Discussion Of Accounting Policy And Its Implications Of Leases Finance Essay - Free Essay Example

Sample details Pages: 9 Words: 2621 Downloads: 7 Date added: 2017/06/26 Category Finance Essay Type Research paper Did you like this example? ASL capitalized its finance leases at inception at the fair value of the leased asset or lower, at the present value of the minimum lease payments, when there is a transfer of substantially all risks and rewards incidental to ownership of the leased assets to the Group. Capitalized leased assets are depreciated over the shorter of the lease term and the estimated useful life if there is no reasonable certainty that the Group will obtain ownership by the end of the lease term. sj.tay.20092011-03-04T21:56:00 The accounting measures the firm uses (quite long) I was trying to use it to link to the point that depreciation may play a significant impactÃÆ' ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ ¦Thus, the Groups depreciation policy of estimating the useful life would have a significant impact on the reported performance. Don’t waste time! Our writers will create an original "Discussion Of Accounting Policy And Its Implications Of Leases Finance Essay" essay for you Create order A longer estimated useful life delays expense recognition. It overstates income in early term and understates income later in the lease term. Compared to Jaya, ASL seems aggressiveGrace2011-03-05T14:45:00 Why aggressive? in determining the useful lives of leasehold property and buildings. (ASL: 20-30 years; Jaya: remaining 1-18 years) However, ASLs estimates are reasonable as they are the same as some of its competitors like Otto Marine Limited  [1]  and CIMC Raffles Offshore Singapore Limited  [2]  . The financing lease liabilities are stated on ASLs balance sheet under interest-bearing loans and borrowings. Such a classification is fine as it is still an on-balance sheet liability. Also, ASL has included the finance lease liability into its total debt when calculating its gearing ratio. Such an approach is uncommon as people usually only include all borrowings as total debt. Hence, the additional finance lease liability will be increase the total debt which will th en increase gearing ratio of debt-to-equity. A higher gearing ratio, at first sight, may not be desirable as it shows the Group poorer ability to cover its long-term obligations. However, there is tax deductibility on interest (including the finance lease interest) which will help to lower tax expense and increase profit after tax. On the other hand, ASL enters into operational leases on its vessels and commercial property. These leases are off-balance sheet liabilities. As lease interests are included with lease rental, it understates both ASLs operating income and interest expense. Thus, interest coverage ratio is inflated. Not only that, a higher lease expense and lower interest understates operating cash flow and overstates financing cash flow. In addition, operating leases understate liabilities and assets. Thus, solvency ratios and return on investment ratios are improved. If we were to compare to Jaya, the company only has operating leases. This is surprising given that the type of industry it is in, most marine companies would want to lease plant and machinery for a major part of the assets useful life and capitalized the large amount of lease as finance lease. Also, depreciation is usually accounted into the lease agreement which at the end of the finance lease, the lessee can pay no more than a fair market value to gain full ownership of the asset. Thus, Jaya seems to place all leases as operating so as to reduce the likelihood of debt covenant violation and improving financial performance ratios like ROA via financial distortions. (Adjustments)sj.tay.20092011-03-04T21:56:00 I dont know how to comment on the adjustments..If we were to capitalize operating leases back into the companys Grace2011-03-05T14:48:00 Which company?financial statements to get a more proper view of their true debt and related expenses (refer to appendix), there is no change in net income. Non-consolidated associates Jointly-controlled entities Even though proportionate consolidation is the benchmark method, ASL uses the equity method instead to consolidate its proportionate share of HKR-ASL Joint Venture Limited. By using such method, the joint ventures revenues, expenses, assets and liabilities are not consolidated with those of parent. The implication of doing so is that ASL may consciously manage their percentage shareholdings in their joint venture in order to influence how the financial performance is reported in their statement. The use of equity method implies that ASL may try to avoid the consolidation of the investees liabilities by taking the liabilities off balance sheet. However, at closer view, ASL only has one joint venture and it has disclosed the proportional share of the assets and liabilities in the notes just that it used the equity method to consolidate the statement. Not only that, the joint venture has high amount of assets and extraordinary little liabilities ($1,000) in FY2010, thus it is not a serious problem to not consolidate using proportionate method since it is not trying to hide any liabilities. (Adjustments for JV) Instead, if they were to consolidate the high asset to liabilities ratio from the joint venture, the high average total assets they get will lead to a lower return on assets (ROA). Thus, a lower overall profitability ratio would be achieved. Also, the higher assets will lower total asset turnover, hence reflecting badly on the groups efficiency at using its assets in generating revenue. The joint venture only has $1,000 liability and no turnover in FY2010 because it has been dormant since the completion of a ship-chartering project in FY2009. Initially, the joint venture was set up in 2005 to tap on the marine transportation business opportunities in China and the Board viewed it as an extension of the Groups existing core businesses  [3]  . Thus, the establishment of joint venture seems suspicious as it seems to suggest that the Group is usin g the joint venture just to complete on the project. Since they used the equity method, the large expenses and liabilities that may be associated to the building of the project would not need to be consolidated with the Groups financial statement. The group just has to reflect profit/loss from the joint venture. Hence, the cash flow from operating activities will appear to be rosy too. Jaya does not have joint venture. (Note that our group do not mention about associates as the method used by both groups is the same.) Provisions, contingent liabilities and assets Provisions Provision for warranty claims represents the best estimate of the Groups liability to repair vessels and replace affected parts during warranty period and is calculated based on past experience of the level of repairs and returns  [4]  . As the Groups offers general repair and maintenance services for vessels, the provision thus present a fair view of the liabilities the Group has to incur in the future. As the provision provided was based on an estimated figure, there is a possibility that management may underestimate the amount of provision. Thus, this decreases the Groups liabilities, especially during a bad year. Looking to the notes to the financial statement, it was unclear whether the provision was made for a particular job or a few jobs and how long the warranty period is. The provision charged during the past few years were always utilized instead of being reversed. This shows that (1) the Group has been fairly accurate in the estimation for provision (2) its shi prepair service needs improvement. In comparison, the provision for Jaya Holdings is made for the cancellation and deferment of certain committed purchase orders instead of warranty claims. Hence, the different purpose for which provision is provided for could reflect different amount of liabilities of the Group. In addition, the difference in reasons for provision made highlights the financials of the company. ASL did not provide for cancellation of orders as compared to Jaya probably reflects that ASL has better credibility among its shareholders. Therefore, the likelihood of cancellation of orders is low. It can be seen that provision covered under FRS 37 encompasses a wide area of issues. As long as it is more than 50% likelihood that a present obligation by the firm exists, the Group has to make a provision. Still, provision is an estimated amount and could be manipulated by management unless the firm is more forthcoming in its disclosure. Contingent liabilities The Group disclosed two contingent liabilities corporate guarantees (unsecured) and a legal claim for FY 2010. Corporate guarantees were given to the Groups subsidiaries in respect of bank loan agreements obtained. Provision should be made if it is likely that its subsidiaries default on these agreements. Items that are classified as contingent liabilities are not recognized on the balance sheet and therefore have no impact on the Groups financial figures. Therefore, this may be a blind spot to investors as management may choose not to recognise or account for provision despite the likelihood that its subsidiaries may default its payment. However, from FY 2006 to FY 2009, the Group has been maintaining an increasing net profit from long term chartering contracts and ship repair projects. Therefore, the chances of default by the Groups subsidiaries are considered to be low, no adjustment is required. The Group had also reported a legal claim as a contingent liability. In M ay 2009, two of its wholly-owned subsidiaries were involved in an incident, causing damage to an underwater pipeline. As a result, the subsidiaries were served with arbitration proceedings  [5]  by a customer for exposure to a third party claim for the damage caused. The claim could cost approximately USD 1.75 million if the subsidiaries were found liable. However, considering that the case is still in its early stages, it is uncertain how the case would proceed. It is not a matter that is wholly within the firms control as the case mainly depends on the customer to proceed with the claim. Therefore, it is justified for legal claim be classified under contingent liabilities. No adjustment is required. Similarly, Jaya Holdings disclosed the Groups financial guarantees as part of its contingent liabilities. But there is an incentive for management to recognise the guarantees under contingent liabilities instead of the balance sheet because it is not disclosed which company i n the Group the guarantee is for. In the event of a default, provision made would have prepared the management for unforeseen uncertainties. This allows for the flexibility to repay its liabilities and not being cash-strapped later. FRS 16: Property, Plant and Machinery Features: Both companies recognise fixed assets initially at cost if, and only if, the economic benefits flowing out from these assets to the entity are probable and can be measured reliably. They are then depreciated over their useful life on a straight line basis, less any impairment losses. Assets under construction or incomplete are recognised at cost but are not depreciated as they are not ready for use. Once the asset is held for disposal or when no future economic benefits are expected from its use, the assets are derecognized. Residual values and useful lives of the fixed assets are estimated based on the expected consumption pattern of future economic benefits. For ASL, the management estimation of useful lives range from 1 to 30 years. Changes in useful lives and hence, depreciation charges are primarily affected by the changes in expected utilization rate and technological developments. This would give the management more leeway as long as they can reasonable justify for any ch anges made. The useful lives of Jayas fixed asset are estimated to be from 12 to 15 years, which are based on past experience and industry trends. (Risks?) Accounting Flexibility: ASL: Annually, the management has the flexibility to change the depreciation policy, residual values and useful lives when deemed appropriate. Thus the depreciation policy is easily manipulated and can be adjusted to suit the quality of the firms earnings. Jaya: It is implicitly stated in the Notes that the residual values, useful lives and depreciation policy are reviewed annually to ensure consistency throughout the financial period. The management does not have significant control over the depreciation method and useful lives as these values need to be consistent with past estimates and industry trends. Thus, it is less likely to overstate its reported earnings. Accounting Strategy: Average useful life of fixed assets: As shown in tables above, the average useful lives of fixed assets are almost similar except for leasehold properties where ASL adopts a more aggressive approach in estimating its useful life. The straight-line basis adopted is simple as compared to accelerated depreciation as depreciation charges are allocated equally over the assets useful life. This would avoid big baths when using accelerated depreciation method. With reference to the Appendix, weighted average of useful life used is almost half of the weighted average of useful life remaining for ASL in FY 2009 and FY 2010. However, for Jaya, these weighted averages are almost equal to one another. One minor reason is the more aggressive approach taken by ASL. The average useful life of ASLs leasehold property is twice as much as that of Jaya. This causes the depreciation charges claimed over each financial year for ASL to be lower than Jaya. I believe the main reason is due to the compl etion of projects undertaken by ASL and the expansion of it Batam port operations. This causes the cost of ASLs fixed assets, excluding assets under construction, to increase substantially by 19% from FY 2009 to FY 2010. However, it is noted that the cost of Jayas fixed assets, excluding assets under construction, increased by 12%. The additions to Jayas fixed assets are partially set off by impairment losses incurred in FY2010. The total impairment loss recognised in Jayas financial reporting is $11m while ASL did not incur any impairment losses in FY 2010. FRS 23: Tangible Assets (Borrowing Costs) Features: Borrowing costs attributable to the acquisition, construction or production of a qualifying asset are capitalized. They define a qualifying asset that necessarily takes a substantial period of time to get ready for their intended use or sale. Thus, Jaya identified vessels under construction as qualifying assets. Whereas for ASL, the construction of vessels, plant and machinery and the development of yard facilities in China and Batam are assets under construction and hence, are classified under qualifying assets. Capitalization of qualifying assets commences when the activities to prepare assets for its intended use or sale are in progress. Capitalization ceases when assets are ready for their intended use. However the definition of substantial period of time defers for both companies. In ASLs balance sheet and notes, they disclosed interest expenses capitalized as part of the cost of property, plant and equipment, inferring that qualifying assets will take more than a year to b ring the assets to its intended use. But, this definition defers for Jaya where they defined qualifying assets as current assets- stocks and work-in-progress. Furthermore, the amount to be capitalized depends on the interest expense from funds borrowed generally. Both companies incur interest expenses from bank borrowings and finance leases which are then offset by the capitalized amount. We believe that this capitalized amount is identified as attributable to the qualifying assets. Accounting flexibility: ASL: The Group adopts an efficient and optimal interest cost structure to apply a mixture of fixed and variable interest rates on the borrowings. The interest rates applied increased from 3.65% in FY 2009 to 5.13% in FY 2010. Jaya: Adjusts interest rates to foreign currency exposure so as to minimise interest rate risks on borrowings. Only recently, the Group has restructured its bank borrowings into a 5-year USD denominated secured loans with a principal holiday for the first two years and quarterly repayment installments over the subsequent three years. Prior to the Scheme, the average interest rate was 6.05% in FY 2009 but fell to 2.84% in FY 2010. Accounting Strategy: The interest expense to be capitalized as part of the cost of the underlying assets forms an insignificant portion of the cost. Most of the acquisition costs of the qualifying assets are financed internally. In ASL FY 2010, about 3% of the cost of PPE is financed through borrowings whereas for Jaya, 23% of the cost of stocks and work-in-progress is financed via bank borrowings. Thus, ASLs management is more focused on financial risk then Jaya.

Personal Narrative- Television Remote Essay - 681 Words

Personal Narrative- Television Remote Those who are recognized as having authority earn power because of strong leadership skills and the drive to make the world a better place. When people use power to do good deeds they gain respect. A typical leader also holds something in his or her hands, like a staff, that yields power. It is amazing to see people follow an individual who is holding on to something. If they could possess that object then they too could have power. I agree that one must hold something to attain power, but it isn’t a staff that is needed. What a true leader needs is the television remote. Believe it or not, that little battery-operated piece of plastic with the multi-colored buttons can make anyone the supreme†¦show more content†¦A surge of power runs through the ruler as the channel begins to change. The ruler prepares to make an important decision. The subjects hold their breath as images flash by on the screen and those who hoped for football are silently cheering when the ruler releases his or her finger from the remote button and stops on channel nine. The decision is made and football wins. After the ruler makes the decision, the peasants who still wish to watch a movie instead of football begin to plan a revolt. The revolt is not a gender issue. Men and women peasants work together to create a plan to gain control. The ruler can read the peasants’ minds and places a firm grip on the piece of plastic. When the remote is taken from the hands of the one on the throne, all power is lost. The peasants are a little smarter than the ruler is. They look for the ruler’s weakness. They calmly leave the television area and head for the kitchen. The ruler has something they want so they head out in search of something the ruler wants. The peasants return and make the ruler an offering of pop and chips. 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Mayan Collapse Free Essays

The purpose of this investigation is to find the real reason why and how Copal collapsed. Copal was placed where Honduras is today; Copal showed they were great civilization of the cultural Amman life. People wondered what happened to the once great civilization. We will write a custom essay sample on Mayan Collapse or any similar topic only for you Order Now Copal civilization had a rapid fall do to diseases, natural disasters, overpopulation, economic disruption, and many more. All those things that can happen to a civilization bring a collapse. But people around wanted evidence, from information that I read it was said there was no follow period and land was Ewing overused. This was also hipping leading into another fact that the population was getting to large more than what civilization can hold. The evidence that kind of gave you the answer of the telling of overpopulation was the skulls and bones left behind. When the skulls were found there was a question was do these to skulls have different outcomes on what happened to them? Skull #1 showed it had severe anemia which probably killed the Copal citizen. Skull # 1 had spongy looking areas on the back of their skull which are caused by lack of iron in diet. The percentage was 0 percent of skeleton showed they had anemia. Skull # 2 showed the Amman culture way of they had being wrapped during childhood formed into shape that Mayans are use to. The teeth were carved in an intricate pattern, something that Maya upper social classes would do. Skull # 2 was also shown that it had spongy looking areas on the on there had. This Amman noble had anemia also. The evidence has now proved this was not a gradual fall. In the forest there were plants that could have evidence In how and why Copal collapsed. These plants can tell us how it collapsed based on heir life style and how It affected the plants. These plants showed that Copal valley had a heaver return back to the forest. I reached the conclusion that there were people with anemia when they died. A constant of over farming, too big of a population for Its own civilization, and economic disruption. This to me and evidence that there was a rapid decline going on. To prevent a collapse from happening It Is said that every society should have their own needs of rules, food, labor, drinks, and trader. Also your society should have educational purposes, with a passing of certain expectations. To make sure this Is all being followed your society should provide meaning and motivation to Its members. Amman Collapse By Distant-Johnson this was not a gradual fall. In the forest there were plants that could have evidence in their life style and how it affected the plants. These plants showed that Copal valley population for its own civilization, and economic disruption. This to me and evidence that there was a rapid decline going on. To prevent a collapse from happening it is expectations. To make sure this is all being followed your society should provide meaning and motivation to its members. How to cite Mayan Collapse, Papers

Mearns and Thorne Essay Example For Students

Mearns and Thorne Essay I recall feeling optimistic about the potential for our relationship and the clients state of readiness for counselling. She was decisive about improving her level of functioning and showed an admirable willingness to explore her feelings. Session two brought an opportunity for the growth of trust as the client took the risk of sharing feelings she described as pathetic, and received acceptance and understanding. She had arranged for her hairdresser be at her home when she returned from our session. However, that morning she had seen an advertisement for the Yellow Pages and, subsequently, did not want her hair coloured as her feelings of contamination were too high. At a superficial level, the client faced a decision to cancel or keep the appointment, but in terms of her determination to fight her problems, the situation held enormous significance. She worked through the positive and negative consequences of each option and subsequently chose to keep the appointment, despite her concerns about levels of subsequent anxiety. She returned the next week having experienced much less anxiety than expected and I felt this was a distinct victory for her in terms of empowerment and could be seen as a temporary restoration of her trust in the dependability of her organismic self. I remained congruent by offering congratulations on her achievement, but stated that my positive regard was not conditional on her movement towards growth. Following the first two sessions, I was questionned in university group supervision about my feelings for the client. I was privileged that an individual experiencing such self-rejection, had perceived an environment of sufficient safety to disclose personal material, whilst risking my rejection of her. I believed that the clients experiencing of warmth and unconditional regard was the most important ingredient for the fostering of trust between us, and that my communication of positive regard lessened her feelings of alienation; from self and others. Lietaer (1984) uses the term counter-conditioning to describe the process of the counsellors unconditional positive regard breaking down the link between meeting conditions of worth and being valued. In session four, the client had described the level of emotional support she received from her husband, and through reflection, became aware that his patience and warmth felt time-limited and conditional. It became apparent that our relationship was the first time she had been freely given the time and space to feel truly understood, and was consequently able to articulate her feelings to her satisfaction. Through reflection on my feelings, I was aware of a non-possessive warmth and respect, plus an initial desire to protect. My group supervisor questioned how such feelings might impede/facilitate the process, which I needed some time to consider. I feel my ability to communicate warmth and acceptance to be a personal strength, and believe it facilitated the clients feeling of being valued. However, a potential negative implication of feeling protective, would be to hold the client back through the desire to shield from potential failure or painful experience. I would be concerned if my response to the client was of pity, sadness and a desire to rescue her from her immediate experience. The desire to protect has lessened as the clients appearance of anxiety has reduced. In session four, the client was as low as I had ever witnessed her, experiencing nihilism with regard to her family and job. She did not return to any of the material we had focused on in previous sessions, instead talking about the difficulties with her family and job. On reflection, this could be indicative of her need to gain further strength before moving forward. At the end of session three, she had been contemplating showing her husband her affection by hugging him if discussing other concerns she avoided potentially disappointing me with her failure to act on this. She had perhaps realised that she was at a point of no return, but to go on would lead to considerable life changes, and session four may have been a pause to gather energy and coherence for the oncoming difficulties. I am aware that clients who drastically improve their self-acceptance may end up being a new person in an old life (Mearns and Thorne, 1990). .u187048670276e1ee4b74f91d3842fa05 , .u187048670276e1ee4b74f91d3842fa05 .postImageUrl , .u187048670276e1ee4b74f91d3842fa05 .centered-text-area { min-height: 80px; position: relative; } .u187048670276e1ee4b74f91d3842fa05 , .u187048670276e1ee4b74f91d3842fa05:hover , .u187048670276e1ee4b74f91d3842fa05:visited , .u187048670276e1ee4b74f91d3842fa05:active { border:0!important; } .u187048670276e1ee4b74f91d3842fa05 .clearfix:after { content: ""; display: table; clear: both; } .u187048670276e1ee4b74f91d3842fa05 { display: block; transition: background-color 250ms; webkit-transition: background-color 250ms; width: 100%; opacity: 1; transition: opacity 250ms; webkit-transition: opacity 250ms; background-color: #95A5A6; } .u187048670276e1ee4b74f91d3842fa05:active , .u187048670276e1ee4b74f91d3842fa05:hover { opacity: 1; transition: opacity 250ms; webkit-transition: opacity 250ms; background-color: #2C3E50; } .u187048670276e1ee4b74f91d3842fa05 .centered-text-area { width: 100%; position: relative ; } .u187048670276e1ee4b74f91d3842fa05 .ctaText { border-bottom: 0 solid #fff; color: #2980B9; font-size: 16px; font-weight: bold; margin: 0; padding: 0; text-decoration: underline; } .u187048670276e1ee4b74f91d3842fa05 .postTitle { color: #FFFFFF; font-size: 16px; font-weight: 600; margin: 0; padding: 0; width: 100%; } .u187048670276e1ee4b74f91d3842fa05 .ctaButton { background-color: #7F8C8D!important; color: #2980B9; border: none; border-radius: 3px; box-shadow: none; font-size: 14px; font-weight: bold; line-height: 26px; moz-border-radius: 3px; text-align: center; text-decoration: none; text-shadow: none; width: 80px; min-height: 80px; background: url(https://artscolumbia.org/wp-content/plugins/intelly-related-posts/assets/images/simple-arrow.png)no-repeat; position: absolute; right: 0; top: 0; } .u187048670276e1ee4b74f91d3842fa05:hover .ctaButton { background-color: #34495E!important; } .u187048670276e1ee4b74f91d3842fa05 .centered-text { display: table; height: 80px; padding-left : 18px; top: 0; } .u187048670276e1ee4b74f91d3842fa05 .u187048670276e1ee4b74f91d3842fa05-content { display: table-cell; margin: 0; padding: 0; padding-right: 108px; position: relative; vertical-align: middle; width: 100%; } .u187048670276e1ee4b74f91d3842fa05:after { content: ""; display: block; clear: both; } READ: Analysing Gap Model On Burger King Commerce EssayThe clients close relationships may flourish with her personal growth, or they may have relied upon the client being troubled, weak and physically distant. It would be ethical to encourage the client to consider these issues within therapy. I saw myself as a companion through this phase of regression, and I realised the futility of pre-empting sessional content, as the client will bring whatever is important at that time. It also emphasised the clients belief that I am strong enough to cope with her regression, that I would still value her and not attempt to remove her from her desolation.

Book Review Night by Ellie Wiesel

Introduction During the Second World War, a number of scholars and writers came up with various writings to express their opinions, views, and standpoints. The Night, by Ellie Wiesel, is one such book that expresses the views of the writer.Advertising We will write a custom book review sample on Book Review: Night by Ellie Wiesel specifically for you for only $16.05 $11/page Learn More Life was unbearable during the Second World War, particularly in Germany whereby concentration camps existed. Wiesel describes the state of affairs in the Nazi concentration camps at Auschwitz and Buchenwald. Many people lost their lives, including property. Families broke up because family members had to be taken to different places. Others were unable to escape and found themselves in death camps whereby they could provide cheap labor without payment. This piece of writing revisits the works of Wiesel in the book titled Night. The paper summarizes the reasoning of the wr iter and goes a notch higher to analyze some of the themes in order to establish the relevance of the book to the modern political environment. In other words, the paper looks at the strengths and weaknesses of the book. Synopsis The writer explains that life in the concentration camps was unbearable. He wonders where God was when such injustices were mated out to the Jews. He concluded that God might have died because he could have intervened could he be alive. To the writer, life had taken a new twist meaning that the relationship between family members had changed. The writer complained that his father had burdened him since he had to take care of him in everything. This was a great challenge to the writer given that he was only sixteen years.Advertising Looking for book review on american literature? Let's see if we can help you! Get your first paper with 15% OFF Learn More In the concentration camps, family relations had no meaning. This is captured in a statement where the writer complained that if he could only eliminate his father since the old man was a form of a burden to him. However, he regretted using such string words on his father. In the book, the writer shows that life had taken a new twist meaning that moral values were no more. In the concentration camps, there were no fathers, no brothers, and no friends. In 1945, the writer reveals that the US liberated Buchenwald, even though it was late for his father who had already perished in the hands of Nazi. In the introduction, the writer gives a brief description of his life (Wiesel 35) He reports that he was born in a place referred to as Sighet, which is a town situated in a hill in Hungary. Before invasion, laws had been passed aimed at suppressing the Jews. Things got worse when Adolf and his men invaded Hungary. Wiesel was separated from his family as his mother was taken to the gas chamber in Auschwitz and his father and he were taken to Buchenwald. The mother could not surviv e the conditions of Auschwitz and passed on immediately while his father died some few days before liberation. Review/Analysis Moshe the Beadle The narrator tells us the importance of religion in society in this section. He claims that he cried uncontrollably when he noticed that the Temple had been vandalized. Moshe the Beadle was a man in charge of marinating the Synagogue.Advertising We will write a custom book review sample on Book Review: Night by Ellie Wiesel specifically for you for only $16.05 $11/page Learn More In other words, he was a caretaker who ensured that everything went well during prayers. The caretaker is presented as a humble man who never quarreled with any one in society. In 1942, the man of God was whisked to Poland but he managed to come back in order to pass the information to villagers. However, the villagers never minded listening to him. The story of Moshe the Beadle shows that a political enemy always targets the soft sport , which is normally the religious leader. The villagers could not listen to Moshe simply because he was not influential. In the section, the government of Hungary proved that it was part of the Nazi project since it ordered all non-citizen Jews to leave. This is one of the strengths of the book since the Holocaust could not have materialized without the help of other Eastern European governments. The Sighet Ghettos Jews were restricted from participating in important societal activities and enjoying their lives to the fullest. In this regard, Jews were not supposed to own property or to practice their religion. Wherever they moved, Jews were required to wear the Yellow Star, as a form of identification. The Hungarian administration came up with a decision to transfer Jews to one of the Ghettos for easier supervision. The Jews were only restricted to two Ghettos and the rest of their residences were closed. This shows how the Nazi regime was ambitious to control the influence of Jews in other neighboring countries.Advertising Looking for book review on american literature? Let's see if we can help you! Get your first paper with 15% OFF Learn More The Jews could not influence political leaders to come up with fairer laws since their movements were easily monitored in the Ghettos. The writer reports that the Ghettos were self-contained meaning that all social services were provided. No Jew could move out in search of a social amenity. In fact, they were allowed to appoint their councils, referred to as the Jewish Council, which could arbitrate on any issue in the ghettos This was meant to facilitate compliance since the Nazi government could easily approach the council leaders and inform them about the new developments. After sometime, the Ghettos were closed and the Jews were transferred to the concentration camps in Poland and Germany. The writer reports that the Hungarian police had no mercy since each person was mistreated irrespective of his or her societal standing. Auschwitz The writer reports that he was moved to one of the concentration camps referred to as Auschwitz, together with other eighty members of his communit y, including Madame Schachter. Schachter prophesized that the bodies of people were burning but the rest of the Jews could not believe her, just the way Moshe the Beadle had been ignored. People were put in different sections based on gender, age, and health. Unfortunately, the narrator’s mother was send straight to the gas chamber owing to her old age and deteriorating health. The Auschwitz shows that political opponents will never have mercy because they will ensure that only relevant individuals are allowed to live. The weak are eliminated immediately to avoid any costs. Children were eliminated right away since the writer reports that the lorry delivered children into a burning fire while he was watching with the father. The political class and the politicians will never care about morality as long as their interests are well catered for by the existing policy. In the book, the main aim of the political class in Eastern Europe was to acquire wealth. The political class ne ver cared about the value of human life. They would allow the soldiers to strangle innocent children only to frustrate parents. Buchenwald and Liberation In the last section of the book, which talks about the Buchenwald camp and subsequent liberation, the writer does not explain the factors behind liberation. He simply goes ahead to describe how liberation came about but does explain the immediate and long term factors that were responsible for liberation. In Germany, there had been some sort of resistance since some leaders wanted the government to close the concentration camps. In Europe, other world powers such as Britain and Russia had gained momentum and wanted to liberate their citizens who had been kidnapped by the Nazi regime as prisoners of war. The writer does not explain all these factors. Furthermore, he does not give a brief explanation of how the US joined the war. There had been some developments in the international system, which could not allow Germany to continue o ppressing the Jews. In the beginning of the story, the writer explained that the old and those perceived to be unhealthy were eliminated immediately. However, he explains towards the end of the story that his father was ailing from dysentery. The question is how comes the father was allowed to live yet it was against the policy of the Nazi government. This leaves us with some questions to answer. Conclusion The chapters of the book by Wiesel are arranged in a manner that would help the reader to comprehend the real meaning of Holocaust and how the Nazi unleashed terror on the Jews. The book has some strengths including explaining how the Eastern European states contributed in implementing the Nazi policies. The Hungarian government declared that all immigrants would be deported in case they could not provide sufficient documents. This proves that the government of Hungary knew what was just about to happen to the Jews. One weakness of the book is that it does not give authentic info rmation. At one moment, we are told that the old and the sick were never allowed to see the day while at other times the writer tells us that the sick could be left to suffer. Works Cited Wiesel, Elie. Night. New York: Farrar, Straus, and Giroux, 2012. Print. This book review on Book Review: Night by Ellie Wiesel was written and submitted by user Julissa C. to help you with your own studies. You are free to use it for research and reference purposes in order to write your own paper; however, you must cite it accordingly. You can donate your paper here.

Free Essays on Plato And Black Elk

Plato’s â€Å"Allegory of the Cave† and Black Elk’s â€Å"The Great Vision† each make a case for a particular way of knowing through a vision or higher realm. Black Elk describes a far more detailed version of his own vision as a young child. His experience provides a way of knowing the spiritual world. Plato describes a man imprisoned in a cave who finds a way of knowing and understanding the world through an enlightening experience. Both authors share similar ideas of gaining knowledge at a higher level, but smaller details of their writings show the differences in their positions. In â€Å"Allegory of the Cave,† Plato created a metaphoric story beginning at the far end of a cave, a long way from the outside world where men had lived since childhood with their legs and necks tied up in a way that kept them in one place and only allowed them to look straight ahead. Further up the cave, a fire was burning which allowed minimal lighting. There was a wall between the fire and the men, behind which people carried all sorts of artifacts. The men were only able to see shadows of these artifacts. They did not have any knowledge of definite objects. One of the men was untied and dragged into the sunlight. After his eyes adjusted to the light, he feasted his eyes â€Å"on the heavenly bodies and the heavens themselves.† He was told he was now closer to reality and was seeing more accurately. When the man saw the sun, he deducted that it was â€Å"the source of the seasons and the yearly cycle that the whole of the visible realm is its domain.† Plato called the upward journey the mind’s ascent to the intelligible realm. â€Å"In the realm of knowledge is goodness† which is responsible for everything that is right and fine and â€Å"is the source and provider of truth.† Plato deducted that after visiting the higher realm, one would not want â€Å"to engage in human business† in the lower realm because his mind would rather be in the upper regi... Free Essays on Plato And Black Elk Free Essays on Plato And Black Elk Plato’s â€Å"Allegory of the Cave† and Black Elk’s â€Å"The Great Vision† each make a case for a particular way of knowing through a vision or higher realm. Black Elk describes a far more detailed version of his own vision as a young child. His experience provides a way of knowing the spiritual world. Plato describes a man imprisoned in a cave who finds a way of knowing and understanding the world through an enlightening experience. Both authors share similar ideas of gaining knowledge at a higher level, but smaller details of their writings show the differences in their positions. In â€Å"Allegory of the Cave,† Plato created a metaphoric story beginning at the far end of a cave, a long way from the outside world where men had lived since childhood with their legs and necks tied up in a way that kept them in one place and only allowed them to look straight ahead. Further up the cave, a fire was burning which allowed minimal lighting. There was a wall between the fire and the men, behind which people carried all sorts of artifacts. The men were only able to see shadows of these artifacts. They did not have any knowledge of definite objects. One of the men was untied and dragged into the sunlight. After his eyes adjusted to the light, he feasted his eyes â€Å"on the heavenly bodies and the heavens themselves.† He was told he was now closer to reality and was seeing more accurately. When the man saw the sun, he deducted that it was â€Å"the source of the seasons and the yearly cycle that the whole of the visible realm is its domain.† Plato called the upward journey the mind’s ascent to the intelligible realm. â€Å"In the realm of knowledge is goodness† which is responsible for everything that is right and fine and â€Å"is the source and provider of truth.† Plato deducted that after visiting the higher realm, one would not want â€Å"to engage in human business† in the lower realm because his mind would rather be in the upper regi...

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Marketing - Brio Tuscan Grill Assignment Example | Topics and Well Written Essays - 1250 words

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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