numerous academic literatures which discuss how the CAPM should be implemented, there is no consensus in relation to the time frame and the data frequency that should be used for estimation. Bartholdy Peare (2005) in their paper concluded that, for estimation of beta, five years of monthly data is the appropriate time period and data frequency. They also found that an equal weighted index, as opposed to the commonly recommended value-weighted index provides a better estimate. Their findings also revealed that it does not really matter whether dividends are included in the index or not or whether raw returns or excess returns are used in the regression equation. The CAPM has over the years been said to have failed greatly in explaining accurate expected returns and this some researchers have attributed to its many unrealistic assumptions. One other major assumption of the CAPM is that there exists complete knowledge of the true market portfolios composition or index to be used. This assumed index is to consist of all the assets in the world. However since only a small fraction of all assets in the world are traded on stock exchanges, it is impossible to construct such an index leading to the use of proxies such as the SP500, resulting in ambiguities in tests. The greatest challenge to the CAPM aside that of Banz (1981) came from Fama and French (1992). Using similar procedures as Fama and MacBeth (1973) and ten size classes and ten beta classes, Fama and French (1992) found that the cross section of average returns on stocks for the periods spanning 1960s to 1990 for US stocks is not fully explained by the CAPM beta and that stock risks are multidimensional. Their regression analysis suggest that company size and book-to-market equity ratio do perform better than beta in capturing cross-sectional variation in the cost of equity capital across firms. Their work was however preceded by Stattman (1980) who was the first to document a positive relation between book-to-market ratios and US stock returns. The findings of Fama and French could however not be dismissed as being economically insignificant as in the case of Banz. Fama and French therefore in 1993 identified a model with three common risk factors in the stock return- an overall market factor, factors related to firm size (SMB) and those related to book-to-market equity (HML), as an alternative to the CAPM. The SMB factor is computed as the average return on three small portfolios (small cap portfolios) less the average return on three big portfolios (large cap portfolios). The HML factor on the other hand is computed as the average return on two value portfolios less the average return on two growth portfolios. The growth portfolio represents stocks with low Book Equity to Market Equity ratio (BE/ME) while the value portfolios represent stocks with high BE/ME ratio. Their three-factor model equation is described as follows: E(Ri) Rf = ÃŽ ±i + [ E(RM) Rf ]bi + si E(SMB) + hi E(HML) + ÃŽ µi (7) Where E(RM) Rf, , E(SMB) and E(HML) are the factor risk premiums and bi , si and hi are the factor sensitivities. It is however believed that the introduction of these two additional factors was motivated by the works of Stattman (1980) and Banz (1981). The effectiveness of these two models in capturing variations in stock returns may be judged by the intercept (alpha) in equations (6) and (7) above. Theory postulates that if these models hold, then the value of the intercept or alpha must equal zero for all assets or portfolio of assets. Fama and French (1997) tested the ability of both the CAPM and their own three-factor model in estimating industry costs of equity. Their test considered 48 industries in which they found that their model outperformed the CAPM across all the industries considered. They however could not conclude that their model was better since their estimates of industry cost of equities were observed to be imprecise. Another disturbing outcome of their study is that both models displayed very large standard errors in the order of 3.0% per annum across all industries. Connor and Senghal (2001) tested the effectiveness of these two models in predicting portfolio returns in indias stock market. They tested the models using 6 portfolio groupings formed from the intersection of two size and three book-to-market equity by examining and testing their intercepts. Connor and Senghal in this paper examined the values of the intercepts and their corresponding t-statistics and then tested the intercepts simultaneously by using the GRS statistic first introduced by Gibbons, Ross and Shanken (1989). Based on the evidence provided by the intercepts and the GRS tests, Connor and Senghal concluded generally that the three-factor model of Fama and French was superior to the CAPM. There have been other several empirical papers ever since, to ascertain which of these models is better in the estimation of expected return or cost of equity, most producing contrasting results. Howard Qi (2004) concluded in his work that on the aggregate level, the two models behave fairly well in their predictive power but the CAPM appeared to be slightly better. Bartholdy and Peare (2002) in their work came to the conclusion that both models performed poorly with the CAPM being the poorest. 3.0 DATA SOURCES T