Second World War Music Propaganda Music Essay

Second World War Music Propaganda Music Essay From Wilhelm Richard Wagner to Irving Berlin, the music of World War II was used on both sides of the conflict to gain support at home and give a feeling of patriotism and boost morale. Interestingly, it was also used as a vehicle to express a vision of government, to attract the enemy troops to propaganda, and encourage the home troops as well. Looking at the music of this time provides insight into the attitudes and cultural tone of the political leaders to all different levels of society Adolf was a fanatical admirer of Wagner since his teens. His passion for Wagner knew no bounds and a performance was almost like a religious experience to the young Adolph. Adolph was carried away by Wagners powerful musical dramas, the evocation of a heroic, sublimely and distant mystical Germanic past. Adolphs first and favorite Wagner opera was Lohengrin, which is the saga of a knight of the grail, the epitome of the Teutonic hero, who was sent from the castle of Monsalvat by his father Parzival to rescue Elsa who had been wrongly condemned, but ended up betraying her. Adolphs philosophy was built upon the works of Wagner which can be seen in his statement These criminals who wanted do away with me have no idea what would happen to the German people, they dont know the plans of our enemies, who want to annihilate Germany so that it never can rise again. If they think that the western powers are strong enough without Germany to hold Bolshevism in check, they are deceiving themselvesà ¢Ã¢â€š ¬Ã‚ ¦ I am the only one who knows the danger, and the only one who can prevent it. The author Ian Kershaw sums up Adolphs statement Such sentiments were redolent, through a distorting mirror, of the Wagnerian redeemer-figure, a hero who alone could save the holders of the Grail, indeed the world itself from disaster a latter-day Parsifal. (Page 851). Adolph thought he could save Germany and the world, but how wrong he was in his thinking that he was an epic hero. As fascinated with Wagner as Adolph was One cannot help but wonder what Richard Wagner would have thought about Adolf Hitler, one of his all-time biggest fans! (Ferguson). Would Wagner consider Adolph and epic hero? One has to think not, but one could be wrong. German Songs Due to Hitlers fascinating with Wagner and especially the Germanic culture that Wagner promoted, the Nazis took a strong interest in promoting the music and culture of their remote ancestors through the use of radio and at the same time promote their propaganda. As with most dictatorial governments the Nazis had an obsession with controlling and promoting the culture of the people and as a result the common peoples taste in music was kept secret, but many Germans were able to use their radios to listen to Jazz which was hated by Hitler but loved by the world. Soldiers in the German army were expected to learn a repertoire of marching songs and traditional songs that they could perform on demand. (Les Cleveland page 8). Which is a type of propaganda. One of the most popular songs of World War Two was Lili Marlene which was popular with both the German and British forces. Based on the German poem Das Mà ¤dchen unter der Laterne which was set to music in 1938. The song was recorded in both German and English versions. German English Vor der Kaserme vor dem großen Tor stand eine Lanterne und steht sie nach davor so wollen wir da uns wieder sehen bei der Lanterne wollen wir stehen wie einst Lili Marlen Unsere beide Schatten sahen wir einer aus daß wir so lieb uns hatten daß gleich man daraus und alle Leute sollen es sehen wie einst Lili Marlen Schon rief der Posten, sie blasen zapfenstreich es kann drei Tage kosten Kamrad, ich komm so gleich da sagten wir auf wiedersehen wie gerne wollt ich mit dir gehen mit dir Lili Marlen Deine Schritte kennt sie, deine Zierengang alle abend brennt sie, doch mich vergaß sie lang und sollten mir ein leids geschehen wer wird bei der Lanterne stehen mit dir Lili Marlen? Aus dem Stillen raume, aus der erder Grund heßt mich wie un Traà ¼me dein verliebster Mund wenn sich die Spaten nebel drehn werdich bei der Lanterne stehen wie einst Lili Marlen Underneath the lantern by the barrack gate, Darling I remember the way you used to wait, Twas there that you whispered tenderly, That you loved me, Youd always be, My Lili of the lamplight, My own Lili Marlene. Time would come for roll call, Time for us to part, Darling Id caress you and press you to my heart, And there neath that far off lantern light, Id hold you tight, Wed kiss good-night, My Lili of the lamplight, My own Lili Marlene. Orders came for sailing somewhere over there, All confined to barracks was more than I could bear; I knew you were waiting in the street, I heard your feet, But could not meet, My Lili of the lamplight, My own Lili Marlene. Resting in a billet just behind the line, Even thowere parted your lips are close to mine; You wait where that lantern softly gleams, Your sweet face seems to haunt my dreams, My Lili of the lamplight, My own Lili Marlene. Due to the popularity of the song it was used throughout the war not only as a popular song, but a propaganda tool. The best understanding of German Music from World War Two has to come from official Nazi government policy. Regrettably as the losers in the war, Nazi songs and German music from this time period has not been assigned the high heroic status as have British and American popular music of this time period. British Songs Although First World War poets [Rupert Brooke, Wilfred Owen, Siegfried Sassoon] are often presented as the literature of wartime, popular songs were important in keeping up morale.   Those from World War II have become especially well known. Some songs were overtly nationalistic, such as ThereHYPERLINK #There%27ll%20Always%20Be%20An%20EnglandHYPERLINK #There%27ll%20Always%20Be%20An%20Englandll Always Be An England. Other music was popular because it evoked emotional states common in wartime, for instance a sense of nostalgic sadness and loss. ( Modern History Sourcebook: Therell Always Be An England and other War Music.) Without question the most popular vocalist of World War Two would be Vera Lynn who sang almost ever well known war time tune in her concerts including Lili Marlene and Therell always be an England but her best know songs were Well meet again and White cliffs of Dover. These songs just give a hint of the differing varieties of wartime songs, other popular music genres included music with lush instrumental compositions as well as just silly songs. American Songs During the war, many people in the US and Great Britain found an escape by listening to the radio. Hit songs were a nice form of catharsis for the public; the lyrics were often about situations the average person could relate to, and it helped the listeners to feel that they were not alone. So, naturally, songwriters wanted to provide music that would be uplifting, encouraging, and of course, patriotic American Songs. A list notable songs of World War Two from America would have to include hits such as Boogie Woogie Bugle Boy which was composed by Don Raye and Hughie Prince and was recorded on January 2, 1941, nearly a year before the United States entered the war The storyline of the song told about, a renowned Illinois street musician is drafted into the United States Army during the draft. In addition to being famous, the bugler was the top man at his craft, but the Army had little use for his talents and he was reduced to blowing Reveille in the morning, which caused the musician to become dejected. Other hits were: Dont Sit Under The Apple Tree (With Anyone Else But Me), Ill Be Home For Christmas, Juke Box Saturday Night, Kiss The Boys Goodbye , Praise The Lord And Pass The Ammunition, and God Bless America which was written by Irving Berlin in 1918 but made famous by Kate Smith in an Armistice Day radio broadcast in 1938. This list could go on with all the notable songs from this time period but space and time will not permit. Conclusion Music goes much deeper, and reaches into our psyches for reasons other than its initial sound. Music evokes emotions of patriotism, fear, jubilation, sadness and many more emotions. As well as invoking emotional responses, music is used as a propaganda tool to get people fired up for a certain cause whether in war or even politics. Was the music of World War Two patriotic or was it propaganda, or was it both? It all depends from which side you view the question. Usually the winning sides music was patriotic and the losers propaganda In closing Susan Burns states from the article War, music, and evolution. No doubt, its dismaying to realize that warfare is so deeply a part of our makeup that well never have the war to end all wars. Those war songs touch a deep, dark chord. Yet, I consider myself better off for having this perspective on warfare.(Burns 2003). What kind of music will be used as patriotic music in the next major war? What music will be used as a propaganda tool? We may have to wait for the answers to these questions, but one thing is certain, as with all past wars, music will have an influence.

Customer Relationship Management and Flight Attendants

Essentials of MIS Additional Cases 1 BUSINESS PROBLEM-SOLVING CASE JetBlue Hits Turbulence In February 2000, JetBlue started flying daily to Fort Lauderdale, Florida and Buffalo, New York, promising top-notch customer service at budget prices. The airline featured new Airbus A320 planes with leather seats, each equipped with a personal TV screen, and average one-way fares of only $99 per passenger. JetBlue was able to provide this relatively luxurious flying experience by using information systems to automate key processes, such as ticket sales (online sales dominate) and baggage handling (electronic tags help track luggage).Jet Blue prided itself on its â€Å"paperless processes. † JetBlue’s investment in information technology enabled the airline to turn a profit by running its business at 70 percent of the cost of larger competitors. At the same time, JetBlue filled a higher percentage of its seats, employed non-union workers, and established enough good will to scor e an impressive customer retention rate of 50 percent. Initially, JetBlue flew only one type of plane from one vendor: the Airbus A320. This approach enabled the airline to standardize flight operations and maintenance procedures to a degree that resulted in considerable savings.CIO Jeff Cohen used the same simple-is-better strategy for JetBlue’s information systems. Cohen depended almost exclusively on Microsoft software products to design JetBlue’s extensive network of information systems. (JetBlue’s reservation system and systems for managing planes, crews, and scheduling are run by an outside contractor. ) Using a single vendor provided a technology framework in which Cohen could keep a small staff and favor in-house development of systems over outsourcing and relying on consultants. The benefit was stable and focused technology spending. JetBlue spent only 1. percent of its revenue on information technology, as opposed to the 5 percent spent by competitors. JetBlue’s technology strategy helped create a pleasing flying experience for passengers. As president and chief operating officer Dave Barger put it, â€Å"Some people say airlines are powered by fuel, but this airline is powered by its IT infrastructure. † JetBlue consistently found itself at the top of J. D. Power and Associates customer satisfaction surveys. JetBlue believed it had learned to work lean and smart. The big question was whether JetBlue would be able to maintain its strategy and its success as the airline grew.By the end of 2006, the company was operating 500 flights daily in 50 cities and had $2. 4 billion in annual revenue. Along the way, JetBlue committed to purchasing a new plane every five weeks through 2007, at a cost of $52 million each. Through all of this, JetBlue remained true to its formula for success and customers continued to return. February 14, 2007, was a wake-up call. A fierce ice storm struck the New York City area that day and set i n motion a string of events that threatened JetBlue’s sterling reputation and its stellar customer relationships.JetBlue made a fateful decision to maintain its schedule in the belief that the horrible weather would break. JetBlue typically avoided pre-canceling flights because passengers usually preferred to have a delayed arrival than to camp out at a terminal or check into a hotel. If the airline had guessed correctly, it would have kept its revenue streams intact and made the customers who were scheduled to fly that day very happy. Most other airlines began canceling flights early in the day, believing it was the prudent decision even though passengers would be inconvenienced and money would be lost.The other airlines were correct. Nine JetBlue planes left their gates at John F. Kennedy International Airport and were stranded on the tarmac for at least six hours. The planes were frozen in place or trapped by iced-over access roads, as was the equipment that would de-ice o r move the aircraft. Passengers were confined inside the planes for up to ten and one-half hours. Supplies of food and water on the planes ran low and toilets in the restrooms began to back up. JetBlue found itself in the middle of a massive dual crisis of customer and public relations.JetBlue waited too long to solicit help for the stranded passengers because the airline figured that the planes would be able to take off eventually. Meanwhile, the weather conditions and the delays or cancellations of other flights caused customers to flood JetBlue’s reservations system, which could not handle the onslaught. At the same time, many of the airline’s pilots and flight crews were also stranded and unable to get to locations where they could pick up the slack for crews that had just worked their maximum hours without rest, but did not actually go anywhere.Moreover, JetBlue did not have a system in place for the rested crews to call in and have their assignments rerouted. The glut of planes and displaced or tired crews forced JetBlue to cancel more flights the next day, a Thursday. And the cancellations continued daily for nearly a week, with the Presidents’ Day holiday week providing few opportunities for rebooking. On the sixth day, JetBlue cancelled 139 of 600 flights involving 11 other airports. 2 76 Part I: Information Systems in Hits Digital Age JetBlue the TurbulenceJetBlue’s eventual recovery was of little solace to passengers who were stranded at the airport for days and missed reservations for family vacations. Overall, more than 1,100 flights were cancelled, and JetBlue lost $30 million. The airline industry is marked by low profit margins and high fixed costs, which means that even short revenue droughts, such as a four-day shutdown, can have devastating consequences for a carrier’s financial stability. Throughout the debacle, JetBlue’s CEO David G. Neeleman was very visible and forthcoming with accountability and apologies.He was quoted many times, saying things such as, â€Å"We love our customers and we’re horrified by this. There’s going to be a lot of apologies. † Neeleman also admitted to the press that JetBlue’s management was not strong enough and its communications system was inadequate. The department responsible for allocating pilots and crews to flights was too small. Some flight attendants were unable to get in touch with anyone who could tell them what to do for three days. With the breakdown in communications, thousands of pilots sand flight attendants were out of position, and the staff could neither find them nor tell them where to go.JetBlue had grown too fast, and its low-cost IT infrastructure and systems could not keep up with the business. JetBlue was accustomed to saving money both from streamlined information systems and lean staffing. Under normal circumstances, the lean staff was sufficient to handle all operations, and the computer syste ms functioned well below their capacity. However, the ice storm exposed the fragility of the infrastructure as tasks such as rebooking passengers, handling baggage, and locating crew members became impossible. Although Neeleman asserted in a conference call hat JetBlue’s computer systems were not to blame for its meltdown, critics of the company pointed out that JetBlue lacked systems to keep track of off-duty flight crews and lost baggage. Its reservation system could not expand enough to meet the high customer call volume. Navitaire, headquartered in Minneapolis, hosts the reservation system for JetBlue as well as for a dozen other discount airlines. The Navitaire system was configured to accomodate up to 650 agents at one time, which was more than sufficient under normal circumstances.During the Valentine’s Day crisis, Navitaire was able to tweak the system to accomodate up to 950 agents simultaneously, but that was still not enough. Moreover, JetBlue could not find enough qualified employees to staff its phones. The company employs about 1,500 reservation agents who work primarily from their homes, linking to its Navitaire Open Skies reservation system using an Internet-based voice communications system. Many ticketholders were unable to determine the status of their flights because the phone lines were jammed.Some callers received a recording that directed them to JetBlue’s Web site. The Web site stopped responding because it could not handle the spike in visitors, leaving many passengers with no way of knowing whether they should make the trip to the airport. JetBlue lacked a computerized system for recording and tracking lost bags. It did have a system for storing information such as the number of bags checked in by a passenger and bag tag identification numbers. But the system could not record which bags had not been picked up or their location.There was no way for a JetBlue agent to use a computer to see if a lost bag for a partic ular passenger was among the heap of unclaimed bags at airports where JetBlue was stranded. In the past, JetBlue management did not feel there was a need for such a system because airport personnel were able to look up passenger records and figure out who owned leftover bags. When so many flights were canceled, the process became unmanageable. JetBlue uses several applications provided by outsourcing vendor Sabre Airline Solutions of Southlake, Texas to manage, schedule, and track planes and crews and to develop actual flight plans.Sabre’s FliteTrac application interfaces with the Navitaire reservation system to provide managers with information about flight status, fuel, passenger lists, and arrival times. Sabre’s CrewTrac application tracks crew assignments and provides pilots and flight attendants access to their schedules via a secure Web portal. JetBlue uses a Navitaire application called SkySolver to determine how to redeploy planes and crews to emerge from fligh t disruptions. However, JetBlue found out during the Valentine’s Day emergency that SkySolver was unable to transfer the information quickly to JetBlue’s Sabre applications.And even if these systems had worked properly together, JetBlue would have probably been unable to locate all of its flight crews to redirect them. It did not have a system to keep track of off-duty crew members. Overtaxed phone lines prevented crew members from calling into headquarters to give their locations and availability for work. JetBlue’s response to its humiliating experience was multifaceted. On the technology front, the airline deployed new software that sends recorded messages to pilots and flight attendants to inquire about their availability.When the employees return the calls, the information they supply is entered into a system that stores the data for access and analysis. From a staffing standpoint, Neeleman promised to train 100 employees from the airline’s corporate office to serve as backups for the departments that were stretched too thin by the effects of the storm. Chapter 2:of MIS AdditionalBusinesses Use Information Systems Essentials E-Business: How Cases 77 3 JetBlue attempted to address its customer relations and image problems by creating a customer bill of rights to enforce standards for customer treatment and airline behavior.JetBlue would be penalized when it failed to provide proper service, and customers who were subjected to poor service would be rewarded. JetBlue set the maximum time for holding passengers on a delayed plane at five hours. The company changed its operational philosophy to make more accomodation for inclement weather. An opportunity to test its changes arrived for JetBlue just one month after the incident that spurred the changes. Faced with another snow and ice storm in the northeast United States on March 16, 2007, JetBlue cancelled 215 flights, or about a third of its total daily slate.By canceling early, ma nagement hoped to ensure that its flight crews would be accessible and available when needed, and that airport gates would be kept clear in case flights that were already airborne had to return. In the wake of its winter struggles, JetBlue was left to hope that its customers would be forgiving and that its losses could be offset. Neeleman pointed out that only about 10,000 of JetBlue’s 30 million annual customers were inconvenienced by the airline’s weather-related breakdowns.On May 10, 2007, JetBlue’s Board of Directors removed Neeleman as CEO, placing him in the role of non-executive chairman. According to Liz Roche, managing partner at Customers Incorporated, a customer relationship management research and consulting firm, â€Å"JetBlue demonstrated that it’s an adolescent in the airline industry and that it has a lot of learning and growing up to do. † Sources: Doug Bartholomew and Mel Duvall, â€Å"What Really Happened at JetBlue,† Base line Magazine, April 1, 2007; â€Å"JetBlue Cancels Hundreds of Flights,† The Associated Press, accessed via www. nytimes. om, March 16, 2007; Susan Carey and Darren Everson, â€Å"Lessons on the Fly: JetBlue’s New Tactics,† The Wall Street Journal, February 27, 2007; Eric Chabrow, â€Å"JetBlue’s Management Meltdown,† CIO Insight, February 20, 2007; Jeff Bailey, â€Å"Chief ‘Mortified’ by JetBlue Crisis,† The New York Times, February 19, 2007 and â€Å"Long Delays Hurt Image of JetBlue,† The New York Times, February 17, 2007; Susan Carey and Paula Prada, â€Å"Course Change: Why JetBlue Shuffled Top Rank,† The Wall Street Journal, May 11, 2007; Coreen Bailor, JetBlue’s Service Flies South,† Customer Relationship Management, May 2007; Thomas Hoffman, â€Å"Out-of-the-Box Airline Carries Over Offbeat Approach to IT,† Computerworld, March 11, 2003; and Stephanie Overby, â€Å"JetBlue Skies Ahead, † CIO Magazine, July 1, 2002. Case Study Questions 1. What types of information systems and business functions are described in this case? 2. What is JetBlue’s business model? How do its information systems support this business model? 3.What was the problem experienced by JetBlue in this case? What people, organization, and technology factors were responsible for the problem? 4. Evaluate JetBlue’s response to the crisis. What solutions did the airline come up with? How were these solutions implemented? Do you think that JetBlue found the correct solutions and implemented them correctly? What other solutions can you think of that JetBlue hasn’t tried? 5. How well is JetBlue prepared for the future? Are the problems described in this case likely to be repeated? Which of JetBlue’s business processes are most vulnerable to breakdowns? How much will a customer bill of rights help?

Commonly Confused Words Nutritional and Nutritious

The adjectives nutritional and nutritious are both related to the noun nutrition (the process of eating the right kinds of food so you can be healthy and grow properly), but their meanings are slightly different. Definitions Nutritional means related to the process of nutrition—that is, using food to support life and maintain health. Nutritious means nourishing or healthy to eat. In the Good Word Guide (2009), Martin Manser notes that the more formal adjective nutritive may be used in place of nutritional or nutritious, but it more frequently replaces the former. Also see the usage notes below. Examples In a growing trend, there is more and more demand that chain restaurants, at least, should be required to give nutritional information in the restaurants, either on the menu or on or near a posted menu. (The A-Z Encyclopedia of Food Controversies and the Law, 2011)Nutritional advice is notoriously nebulous, and food groups regularly alternate between demonisation and deification. Fat makes you fat; fat makes you thin; carbs are basically crack; carbs are back. Corporate agendas are behind much of this confusion. (Arwa Mahdawi, Take It With a Pinch of Salt: The Food Marketing Myths Weve Swallowed Whole.  The Guardian [UK], June 7, 2016)While growing teens need extra calories, they should get them from nutritious sources--not from high-fat, high-calorie, high-sugar foods.Agriculture’s practitioners have often believed their main task to be the production of ever-increasing yields; concerns about the  nutritious  quality of the food have been dismissed as a nuisance that co uld only interfere with quantity. (Roger Thurow, Why the First 1,000 Days Matter Most. The New York Times, June 20, 2016) Usage Notes Nutritional means related to the nutrition process (using food to support life). This chart contains nutritional information for certain menu items.Nutritious means healthy to eat or nourishing. To increase energy, eat nutritious foods like eggs, fruit, or whole-grain breads. (Dave Dowling,  The Wrong Word Dictionary, 2nd ed. Marion Street Press, 2011)[N]utrition is the discipline concerned with desirable foodstuffs and feeding, also desirable feeding itself; nutritional means having to do with nutrition;  nutritious means  having the character associated with desirable nutrition. (Scientific Style and Format: The CBE Manual for Authors, Editors, and Publishers, 6th ed. Cambridge University Press, 2002) Practice Exercises The papaya is a wondrous fruit--abundant, tasty, and _____.Every junk food manufacturer is expending large amounts of money on research to improve the _____ content of their foods. (Andrew F. Smith, Fast Food and Junk Food. Greenwood, 2011) Answers to Practice Exercises The papaya is a wondrous fruit--abundant, tasty, and nutritious.Every junk food manufacturer is expending large amounts of money on research to improve the nutritional content of their foods. (Andrew F. Smith, Fast Food and Junk Food. Greenwood, 2011)

Losing a Loved One in Shoeless Joe Jackson by W.D....

Sometimes the biggest tragedy in someones life is loosing a loved one. The tragedy of this event can be amplified if youre last words are bad or if there is something you forgot to tell them or meant to tell them. There are many books that are write about this theme, for example In the book Shoeless Joe Jackson by W.P. Kinsella, the main character Ray Kinsella is trying desperately to reconnect with his dead father and is willing to put his reputation and financial security at risk for the opportunity to reconnect with his father as well as put his sanity up for question. In the book Shoeless Joe Jackson Mr Kinsella owns a piece of farm land on which he decides to build a baseball field. He does this because he hears a†¦show more content†¦... are you kidnapping me? this quote shows that in the process of doing what the voices tell him to do he travels across the US to pursue a man he has never met before, kidnap him and then take him to a baseball game. He will do all of this because he hopes that it will lead to his father coming to play baseball. This is one of the most questionable things he does and it puts him in serious risk of absolutely ruining his life and reputation by being sent to jail for kidnapping. This shows just how far Ray is willing to go to reconnect with his father. A third example of Ray Putting his reputation on the line is when ray decides to spend a large amount of money on something he does not really need like a tractor even tho he may not be able to pay his bills. this is stated when Ray says to Eddie. page 19 5 Eddie Still Carps About the $4000 I spent on the tractor this quot shows us that he is discussing the 4000 dollars he spent on this tractor with one of his neighbors and his neighbor apologizing for selling it to him when he kinda knew he could not afford making the decision look like a stupid one to all of his neighbors. This shows that he is no way trying to impress anyone which will cause him to lose his respect. So in these quotes you can see how important this is to him and that he is very desperately trying to reconnect with his father and his reputation is less important. Ray Kinsella does many things in the attempt to

Welfare Should Not Be Removed Completely - 1721 Words

Welfare should not be removed completely, for some actually need it, but it should be limited to prevent people from abusing the system and cheating taxpayers out of their money. Welfare is a program designed to help people in need, like the poor or the disabled, who want and diligently try to work, but lack the capability to find for a job that pays enough money for them to support themselves and their families. In that way, welfare is a beneficial program. There are people on the other hand who abuse welfare by being non-thrifty and improperly using the money to buy wants for themselves as opposed to needs for their families. So the question is, how to give a helping hand to those in need, but not allow abusers to cheat people in need†¦show more content†¦Perhaps the two biggest products that welfare abusers buy are drugs (like marijuana, cocaine, etc.) and cigarettes. Everyday addicts take their welfare checks that they were given to with the intention of buying necessities for their families like bread and milk, but instead buy addictive content such as drugs and cigarettes. Another way addicts get their hands on their â€Å"wants† is that the addicts will sell their food stamps for money to obtain these addicting products. â€Å"Approximately 20 percent of Temporary Assistance for Needy Families (TANF) recipients reported having used an illicit drug at least once in the past year.† (Vitter 3). Another way welfare abusers abuse welfare is by improperly investing their money in the wrong places. An example of this is an investment in â€Å"living large equipment† (like cars, big houses, etc.) The money obtained to support the non-working/thrifty man is unfortunately supported by the working man’s tax money. Unfortunately, people who do these perhaps maliciously intended actions have a tenacity to last on welfare for a long time (generations to be specific). If this trend keeps up, then America will be spending 1 million dollars on welfare (which is much more money than is needed to support people who are fairly, valiantly, and legitimately using welfare because those people honestly need the money to support their families and are trying to make a difference. Not all people on welfare are guilty of these foul

Capital Asset Pricing Model and International Research Journal Free Essays

string(33) " model is supported by the data\." International Research Journal of Finance and Economics ISSN 1450-2887 Issue 4 (2006)  © EuroJournals Publishing, Inc. 2006 http://www. eurojournals. We will write a custom essay sample on Capital Asset Pricing Model and International Research Journal or any similar topic only for you Order Now com/finance. htm Testing the Capital Asset Pricing Model (CAPM): The Case of the Emerging Greek Securities Market Grigoris Michailidis University of Macedonia, Economic and Social Sciences Department of Applied Informatics Thessaloniki, Greece E-mail: mgrigori@uom. gr Tel: 00302310891889 Stavros Tsopoglou University of Macedonia, Economic and Social Sciences Department of Applied Informatics Thessaloniki, Greece E-mail: tsopstav@uom. r Tel: 00302310891889 Demetrios Papanastasiou University of Macedonia, Economic and Social Sciences Department of Applied Informatics Thessaloniki, Greece E-mail: papanast@uom. gr Tel: 00302310891878 Eleni Mariola Hagan School of Business, Iona College New Rochelle Abstract The article examines the Capital Asset Pricing Model (CAPM) for the Greek stock market using weekly stock returns from 100 companies listed on the Athens stock exchange for the period of January 1998 to December 2002. In order to diversify away the firm-specific part of returns thereby enhancing the precision of the beta estimates, the securities where grouped into portfolios. The findings of this article are not supportive of the theory’s basic statement that higher risk (beta) is associated with higher levels of return. The model does explain, however, excess returns and thus lends support to the linear structure of the CAPM equation. The CAPM’s prediction for the intercept is that it should equal zero and the slope should equal the excess returns on the market portfolio. The results of the study refute the above hypothesis and offer evidence against the CAPM. The tests conducted to examine the nonlinearity of the relationship between return and betas support the hypothesis that the expected return-beta relationship is linear. Additionally, this paper investigates whether the CAPM adequately captures all-important determinants of returns including the residual International Research Journal of Finance and Economics – Issue 4 (2006) variance of stocks. The results demonstrate that residual risk has no effect on the expected returns of portfolios. Tests may provide evidence against the CAPM but they do not necessarily constitute evidence in support of any alternative model (JEL G11, G12, and G15). Key words: CAPM, Athens Stock Exchange, portfolio returns, beta, risk free rate, stocks JEL Classification: F23, G15 79 I. Introduction Investors and financial researchers have paid considerable attention during the last few years to the new equity markets that have emerged around the world. This new interest has undoubtedly been spurred by the large, and in some cases extraordinary, returns offered by these markets. Practitioners all over the world use a plethora of models in their portfolio selection process and in their attempt to assess the risk exposure to different assets. One of the most important developments in modern capital theory is the capital asset pricing model (CAPM) as developed by Sharpe [1964], Lintner [1965] and Mossin [1966]. CAPM suggests that high expected returns are associated with high levels of risk. Simply stated, CAPM postulates that the expected return on an asset above the risk-free rate is linearly related to the non-diversifiable risk as measured by the asset’s beta. Although the CAPM has been predominant in empirical work over the past 30 years and is the basis of modern portfolio theory, accumulating research has increasingly cast doubt on its ability to explain the actual movements of asset returns. The purpose of this article is to examine thoroughly if the CAPM holds true in the capital market of Greece. Tests are conducted for a period of five years (1998-2002), which is characterized by intense return volatility (covering historically high returns for the Greek Stock market as well as significant decrease in asset returns over the examined period). These market return characteristics make it possible to have an empirical investigation of the pricing model on differing financial conditions thus obtaining conclusions under varying stock return volatility. Existing financial literature on the Athens stock exchange is rather scanty and it is the goal of this study to widen the theoretical analysis of this market by using modern finance theory and to provide useful insights for future analyses of this market. II. Empirical appraisal of the model and competing studies of the model’s validity 2. 1. Empirical appraisal of CAPM Since its introduction in early 1960s, CAPM has been one of the most challenging topics in financial economics. Almost any manager who wants to undertake a project must justify his decision partly based on CAPM. The reason is that the model provides the means for a firm to calculate the return that its investors demand. This model was the first successful attempt to show how to assess the risk of the cash flows of a potential investment project, to estimate the project’s cost of capital and the expected rate of return that investors will demand if they are to invest in the project. The model was developed to explain the differences in the risk premium across assets. According to the theory these differences are due to differences in the riskiness of the returns on the assets. The model states that the correct measure of the riskiness of an asset is its beta and that the risk premium per unit of riskiness is the same across all assets. Given the risk free rate and the beta of an asset, the CAPM predicts the expected risk premium for an asset. The theory itself has been criticized for more than 30 years and has created a great academic debate about its usefulness and validity. In general, the empirical testing of CAPM has two broad purposes (Baily et al, [1998]): (i) to test whether or not the theories should be rejected (ii) to provide information that can aid financial decisions. To accomplish (i) tests are conducted which could potentially at least reject the model. The model passes the test if it is not possible to reject the hypothesis that it is true. Methods of statistical analysis need to be applied in order to draw reliable conclusions on whether the 80 International Research Journal of Finance and Economics – Issue 4 (2006) model is supported by the data. You read "Capital Asset Pricing Model and International Research Journal" in category "Free Research Paper Samples" To accomplish (ii) the empirical work uses the theory as a vehicle for organizing and interpreting the data without seeking ways of rejecting the theory. This kind of approach is found in the area of portfolio decision-making, in particular with regards to the selection of assets to the bought or sold. For example, investors are advised to buy or sell assets that according to CAPM are underpriced or overpriced. In this case empirical analysis is needed to evaluate the assets, assess their riskiness, analyze them, and place them into their respective categories. A second illustration of the latter methodology appears in corporate finance where the estimated beta coefficients are used in assessing the riskiness of different investment projects. It is then possible to calculate â€Å"hurdle rates† that projects must satisfy if they are to be undertaken. This part of the paper focuses on tests of the CAPM since its introduction in the mid 1960’s, and describes the results of competing studies that attempt to evaluate the usefulness of the capital asset pricing model (Jagannathan and McGrattan [1995]). 2. 2. The classic support of the theory The model was developed in the early 1960’s by Sharpe [1964], Lintner [1965] and Mossin [1966]. In its simple form, the CAPM predicts that the expected return on an asset above the risk-free rate is linearly related to the non-diversifiable risk, which is measured by the asset’s beta. One of the earliest empirical studies that found supportive evidence for CAPM is that of Black, Jensen and Scholes [1972]. Using monthly return data and portfolios rather than individual stocks, Black et al tested whether the cross-section of expected returns is linear in beta. By combining securities into portfolios one can diversify away most of the firm-specific component of the returns, thereby enhancing the precision of the beta estimates and the expected rate of return of the portfolio securities. This approach mitigates the statistical problems that arise from measurement errors in beta estimates. The authors found that the data are consistent with the predictions of the CAPM i. e. the relation between the average return and beta is very close to linear and that portfolios with high (low) betas have high (low) average returns. Another classic empirical study that supports the theory is that of Fama and McBeth [1973]; they examined whether there is a positive linear relation between average returns and beta. Moreover, the authors investigated whether the squared value of beta and the volatility of asset returns can explain the residual variation in average returns across assets that are not explained by beta alone. 2. 3. Challenges to the validity of the theory In the early 1980s several studies suggested that there were deviations from the linear CAPM riskreturn trade-off due to other variables that affect this tradeoff. The purpose of the above studies was to find the components that CAPM was missing in explaining the risk-return trade-off and to identify the variables that created those deviations. Banz [1981] tested the CAPM by checking whether the size of firms can explain the residual variation in average returns across assets that remain unexplained by the CAPM’s beta. He challenged the CAPM by demonstrating that firm size does explain the cross sectional-variation in average returns on a particular collection of assets better than beta. The author concluded that the average returns on stocks of small firms (those with low market values of equity) were higher than the average returns on stocks of large firms (those with high market values of equity). This finding has become known as the size effect. The research has been expanded by examining different sets of variables that might affect the riskreturn tradeoff. In particular, the earnings yield (Basu [1977]), leverage, and the ratio of a firm’s book value of equity to its market value (e. g. Stattman [1980], Rosenberg, Reid and Lanstein [1983] and Chan, Hamao, Lakonishok [1991]) have all been utilized in testing the validity of CAPM. International Research Journal of Finance and Economics – Issue 4 (2006) 81 The general reaction to Banz’s [1981] findings, that CAPM may be missing some aspects of reality, was to support the view that although the data may suggest deviations from CAPM, these deviations are not so important as to reject the theory. However, this idea has been challenged by Fama and French [1992]. They showed that Banz’s findings might be economically so important that it raises serious questions about the validity of the CAPM. Fama and French [1992] used the same procedure as Fama and McBeth [1973] but arrived at very different conclusions. Fama and McBeth find a positive relation between return and risk while Fama and French find no relation at all. 2. 4. The academic debate continues The Fama and French [1992] study has itself been criticized. In general the studies responding to the Fama and French challenge by and large take a closer look at the data used in the study. Kothari, Shaken and Sloan [1995] argue that Fama and French’s [1992] findings depend essentially on how the statistical findings are interpreted. Amihudm, Christensen and Mendelson [1992] and Black [1993] support the view that the data are too noisy to invalidate the CAPM. In fact, they show that when a more efficient statistical method is used, the estimated relation between average return and beta is positive and significant. Black [1993] suggests that the size effect noted by Banz [1981] could simply be a sample period effect i. e. the size effect is observed in some periods and not in others. Despite the above criticisms, the general reaction to the Fama and French [1992] findings has been to focus on alternative asset pricing models. Jagannathan and Wang [1993] argue that this may not be necessary. Instead they show that the lack of empirical support for the CAPM may be due to the inappropriateness of basic assumptions made to facilitate the empirical analysis. For example, most empirical tests of the CAPM assume that the return on broad stock market indices is a good proxy for the return on the market portfolio of all assets in the economy. However, these types of market indexes do not capture all assets in the economy such as human capital. Other empirical evidence on stock returns is based on the argument that the volatility of stock returns is constantly changing. When one considers a time-varying return distribution, one must refer to the conditional mean, variance, and covariance that change depending on currently available information. In contrast, the usual estimates of return, variance, and average squared deviations over a sample period, provide an unconditional estimate because they treat variance as constant over time. The most widely used model to estimate the conditional (hence time- varying) variance of stocks and stock index returns is the generalized autoregressive conditional heteroscedacity (GARCH) model pioneered by Robert. F. Engle. To summarize, all the models above aim to improve the empirical testing of CAPM. There have also been numerous modifications to the models and whether the earliest or the subsequent alternative models validate or not the CAPM is yet to be determined. III. Sample selection and Data 3. 1. Sample Selection The study covers the period from January 1998 to December 2002. This time period was chosen because it is characterized by intense return volatility with historically high and low returns for the Greek stock market. The selected sample consists of 100 stocks that are included in the formation of the FTSE/ASE 20, FTSE/ASE Mid 40 and FTSE/ASE Small Cap. These indices are designed to provide real-time measures of the Athens Stock Exchange (ASE). The above indices are formed subject to the following criteria: (i) The FTSE/ASE 20 index is the large cap index, containing the 20 largest blue chip companies listed in the ASE. 82 International Research Journal of Finance and Economics – Issue 4 (2006) ii) The FTSE/ASE Mid 40 index is the mid cap index and captures the performance of the next 40 companies in size. (iii) The FTSE/ASE Small Cap index is the small cap index and captures the performance of the next 80 companies. All securities included in the indices are traded on the ASE on a continuous basis throughout the full Athens stock exchange t rading day, and are chosen according to prespecified liquidity criteria set by the ASE Advisory Committee1. For the purpose of the study, 100 stocks were selected from the pool of securities included in the above-mentioned indices. Each series consists of 260 observations of the weekly closing prices. The selection was made on the basis of the trading volume and excludes stocks that were traded irregularly or had small trading volumes. 3. 2. Data Selection The study uses weekly stock returns from 100 companies listed on the Athens stock exchange for the period of January 1998 to December 2002. The data are obtained from MetaStock (Greek) Data Base. In order to obtain better estimates of the value of the beta coefficient, the study utilizes weekly stock returns. Returns calculated using a longer time period (e. g. onthly) might result in changes of beta over the examined period introducing biases in beta estimates. On the other hand, high frequency data such as daily observations covering a relatively short and stable time span can result in the use of very noisy data and thus yield inefficient estimates. All stock returns used in the study are adjusted for dividends as required by the CAPM. The ASE Composite Sh are index is used as a proxy for the market portfolio. This index is a market value weighted index, is comprised of the 60 most highly capitalized shares of the main market, and reflects general trends of the Greek stock market. Furthermore, the 3-month Greek Treasury Bill is used as the proxy for the risk-free asset. The yields were obtained from the Treasury Bonds and Bill Department of the National Bank of Greece. The yield on the 3-month Treasury bill is specifically chosen as the benchmark that better reflects the short-term changes in the Greek financial markets. IV. Methodology The first step was to estimate a beta coefficient for each stock using weekly returns during the period of January 1998 to December 2002. The beta was estimated by regressing each stock’s weekly return against the market index according to the following equation: Rit – R ft = a i + ? ? ( Rmt – R ft ) + eit (1) where, Rit is the return on stock i (i=1†¦100), R ft is the rate of return on a risk-free asset, Rmt is the rate of return on the market index, ? i is the estimate of beta for the stock i , and eit is the corresponding random disturbance term in the regression equation. [Equation 1 could also be expressed using excess return notation, where ( Rit – R ft ) = rit and ( Rmt – Rft ) = rmt ] In spite of the fact that weekly returns were used to avoid short-term noise effects the estimation diagnostic tests for equation (1) indicated, in several occasions, departures from the linear assumption. www. ase. gr International Research Journal of Finance and Economics – Issue 4 (2006) 83 In such cases, equation (1) was re-estimated providing for EGARCH (1,1) form to comfort with misspecification. The next step was to compute average portfolio excess returns of stocks ( rpt ) ordered according to their beta coefficient computed by Equation 1. Let, rpt = ?r i =1 k it k (2) where, k is the number of stocks included in each portfolio (k=1†¦10), p is the number of portfolios (p=1†¦10), rit is the excess return on stocks that form each portfolio comprised of k stocks each. This procedure generated 10 equally-weighted portfolios comprised of 10 stocks each. By forming portfolios the spread in betas across portfolios is maximized so that the effect of beta on return can be clearly examined. The most obvious way to form portfolios is to rank stocks into portfolios by the true beta. But, all that is available is observed beta. Ranking into portfolios by observed beta would introduce selection bias. Stocks with high-observed beta (in the highest group) would be more likely to have a positive measurement error in estimating beta. This would introduce a positive bias into beta for high-beta portfolios and would introduce a negative bias into an estimate of the intercept. (Elton and Gruber [1995], p. 333). Combining securities into portfolios diversifies away most of the firm-specific part of returns thereby enhancing the precision of the estimates of beta and the expected rate of return on the portfolios on securities. This mitigates statistical problems that arise from measurement error in the beta estimates. The following equation was used to estimate portfolio betas: rpt = a p + ? p ? mt + e pt (3) where, rpt is the average excess portfolio return, ? p is the calculated portfolio beta. The study continues by estimating the ex-post Security Market Line (SML) by regressing the portfolio returns against the portfolio betas obtained by Equation 3. The relation examined is the following: rP = ? 0 + ? 1 ? ? P + e P (4) where, rp is the average excess return on a portfolio p (the difference between the return on t he portfolio and the return on a risk-free asset), ? p is an estimate of beta of the portfolio p , ? 1 is the market price of risk, the risk premium for bearing one unit of beta risk, ? is the zero-beta rate, the expected return on an asset which has a beta of zero, and e p is random disturbance term in the regression equation. In order to test for nonlinearity between total portfolio returns and betas, a regression was run on average portfolio returns, calculated portfolio beta, and beta-square from equation 3: 2 rp = ? 0 + ? 1 ? ? p + ? 2 ? ? p + e p (5) Finally in order to examine whether the residual variance of stocks affects portfolio returns, an additional term was included in equation 5, to test for the explanatory power of nonsystematic risk: 2 rp = ? + ? 1 ? ? p + ? 2 ? ? p + ? 3 ? RVp + e p (6) where 84 International Research Journal of Finance and Economics – Issue 4 (2006) RV p is the residual variance of portfolio returns (Equation 3), RV p = ? 2 (e pt ) . The estimated parameters allow us to test a series of hypotheses regarding the CAPM. The tests are: i) ? 3 = 0 or residual risk does not affect return, ii) ? 2 = 0 or there are no nonlinearities in the security market line, iii) ? 1 gt; 0 that is, there is a positive price of risk in the capital markets (Elton and Gruber [1995], p. 336). Finally, the above analysis was also conducted for each year separately (1998-2002), by changing the portfolio compositions according to yearly estimated betas. V. Empirical results and Interpretation of the findings The first part of the methodology required the estimation of betas for individual stocks by using observations on rates of return for a sequence of dates. Useful remarks can be derived from the results of this procedure, for the assets used in this study. The range of the estimated stock betas is between 0. 0984 the minimum and 1. 4369 the maximum with a standard deviation of 0. 240 (Table 1). Most of the beta coefficients for individual stocks are statistically significant at a 95% level and all estimated beta coefficients are statistical significant at a 90% level. For a more accurate estimation of betas an EGARCH (1,1) model was used wherever it was necessary, in order to correct for nonlinearities. Table 1: Stock beta coefficient estimates (Equation 1) Stock name beta Stock name beta Stock name OLYMP . 0984 THEMEL . 8302 PROOD EYKL . 4192 AIOLK . 8303 ALEK MPELA . 4238 AEGEK . 8305 EPATT MPTSK . 5526 AEEXA . 8339 SIDEN FOIN . 5643 SPYR . 8344 GEK GKOYT . 862 SARANT . 8400 ELYF PAPAK . 6318 ELTEX . 8422 MOYZK ABK . 6323 ELEXA . 8427 TITK MYTIL . 6526 MPENK . 8610 NIKAS FELXO . 6578 HRAKL . 8668 ETHENEX ABAX . 6874 PEIR . 8698 IATR TSIP . 6950 BIOXK . 8747 METK AAAK . 7047 ELMEK . 8830 ALPHA EEEK . 7097 LAMPSA . 8848 AKTOR ERMHS . 7291 MHXK . 8856 INTKA LAMDA . 7297 DK . 8904 MAIK OTE . 7309 FOLI . 9005 PETZ MARF . 7423 THELET . 9088 ETEM MRFKO . 7423 ATT . 9278 FINTO KORA . 7520 ARBA . 9302 ESXA RILK . 7682 KATS . 9333 BIOSK LYK . 7684 ALBIO . 9387 XATZK ELASK . 7808 XAKOR . 9502 KREKA NOTOS . 8126 SAR . 9533 ETE KARD . 8290 NAYP . 577 SANYO Source: Metastock (Greek) Data Base and calculations (S-PLUS) beta . 9594 . 9606 . 9698 . 9806 . 9845 . 9890 . 9895 . 9917 . 9920 1. 0059 1. 0086 1. 0149 1. 0317 1. 0467 1. 0532 1. 0542 1. 0593 1. 0616 1. 0625 1. 0654 1. 0690 1. 0790 1. 0911 1. 1127 1. 1185 Stock name EMP NAOYK ELBE ROKKA SELMK DESIN ELBAL ESK TERNA KERK POYL EEGA KALSK GENAK FANKO PLATH STRIK EBZ ALLK GEBKA AXON RINTE KLONK ETMAK ALTEK beta 1. 1201 1. 1216 1. 1256 1. 1310 1. 1312 1. 1318 1. 1348 1. 1359 1. 1392 1. 1396 1. 1432 1. 1628 1. 1925 1. 1996 1. 2322 1. 2331 1. 2500 1. 2520 1. 2617 1. 2830 1. 3030 1. 3036 1. 3263 1. 3274 1. 4369 The article argues that certain hypotheses can be tested irregardless of whether one believes in the validity of the simple CAPM or in any other version of the theory. Firstly, the theory indicates that higher risk (beta) is associated with a higher level of return. However, the results of the study do not International Research Journal of Finance and Economics – Issue 4 (2006) 85 support this hypothesis. The beta coefficients of the 10 portfolios do not indicate that higher beta portfolios are related with higher returns. Portfolio 10 for example, the highest beta portfolio ( ? = 1. 2024), yields negative portfolio returns. In contrast, portfolio 1, the lowest beta portfolio ( ? = 0. 5474) produces positive returns. These contradicting results can be partially explained by the significant fluctuations of stock returns over the period examined (Table 2). Table 2: Average excess portfolio returns and betas (Equation 3) rp beta (p) a10 . 0001 . 5474 b10 . 0000 . 7509 c10 -. 0007 . 9137 d10 -. 0004 . 9506 e10 -. 0008 . 9300 f10 -. 0009 . 9142 g10 -. 0006 1. 0602 h10 -. 0013 1. 1066 i10 -. 0004 1. 1293 j10 -. 0004 1. 2024 Average Rf . 0014 Average rm=(Rm-Rf) . 0001 Source: Metastock (Greek) Data Base and calculations (S-PLUS) Portfolio Var. Error . 0012 . 0013 . 0014 . 0014 . 0009 . 0010 . 0012 . 0019 . 0020 . 0026 R2 . 4774 . 5335 . 5940 . 6054 . 7140 . 6997 . 6970 . 6057 . 6034 . 5691 In order to test the CAPM hypothesis, it is necessary to find the counterparts to the theoretical values that must be used in the CAPM equation. In this study the yield on the 3-month Greek Treasury Bill was used as an approximation of the risk-free rate. For the R m , the ASE Composite Share index is taken as the best approximation for the market portfolio. The basic equation used was rP = ? 0 + ? 1 ? ? P + e P (Equation 4) where ? is the expected excess return on a zero beta portfolio and ? 1 is the market price of risk, the difference between the expected rate of return on the market and a zero beta portfolio. One way for allowing for the possibility that the CAPM does not hold true is to add an intercept in the estimation of the SML. The CAPM considers that the intercept is zero for every asset. Hence, a test can be constructed to ex amine this hypothesis. In order to diversify away most of the firm-specific part of returns, thereby enhancing the precision of the beta estimates, the securities were previously combined into portfolios. This approach mitigates the statistical problems that arise from measurement errors in individual beta estimates. These portfolios were created for several reasons: (i) the random influences on individual stocks tend to be larger compared to those on suitably constructed portfolios (hence, the intercept and beta are easier to estimate for portfolios) and (ii) the tests for the intercept are easier to implement for portfolios because by construction their estimated coefficients are less likely to be correlated with one another than the shares of individual companies. The high value of the estimated correlation coefficient between the intercept and the slope indicates that the model used explains excess returns (Table 3). 86 International Research Journal of Finance and Economics – Issue 4 (2006) Table 3: Statistics of the estimation of the SML (Equation 4) Coefficient ? 0 Value . 0005 t-value (. 9011) p-value . 3939 Residual standard error: . 0004 on 8 degrees of freedom Multiple R-Squared: . 2968 F-statistic: 3. 3760 on 1 and 8 degrees of freedom, the p-value is . 1034 Correlation of Coefficients 0 ,? 1 = . 9818 ? 1 -. 0011 (-1. 8375) . 1034 However, the fact that the intercept has a value around zero weakens the above explanation. The results of this paper appear to be inconsistent with the zero beta version of the CAPM because the intercept of the SML is not greater than the interest rate on risk free-bonds (Table 2 and 3). In the estimation of SML, the CAPM’s prediction for ? 0 is that it should be equal to zero. The calculated value of the intercept is small (0. 0005) but it is not significantly different from zero (the tvalue is not greater than 2) Hence, based on the intercept criterion alone the CAPM hypothesis cannot clearly be rejected. According to CAPM the SLM slope should equal the excess return on the market portfolio. The excess return on the market portfolio was 0. 0001 while the estimated SLM slope was – 0. 0011. Hence, the latter result also indicates that there is evidence against the CAPM (Table 2 and 3). In order to test for nonlinearity between total portfolio returns and betas, a regression was run between average portfolio returns, calculated portfolio betas, and the square of betas (Equation 5). Results show that the intercept (0. 0036) of the equation was greater than the risk-free interest rate (0. 014), ? 1 was negative and different from zero while ? 2 , the coefficient of the square beta was very small (0. 0041 with a t-value not greater than 2) and thus consistent with the hypothesis that the expected return-beta relationship is linear (Table 4). Table 4: Testing for Non-linearity (Equation 5) Coefficient ? 0 Value . 0036 t-value (1. 7771) p-value 0. 1188 Residual standard error: . 0003 o n 7 degrees of freedom Multiple R-Squared: . 4797 F-statistic: 3. 2270 on 2 and 7 degrees of freedom, the p-value is . 1016 ? 1 -. 0084 (-1. 8013) 0. 1147 ? 2 . 0041 (1. 5686) 0. 1607 According to the CAPM, expected returns vary across assets only because the assets’ betas are different. Hence, one way to investigate whether CAPM adequately captures all-important aspects of the risk-return tradeoff is to test whether other asset-specific characteristics can explain the crosssectional differences in average returns that cannot be attributed to cross-sectional differences in beta. To accomplish this task the residual variance of portfolio returns was added as an additional explanatory variable (Equation 6). The coefficient of the residual variance of portfolio returns ? 3 is small and not statistically different from zero. It is therefore safe to conclude that residual risk has no affect on the expected return of a security. Thus, when portfolios are used instead of individual stocks, residual risk no longer appears to be important (Table 5). International Research Journal of Finance and Economics – Issue 4 (2006) Table 5: Testing for Non-Systematic risk (Equation 6) Coefficient ? 0 ? 1 Value . 0017 -. 0043 t-value (. 5360) (-. 6182) p-value 0. 6113 0. 5591 Residual standard error: . 0003 on 6 degrees of freedom Multiple R-Squared: . 5302 F-statistic: 2. 2570 on 3 and 6 degrees of freedom, the p-value is . 1821 ? 2 . 0015 (. 3381) 0. 7468 ? 3 . 3503 (. 8035) 0. 523 87 Since the analysis on the entire five-year period did not yield strong evidence in favor of the CAPM we examined whether a similar approach on yearly data would provide more supportive evidence. All models were tested separately for each of the five-year period and the results were statistically better for some years but still did not support the CAPM hypothesis (Tables 6, 7 and 8). Table 6: Statistics of the estimation SML (yearly series, Equation 4) 1998 1999 2000 2001 2002 Coefficient ? 0 ? 1 ? 0 ? 1 ? 0 ? 1 ? 0 ? 1 ? 0 ? 1 Value . 0053 . 0050 . 0115 . 0134 -. 0035 -. 0149 . 0000 -. 0057 -. 0017 -. 0088 t-value (3. 7665) (2. 231) (2. 8145) (4. 0237) (-1. 9045) (-9. 4186) (. 0025) (-2. 4066) (-. 8452) (-5. 3642) Std. Error . 0014 . 0022 . 0041 . 0033 . 0019 . 0016 . 0024 . 0028 . 0020 . 0016 p-value . 0050 . 0569 . 2227 . 0038 . 0933 . 0000 . 9981 . 0427 . 4226 . 0007 Table 7: Testing for Non-linearity (yearly series, Equation 5) 1998 Coefficient ? 0 ? 1 ? 2 ? 0 ? 1 ? 2 ? 0 ? 1 ? 2 ? 0 ? 1 ? 2 ? 0 ? 1 ? 2 Value . 0035 . 0139 -. 0078 . 0030 -. 0193 . 0135 -. 0129 . 0036 -. 0083 . 0092 -. 0240 . 0083 -. 0077 . 0046 -. 0059 t-value (1. 7052) (1. 7905) (-1. 1965) (2. 1093) (-. 7909) (1. 3540) (-3. 5789) (. 5435) (-2. 8038) (1. 2724) (-1. 7688) (1. 3695) (-2. 9168) (. 139) (-2. 7438) Std. Error . 0020 . 0077 . 0065 . 0142 . 0243 . 0026 . 0036 . 0067 . 0030 . 0072 . 0136 . 0060 . 0026 . 0050 . 0022 p-value . 1319 . 1165 . 2705 . 0729 . 4549 . 0100 . 0090 . 6037 . 0264 . 2439 . 1202 . 2132 . 0224 . 3911 . 0288 1999 2000 2001 2002 88 International Research Journal of Finance and Economics – Issue 4 (2006) Table 8: Testing for Non-Systematic risk (yearly series, Equation 6) 1998 Coefficient ? 0 ? 1 ? 2 ? 3 ? 0 ? 1 ? 2 ? 3 ? 0 ? 1 ? 2 ? 3 ? 0 ? 1 ? 2 ? 3 ? 0 ? 1 ? 2 ? 3 Value . 0016 . 0096 -. 0037 3. 0751 . 0017 -. 0043 . 0015 . 3503 -. 0203 . 0199 -. 0185 2. 2673 . 0062 -. 0193 . 0053 1. 7024 -. 0049 . 000 -. 0026 -5. 1548 t-value (. 7266) (1. 2809) (-. 5703) (. 5862) (1. 4573) (-. 0168) (. 0201) (2. 2471) (-4. 6757) (2. 2305) (-3. 6545) (2. 2673) (. 6019) (-1. 0682) (. 5635) (. 4324) (-. 9507) (. 0054) (-. 4576) (-. 6265) Std. Error . 0022 . 0075 . 0065 1. 9615 . 0125 . 0211 . 0099 1. 4278 . 0043 . 0089 . 0051 . 9026 . 0103 . 0181 . 0094 3. 9369 . 0052 . 0089 . 0058 8. 2284 p-value . 4948 . 2475 . 5892 . 1680 . 1953 . 9846 . 9846 . 0657 . 0034 . 0106 . 0106 . 0639 . 5693 . 3265 . 5935 . 6805 . 3785 . 9959 . 6633 . 5541 1999 2000 2001 2002 VI. Concluding Remarks The article examined the validity of the CAPM for the Greek stock market. The study used weekly stock returns from 100 companies listed on the Athens stock exchange from January 1998 to December 2002. The findings of the article are not supportive of the theory’s basic hypothesis that higher risk (beta) is associated with a higher level of return. In order to diversify away most of the firm-specific part of returns thereby enhancing the precision of the beta estimates, the securities where combined into portfolios to mitigate the statistical problems that arise from measurement errors in individual beta estimates. The model does explain, however, excess returns. The results obtained lend support to the linear structure of the CAPM equation being a good explanation of security returns. The high value of the estimated correlation coefficient between the intercept and the slope indicates that the model used, explains excess returns. However, the fact that the intercept has a value around zero weakens the above explanation. The CAPM’s prediction for the intercept is that it should be equal to zero and the slope should equal the excess returns on the market portfolio. The findings of the study contradict the above hypothesis and indicate evidence against the CAPM. The inclusion of the square of the beta coefficient to test for nonlinearity in the relationship between returns and betas indicates that the findings are according to the hypothesis and the expected returnbeta relationship is linear. Additionally, the tests conducted to investigate whether the CAPM adequately captures all-important aspects of reality by including the residual variance of stocks indicates that the residual risk has no effect on the expected return on portfolios. The lack of strong evidence in favor of CAPM necessitated the study of yearly data to test the validity of the model. The findings from this approach provided better statistical results for some years but still did not support the CAPM hypothesis. The results of the tests conducted on data from the Athens stock exchange for the period of January 1998 to December 2002 do not appear to clearly reject the CAPM. This does not mean that the data do not support CAPM. As Black [1972] points out these results can be explained in two ways. First, measurement and model specification errors arise due to the use of a proxy instead of the actual market International Research Journal of Finance and Economics – Issue 4 (2006) 89 ortfolio. This error biases the regression line estimated slope towards zero and its estimated intercept away from zero. Second, if no risk-free asset exists, the CAPM does not predict an intercept of zero. How to cite Capital Asset Pricing Model and International Research Journal, Essays

The Doctoral Research Process â€Æ

Question: Write an essay on The Doctoral Research Process ? Answer: Source: Maleki, M., Akbarzadeh Pasha, M. (2012). Ethical Challenges: Customer's Rights., 9(4), 5-21. The ecommerce business has opened up potential business opportunities. But there are various ethical issues that are arising which mostly surround the customers. There are various ways in which the ethics has affected the ecommerce business. This has increased the responsibility of the IT professionals towards increasing the trust of the consumers towards the e-commerce business by introducing certain ethical standards and abiding by those standards. This will make the ecommerce business more reliable and trust worthy (Jimerson, 2013). But the most worrisome activity for the customers as well as the developers is the trusting on the ecommerce sites (Fabregat, 2013). Technology has opened up new opportunities but it has also introduced certain illegal and immoral activities. The general society including the IT professionals has to abide by the ethical standards. There are some ethical challenges that are faced by the engineers while providing the service to the customers. But the IT professionals involved in the e-commerce business has to abide by the ethical principles so that the trust of the consumers increases and any kind of risk related to the ethics is avoided. This will increase the trust of the consumers and they will be able to trust more on the e-commerce sites. The e-commerce business activity will grow ('eCommerce', 2013). It is utmost important that the IT professionals follow the ethical guidelines which will increase the confidence of the customers. They must seek for possible solutions to encourage the trust of the consumers (Guptara, 1999). Source: Kenneth McBride, N. (2014). ACTIVE ethics: an information systems ethics for the internet age. J Of Inf, Com Eth In Society, 12(1), 21-44. doi:10.1108/jices-06-2013-0017 Ethical standards in the information systems are an important aspects and it increases the responsibility of the IT professionals to abide by the ethical guidelines. The information system forms an essential part of the social systems and the ethical and the social issues will be addressed by the cooperation of the IT professionals (Scott, 2002). It will lead to the successful implementation of the information system services. There are certain codes of ethics that the IT professional has to abide. These codes of ethics form an important part of the business. The customer trust and reliability towards the various information systems will increase as a result of the participation of the IT professionals in maintenance of the integrity of the IT industry. IT has opened up new opportunities but it has also introduced potential hazards. It has introduced various forms of illegal activities. It has also introduced various illegal activities regarding the use of the Internet. It is the res ponsibility of the IT professionals to build firewall protection and provide password protection so that the illegal activities can be prevented in an adequate manner. This will increase the trust and reliability of the customers. The illegal activities can be reduced to a considerable extent (Jin, Drozdenko Bassett, 2006). Source: Monk,,. (2015). Good engineers. European Journal Of Engineering Education, 22(3), 235. The IT professionals are responsible for making judgments and promoting the decisions. The code of practices is used by the IT professionals for the purpose of decision making. But the decision making process of the IT professionals is a tough job. The IT professionals have to keep a balance between the good judgment and the bad judgment. They have to use their wisdom to make decisions. The customers face certain ethical dilemmas which tend to break their trust. But the IT professionals have to ensure that the ethical values are preserved. The customers tend to be swayed away by the advertisements regarding the performance of a particular electronic product but it is the responsibility of the IT professionals to prepare the product in such a manner so that the trust of the customers is maintained. The products must deliver the high performance and it must be efficient (Derbyshire, 2008). This will make the device efficient and user friendly. The reliability and trust of the consumers will increase. Thus the IT professionals have to abide by the code of conduct and they must not suffer from ethical dilemmas. This increase the confidence of the customers and the reliability will increase (Bauml, 2013). Source: Monk, J. (2008). Ethics, Engineers and Drama. Sci Eng Ethics, 15(1), 111-123. doi:10.1007/s11948-008-9099-9 The IT professionals are seen to suffer from ethical dilemmas and they are not able to make wise choices between the available alternatives. The engineers must possess the skills to deal with the ethical issues that are faced by them. They must be able to deal with the ethical issues and make their engagement with people that are persuasive and know how to make negotiations. While IT professionals have obligations regarding the successful creation, establishment, operation, upkeep or transfer of hardware they are prone to have extra allegiances inside of the more extensive group and duties towards closer weave gatherings, for example, work associates and crew (den Bergh Deschoolmeester, 2010). These diverse gatherings have distinctive intrigues and worth things distinctively and this can create circumstances that set one obligation or worth against another. Defied by such circumstances the designer needs to make his or her own particular judgments and pass on them convincingly. Freq uently the circumstance is developing quickly so judgments lay on fractional or confounding information shaded by forceful feelings and passed on with differing degrees of ability and flurry. By and by, unpalatable results can rise in light of justifiable misjudgments or since a proficient's pride or willfulness makes they disregard choices, since different hobbies are purposely and untrustworthily distorted or on the grounds that activity to accommodate a problem is in light of flawed rationale. Such settings have the potential to prompt individual tragedies, and in this way recommend situations for screenwriters to abuse. While numerous contextual investigations barely concentrate on designing issues, dramatization is generally kept in touch with location a wide crowd and ordinarily conveys to tolerate a more extensive scope of human concerns which are obligated to impact the honing proficient. Periodically the sensational connection has a designing flavor and the outcome is valua ble instructive asset for building understudies. Formalized implicit rules, for example, are an aide and endeavor to suspect troublesome circumstances however they are likewise a rich hotspot for the playwright (Brunswick, 2009). The screenwriter's assignment is not to develop the code, in any case, to design circumstances where the tenets of the code create inconsistencies. The ethics and code of conduct of the IT professionals are as important as the code of ethics of the doctors. Violation of the ethical codes is subjected to punishment. There must be transparency between the customers and the IT professionals. The transparency of the procedures will make the system legal and it will not be subjected to any kind of illegal activity. The moral principles have to be followed. The reliability of the customers will increase. They will trust in ecommerce sites and the information that is presented in the internet. The awareness of the IT professionals has to be increased that will promote the awareness of the IT professionals towards the ethical values and guidelines. This will make the IT professionals more disciplined and they will focus towards the information users. The IT professionals have experience on the professional development and they must focus on the customer service (Cofta, 2006). References Bauml, U. (2013). eCommerce - Der Kunde ist im Netz.CON,25(6), 281-283. doi:10.15358/0935-0381_2013_6_281 Brunswick, S. (2009). eCommerce fraud time to act?.Card Technology Today,21(1), 12-13. doi:10.1016/s0965-2590(09)70019-2 Cofta, P. (2006). 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