Reward Management Theories

Prize Management Theories Prize administration has been portrayed as a key capacity in HRM frameworks in present day associations, and it is regularly intended to pull in, hold and rouse workers (Milkovich and Newman, 2004 refered to in Yu, ying and Angeles, 2011, pp 2). In view of the vital idea of human asset in the fulfillment of corporate objectives and authoritative intensity (Wright and McMahan, 1992), numerous creators have examined the subject of remuneration the executives from alternate points of view. A portion of these points of view center around isolating prize frameworks into two classifications; Extrinsic and inherent prizes. Extraneous prize frameworks for the most part center around offering execution connected pay increments, financial prizes, long haul motivator plans, group based prizes, and security advantages to representatives (Laursen and Foss, 2003). These creators contend for the estimations of these kinds of remuneration in boosting the confidence of workers and improving their eff iciency as a similar time. The consequence of this to the association is better main concern execution through expanded income and productivity (Yu, Ying Yang and Angeles, 2011). Characteristic prizes are non-financial rewards and could come in type of advancing occupation duty and strengthening (Oldham and Cummings, 1996), giving preparing assets and far reaching learning openings (Hennessey and Amabile, 1998), and keeping up great relational connections among workers (Ruppel and Harrington, 2000). Yu, Ying and Angeles (2011) distinguish two prize administration points of view dependent on the above grouping and they call outward prize frameworks, utilitarianism and characteristic prize frameworks, sentimentalism. As indicated by these creators, these are the two exceptionally easy to refute speculations of remuneration the board in the HRM field. One of the fundamental suspicions hidden the hypotheses of remuneration the board is that the exhibition and inspiration of workers can be improved by building up a connection among endeavors and prizes through formalized and explicit individual targets (Fay and Thompson, 2001). Albeit numerous writers and essayists have set up that there are gigantic advantages for associations in placing in a spot a successful prize administration framework, a few writers despite everything caution against over-dependence on remuneration frameworks due to its capability to make a few authoritative issues which center around reasonableness and assorted variety (Beer and Cannon, 2004). Numerous different creators have basically inspected the hidden methods of reasoning of remuneration the board and the factors that make up a run of the mill reward blend. Baeten (2008 refered to in Jonathan and Clare, 2011, pp 2) gives a more profound knowledge into this when they contend that there are 34 various potential speculations hidden a prize administration framework. Eisenhardt (1988 refered to in Jonathan and Clare, 2011, pp 3) anyway restrains reward the executives speculations to two in particular: The organization hypothesis and the institutional hypothesis. Office speculations of remuneration the executives essentially look to adjust association and individual targets. The principle point is to utilize impetuses to coordinate representatives towards the interests of the proprietors of the business, and to structure the prize blend to mirror that. Then again institutional prize administration hypothesis centers around the formal and casual weights applied on associations by different associations with which it is associated. A portion of these weights could come in type of business enactments, lawful prerequisites, charge strategies, and a craving to receive the approaches that others have embraced in order to pick up the security that similarity is seen to bring (DiMaggio and Powell , 1991). At long last other known hypothesis of remuneration the executives under the institutional grouping is regularizing pressure. This happens when individuals from an association on the whole met up to characterize the conditions and strategies for their works. 4 The plan of strategy and set of methods HP works in an exceptionally serious industry with an impossible to miss high staff turnover rates. Workers are frequently impacted by serious prize approaches that add to the achievement of their self-awareness objectives and destinations. Kerr (1999) contends that prizes ought to be the third thing in an association; estimations ought to be second, and away from of the ideal results ought to be the first. In structuring a prize strategy for HP coming up next are the key goals to be accomplished: 4.1 Policy explanation HP will probably draw in and hold the most capable workforce which are deliberately associated with the association's capacity to meet its vital objectives and destinations. In light of this we try to offer a mix of the most serious and adaptable money related awards in the US versatile and PC industry to all units of workers promptly they go along with us. We accept this is imperative to the support of our current initiative situation in the business and we try to seek after this strategy as stubbornly as conceivable with all decency and value. 4.1.1 Procedures Point The focal point of this approach is to empower upgrade HP's capacity to enroll the most capable specialists in the US versatile and PC innovation industry which we consider as vital to the accomplishment of our corporate objectives and destinations. Degree This budgetary prize strategy will apply to all normal full time and ordinary low maintenance workers of HP quickly they join the organization. Standards The whole prize arrangement of this organization will be based on an establishment of uniformity, pledge to assorted variety, reasonableness and a feeling of decision making ability The whole prize strategy will be worked as straightforwardly as could be expected under the circumstances and it will be made to consent to national administrative systems for worker commitment in the PC and versatile innovation industry All classifications of workers that contribute more by proportion of efficiency will be monetarily compensated more. HP will not take any additional commitment of any representative for conceded. We accept that is significant to satisfying our responsibility to correspondence and decency. Duties Job Responsibility Prizes group Guaranteeing that budgetary arrangement reward exercises are solidly set up and checking that the fundamental standards of the prize are maintained HR Director and HR initiative group Giving control activities in circumstances where the standards of the arrangement are not maintained Senior administration gathering Guaranteeing the correct arrangement of HP's corporate methodology and the prize approach 4.2 Correspondence to pertinent national enactment The US branch of work has built up guidelines in regards to employees㠢â‚ ¬Ã¢â€ž ¢ pay rates and wages set up. One of such guidelines is the reasonable work standard Act of 1938 which is directed by the wages and hour division. This Act sets up norms for least wages, additional time, pay and record keeping. These measures influence more than 130 million full time and low maintenance workers at both private and open segments of the US economy. This demonstration advances reasonableness, equity and value and enables workers to start a claim against any business who abuses any of the standards and strategies of the Act. This new strategy altogether conforms to the measures set out in the US branch of work Act of 1938 in all decency and value. The arrangement is additionally straightforward enough to give workers the independence to found claims against HP if the companys strategy is seen to be opposing to national enactments. 4.3 International ramifications The ILO (International Labor Organization) is the body accountable for controlling work guidelines and the association by and by involves 183 countries all around the globe. The ILO has embraced 188 shows which are settlements making global work law measures. The US has just clung to two of these work law norms which are the show on the annulment of constrained work and the show on the most noticeably awful type of youngster work. The US has opposed the appropriation of different pieces of the show including those that control compensation and prizes to representatives from a worldwide point of view. Remuneration and awards at both official and representative level have not been completely evolved in accordance with ILO guidelines. The US asserts that its arrangements have been grown inside and seriously and don't should be changed in accordance with reflect global principles. As serious as the current prize approach in HP seems to be, it is as yet imperative to embrace a universal v iew in its usage. Source:http://www.iclg.co.uk/khadmin/Publications/pdf/4390.pdf-got to on 30-04-2011 5 Evaluation of strategy and methodology 5.1 Reward assessment framework Armstrong, Brown and Reilly (2009) endorse six measurements for assessing the achievement of any prize administration strategy. Taking into account this the strategies for assessing the viability of HP's prize strategy are set out beneath. Prize Aspect Estimated By Prize system Clear prize technique and yearly arrangement on the side of business procedure and plan Efficiency and prize expenses Benefit, esteem included or deals per worker Complete compensation and prize expenses contrasted and rivalry in the US PC industry Budgetary prize Budgetary acknowledgment for practices in accordance with methodology and qualities Proper types of remunerating execution and commitment set up Extent of staffs secured by techniques for remunerating execution and commitment Hazard evaluation of reward plan structure Away from of profit for expenses of reward Business Proportion of bids for employment to acknowledgments Staff automatic turnover to renunciation rates and maintenance of superior to key ability staff Staff turnover and nonappearance levels Prize administration general Real market position contrasted with wanted Equivalent compensation audits completed and followed up on Amount, quality and recurrence of remuneration correspondence Commitment and fulfillment with remunerations Overviewed by and large representative commitment levels Worker by and large fulfillment with p

Demonstrate Leadership in Your Mba Application

Exhibit Leadership in Your MBA Application When confronted with any cycle of the initiative inquiry on MBA expositions, numerous business college candidates blow a gasket since they think they need to concoct a model that is their most noteworthy life or expert accomplishment. As a general rule, it's not about stupendous signals or formal initiative titles. The objective is leaving an impression on whatever circumstance you're in and accomplishing in excess of a great job. Candidates need to thoroughly consider their past encounters to discover the scenes that best show their initiative skills.Sometimes, the best models are not the primary that ring a bell. Your initiative paper will regularly be unique in relation to a â€Å"accomplishment† or â€Å"achievement† situated exposition. Because you accomplished something extraordinary doesn't generally mean initiative aptitudes were included, particularly on the off chance that you did most or the entirety of the work. [Le arn how to send out the correct vibe in MBA expositions. ] One of the focal fundamentals of administration papers is demonstrating that you can stir the activities of others. You draw out their interests. You instruct them. You assist them with seeing hierarchical needs in new ways.And then they share in the accomplishment. Crafted by a pioneer initiates or improves crafted by others, so discover accounts in your expert and extracurricular foundation that delineate this sort of example. What sort of encounters will make the best stories of initiative? Consider difficulties where the accompanying became possibly the most important factor: †¢ Identifying/characterizing an issue †¢ Resisting customary methodologies; testing business as usual †¢ Marshaling assets to address an issue †¢ Motivating others †¢ Making great utilization of others' gifts †¢ Being available to new data and information Building agreement with proper partners †¢ Guiding solid mi dcourse amendments; conquering botches †¢ Building on progress [Get more tips on composing MBA affirmations papers. ] Remember: Leadership isn't just about the titles. A few up-and-comers assemble their administration expositions around the way that they were chosen for or chosen for specific positions where they had an elevated level of power and duty: manager in-head of a school paper, club president, chief of the hockey group, executive of item improvement, or VP of marketing.But what did you do with this position? A proofreader of a school every day could expound on how the individual in question was continually tested to keep up significant levels of publication greatness, oversee staff assignments, and hit all cutoff times. This is unquestionably a regarded position with numerous obligations, yet in the event that you portray your job that way, it sounds precisely equivalent to different many editors-in-head of school papers additionally applying this season. Characterize the initiative difficulties you confronted, not the administration ones.Did you need to manage a specific essayist who distorted meeting notes? Was there a clingy grounds embarrassment that constrained you and your staff to walk a moral tightrope? Did you need to fire understudy editors? Did you lead a change from a week after week to a day by day with the entirety of the planning and HR rigors that involves? Gathering noteworthy titles doesn't make somebody an incredible leaderâ€helping a group defeat extraordinary difficulties does. [Avoid seven fatal sins of MBA candidates. ] The most grounded authority expositions will have saints other than yourself.If you helped Terri in debt claims understand her maximum capacity on an undertaking you drove, feature her as a legend in your administration story. In the most ideal situation, candidates should outline a decent parity toward the start of their application procedure between accomplishment arranged expositions and those concent rating explicitly on initiative. Fortunately, in numerous examples, you can even now alter your application genuinely late in the process to accomplish the fitting harmony between singular accomplishment and leadership.Adding in a couple of sentences about empowering others, or teaching and characterizing needs for bunch attempts, will go far toward balancing your profile. Numerous accomplishment papers can be changed into sublime instances of initiative when you sparkle the focus on other people who were a piece of an incredible aggregate achievement. Remember that authority is never an independent exertion. With regards to MBA expositions, you can't turn out badly on the off chance that you show how you've attempted to move others and draw out the best in them

Bigger is Better Welcome Sandra, Max, Sebastian, Vid, Özge and Christina - Focus

Bigger is Better Welcome Sandra, Max, Sebastian, Vid, Özge and Christina - Focus Bigger is better doesnt only apply to cakes, TVs, wine glasses, and processing power, it also applies to team size! And thats the case for Meister! Weve just added another six team members to our clan, and theyll be utilizing their superpowers (a.k.a very special skills) in Design, Dev Ops, Development and Marketing. Sebastian Banzer: Dev Ops My background I am one of the few native Liechtensteiners there are in this world. Were a rare breed with a population count of just under 39 thousand. Born and raised as they say â€" I lived there my entire life. After finishing middle school and an apprenticeship as an IT Systems Engineer in August 2016, I was taken on as a full-time employee (at the same company where I completed my apprenticeship) and proceeded to work there for another three years. Ive always been interested in computers. My interest dates back to when my father introduced me to MS Paint on Windows 98. Ever since then, I refer to myself as a Paint Picasso even though my MS Paint skills didnt improve. My focus and interests shifted from MS Paint to automating infrastructure and building platforms. My Role at Meister As a DevOps Engineer, my goal is minimize the overhead for code delivery. So once more in English, that means I work to make life easier for our developers whilst simultaneously making sure that security and maintainability is kept at a good level. Ill advise developers on how to build their apps CI/CD ready and Ill also assist on designing and building automated code delivery into our test environments.   About me I really enjoy cooking, taking walks, binge-watching series and hanging out with friends. When Im bored (and in the mood), I occupy myself with a few Codesignal tasks and/or challenges, or playing games either on PC or Switch. Im also partial to a bit of phone browsing, reading reddits superior content on boards like TIFU and AITA. Maximilian Benjamim-Graf: Head of Customer Success My background I was born in a really small town, located in the heart of Austria, called Irdning. With only around 4000 inhabitants, I took the first chance I got to leave and explore the world. Whilst still in high-school, I did a semester abroad in Colorado and the travel bug bit me big time. I then went on to study management and law at the Management Center in Innsbruck, where I did an exchange program in Buenos Aires.   After that (I still wasnt ready to live in Austria) I relocated to London and spent the last four years working in customer service. I gained a wealth of experience within these roles as I took on a variety diverse cases ranging from small all the way up to larger more complicated matters. A quote I really like and that represents my life choices, both professionally and personally, is one by Yoda: Do or do not. There is no try. My Role at Meister As the Head of Customer Success, I am responsible for providing the best service for our millions of customers as well as guaranteeing that the teams skills are used in the most efficient and effective ways possible.   About me When I am not in the office, I spend my time studying and educating myself further. I am enrolled as a law student at the University of Vienna at the moment. Aside from that, my partner and I run a charity that supports members of the LGBTQ+ community in their coming out process. And, if there is still time left after all of that, I like to watch movies, binge watch Netflix or do sports.   Özge Kolay: Performance Marketer My background I come from Istanbul, Turkey and I lived there most of my life. After studying a Bachelor’s degree in Economics there, I moved to Germany to study a Master’s degree in Global Marketing Management. How did I end up in Vienna? Well, I didnt know much about the city before I arrived in 2016 to do an exchange semester, but as it turns out, I fell in love with the city! But, a very interesting job opportunity in online marketing moved me to Graz, where I spent the last two years. But Meister brought me back to the city that I love. Since my Master’s degree, Ive been back and fourth between Germany and Austria living a nomadic (gypsy) lifestyle. Alas, I think those days are over and I am very much looking forward to settling in in Vienna and at Meister. My Role at Meister As part of the Marketing and Growth team at Meister, my area of specialization will be on digital marketing. Ill work on Google, social media ad campaigns, search engine optimization, app store optimization, conversion optimization, and website landing pages. About Me I like traveling, theatre, music, reading, watching series and bike riding. I play a bit of electric guitar, and am part of an amateur band in Graz. I sang in a choir, and was a member of a few amateur theater groups. I also love taking little trips to different destinations and my first goal is to conquer Europe, then maybe Latin America or Australia.   Sandra Unterkircher: UI/UX Designer My background I come from Salzburg, Austria. I have a diploma in Communication Design from the Werbe-Design-Akademie (Marketing and Design Academy). The quest for more knowledge brought me to Vienna, where I went on to study Graphic and Information Design at the New Design University on top of my diploma. Aside from collecting and visualizing data, I was also drawn to coding. Through my studies, I was able to combine my two biggest passions â€" coding and digital design.   My role at Meister I’m a UI/UX designer in the MindMeister team at Meister. One of my responsibilities will be to make sure MindMeister continues to look great. On top of that, Ill work alongside the Meister design team providing input on design and UX tasks. I want all our users to have an amazing experience when using our tools. Additionally, Im responsible for web development, which means that I also get to implement the amazing designs we create. About me In my free time, you can probably find me out and about, exploring the world, while capturing everything with my camera. When I’m not doing that, chances are, I’ll be sitting in a café somewhere, reading a book or design articles over a cup of decaffeinated coffee. I also love nature, watching tv shows and spending time with friends and family. Vid Topolovec: Frontend Developer Processed with VSCO with c9 preset Processed with VSCO with a1 preset My background I grew up in Slovenia. Where I randomly chose to study computer science (which really didnt resonate with me at that time). After a year, I moved to Prague to study interactive media with a specialization in cartoon making. I planned on bringing my dreams to life through animation. However, due to the intensity of the course curriculum, I spent time reflecting on whether or not it was the right move and decided to move back to Slovenia and pursue a degree in mathematics and computer science. This is where I noticed that pretty much each time I solved a coding problem I experienced a very special kind of joy, which eventually led me to learn everything there is to know about the wonderful world of front end development. Finally, an answer to what I want to do professionally after years of searching. My role at Meister As a frontend developer in for MindMeister, Ill be in the team responsible for the user experience. That means that Ill look at building the app in the most user-friendly manner.   About me Im a hobby writer, birdwatcher, music enthusiast and fan of coding. Im the type of person that needs to understand why and how different kinds of patterns work â€" that makes me excited about topics such as: metaphysics, psychology and anything connected to nature. Ive also started digging into behavioral biology and Im absolutely amazed by it! On top of all that, Im also an avid chess player who loves to paint.   Christina Parson: Staff Writer My background My sister and I were lucky enough to grow up around the world with my German mother and American father, due to my fathers job as a diplomat. I earned my first two degrees (Bachelor of Music and Master of Business Administration) in the United States and then moved to Vienna to continue my studies in Opera Performance at the Music and Arts University. Somewhere along the way, I started teaching business English and then technical English, and before too long I was teaching a whole range of subjects at FH Technikum Wien.   After six years there, I made a side-step to work as a Technical Writer at an IT-security company, and found that the work suited me quite well. Im looking forward to applying all the things Ive learned on my twists and turns to my work at MeisterLabs. My role at Meister You should start to see my name pop up on Meister’s blog, Focus, in the near future.   I’ll be writing posts about various topics, such as new features in our apps, how to get the most out of our apps, as well as some personal favorites; stimulating your creativity and practicing mindfulness while working. I’ll also be working on the content on our websites, emails, and newsletters. Who knows what other interesting projects at Meister might find their way to me! About me Im an opera singer, constantly working towards the next project, production, concert, recital, competition or audition. For me, singing is an addiction and the biggest, best challenge of my life. I dont think Ill ever stop, but I very much enjoy the balance I find by working in an unrelated field. Bigger is Better Welcome Sandra, Max, Sebastian, Vid, Özge and Christina - Focus Bigger is better doesnt only apply to cakes, TVs, wine glasses, and processing power, it also applies to team size! And thats the case for Meister! Weve just added another six team members to our clan, and theyll be utilizing their superpowers (a.k.a very special skills) in Design, Dev Ops, Development and Marketing. Sebastian Banzer: Dev Ops My background I am one of the few native Liechtensteiners there are in this world. Were a rare breed with a population count of just under 39 thousand. Born and raised as they say â€" I lived there my entire life. After finishing middle school and an apprenticeship as an IT Systems Engineer in August 2016, I was taken on as a full-time employee (at the same company where I completed my apprenticeship) and proceeded to work there for another three years. Ive always been interested in computers. My interest dates back to when my father introduced me to MS Paint on Windows 98. Ever since then, I refer to myself as a Paint Picasso even though my MS Paint skills didnt improve. My focus and interests shifted from MS Paint to automating infrastructure and building platforms. My Role at Meister As a DevOps Engineer, my goal is minimize the overhead for code delivery. So once more in English, that means I work to make life easier for our developers whilst simultaneously making sure that security and maintainability is kept at a good level. Ill advise developers on how to build their apps CI/CD ready and Ill also assist on designing and building automated code delivery into our test environments.   About me I really enjoy cooking, taking walks, binge-watching series and hanging out with friends. When Im bored (and in the mood), I occupy myself with a few Codesignal tasks and/or challenges, or playing games either on PC or Switch. Im also partial to a bit of phone browsing, reading reddits superior content on boards like TIFU and AITA. Maximilian Benjamim-Graf: Head of Customer Success My background I was born in a really small town, located in the heart of Austria, called Irdning. With only around 4000 inhabitants, I took the first chance I got to leave and explore the world. Whilst still in high-school, I did a semester abroad in Colorado and the travel bug bit me big time. I then went on to study management and law at the Management Center in Innsbruck, where I did an exchange program in Buenos Aires.   After that (I still wasnt ready to live in Austria) I relocated to London and spent the last four years working in customer service. I gained a wealth of experience within these roles as I took on a variety diverse cases ranging from small all the way up to larger more complicated matters. A quote I really like and that represents my life choices, both professionally and personally, is one by Yoda: Do or do not. There is no try. My Role at Meister As the Head of Customer Success, I am responsible for providing the best service for our millions of customers as well as guaranteeing that the teams skills are used in the most efficient and effective ways possible.   About me When I am not in the office, I spend my time studying and educating myself further. I am enrolled as a law student at the University of Vienna at the moment. Aside from that, my partner and I run a charity that supports members of the LGBTQ+ community in their coming out process. And, if there is still time left after all of that, I like to watch movies, binge watch Netflix or do sports.   Özge Kolay: Performance Marketer My background I come from Istanbul, Turkey and I lived there most of my life. After studying a Bachelor’s degree in Economics there, I moved to Germany to study a Master’s degree in Global Marketing Management. How did I end up in Vienna? Well, I didnt know much about the city before I arrived in 2016 to do an exchange semester, but as it turns out, I fell in love with the city! But, a very interesting job opportunity in online marketing moved me to Graz, where I spent the last two years. But Meister brought me back to the city that I love. Since my Master’s degree, Ive been back and fourth between Germany and Austria living a nomadic (gypsy) lifestyle. Alas, I think those days are over and I am very much looking forward to settling in in Vienna and at Meister. My Role at Meister As part of the Marketing and Growth team at Meister, my area of specialization will be on digital marketing. Ill work on Google, social media ad campaigns, search engine optimization, app store optimization, conversion optimization, and website landing pages. About Me I like traveling, theatre, music, reading, watching series and bike riding. I play a bit of electric guitar, and am part of an amateur band in Graz. I sang in a choir, and was a member of a few amateur theater groups. I also love taking little trips to different destinations and my first goal is to conquer Europe, then maybe Latin America or Australia.   Sandra Unterkircher: UI/UX Designer My background I come from Salzburg, Austria. I have a diploma in Communication Design from the Werbe-Design-Akademie (Marketing and Design Academy). The quest for more knowledge brought me to Vienna, where I went on to study Graphic and Information Design at the New Design University on top of my diploma. Aside from collecting and visualizing data, I was also drawn to coding. Through my studies, I was able to combine my two biggest passions â€" coding and digital design.   My role at Meister I’m a UI/UX designer in the MindMeister team at Meister. One of my responsibilities will be to make sure MindMeister continues to look great. On top of that, Ill work alongside the Meister design team providing input on design and UX tasks. I want all our users to have an amazing experience when using our tools. Additionally, Im responsible for web development, which means that I also get to implement the amazing designs we create. About me In my free time, you can probably find me out and about, exploring the world, while capturing everything with my camera. When I’m not doing that, chances are, I’ll be sitting in a café somewhere, reading a book or design articles over a cup of decaffeinated coffee. I also love nature, watching tv shows and spending time with friends and family. Vid Topolovec: Frontend Developer Processed with VSCO with c9 preset Processed with VSCO with a1 preset My background I grew up in Slovenia. Where I randomly chose to study computer science (which really didnt resonate with me at that time). After a year, I moved to Prague to study interactive media with a specialization in cartoon making. I planned on bringing my dreams to life through animation. However, due to the intensity of the course curriculum, I spent time reflecting on whether or not it was the right move and decided to move back to Slovenia and pursue a degree in mathematics and computer science. This is where I noticed that pretty much each time I solved a coding problem I experienced a very special kind of joy, which eventually led me to learn everything there is to know about the wonderful world of front end development. Finally, an answer to what I want to do professionally after years of searching. My role at Meister As a frontend developer in for MindMeister, Ill be in the team responsible for the user experience. That means that Ill look at building the app in the most user-friendly manner.   About me Im a hobby writer, birdwatcher, music enthusiast and fan of coding. Im the type of person that needs to understand why and how different kinds of patterns work â€" that makes me excited about topics such as: metaphysics, psychology and anything connected to nature. Ive also started digging into behavioral biology and Im absolutely amazed by it! On top of all that, Im also an avid chess player who loves to paint.   Christina Parson: Staff Writer My background My sister and I were lucky enough to grow up around the world with my German mother and American father, due to my fathers job as a diplomat. I earned my first two degrees (Bachelor of Music and Master of Business Administration) in the United States and then moved to Vienna to continue my studies in Opera Performance at the Music and Arts University. Somewhere along the way, I started teaching business English and then technical English, and before too long I was teaching a whole range of subjects at FH Technikum Wien.   After six years there, I made a side-step to work as a Technical Writer at an IT-security company, and found that the work suited me quite well. Im looking forward to applying all the things Ive learned on my twists and turns to my work at MeisterLabs. My role at Meister You should start to see my name pop up on Meister’s blog, Focus, in the near future.   I’ll be writing posts about various topics, such as new features in our apps, how to get the most out of our apps, as well as some personal favorites; stimulating your creativity and practicing mindfulness while working. I’ll also be working on the content on our websites, emails, and newsletters. Who knows what other interesting projects at Meister might find their way to me! About me Im an opera singer, constantly working towards the next project, production, concert, recital, competition or audition. For me, singing is an addiction and the biggest, best challenge of my life. I dont think Ill ever stop, but I very much enjoy the balance I find by working in an unrelated field.

Differences Between American Culture And Indian Culture

Culture is the characteristics and knowledge of a particular group of people, defined by everything from language, religion, cuisine, social habits, music and arts. The cultures around the world are very different and very much the alike at the same time. On the other hand, some similarities also lie between two nations. While the culture of America is a mixture of different cultures, the Indian culture is unique and has its own values. There are many types of differences lies between American culture and Indian culture in terms of Religions, Languages, Rituals and Cuisines. Religious is a set of common beliefs and practices generally held by a group of people that is usually separated by rituals and religious laws. There are so many cultures within each country. India is one of the most religiously diverse country in the world with one of the most deeply religious societies and cultures. Religion plays a central and definitive role in the life of many of its people. The religions th at originated in the Indian subcontinents are Hinduism, Islam, Buddhism, Jainism, Sikhism and Christianity. The most of India s population is Hindu which contributes towards the 70% of the population. Hinduism is one of the oldest religions in the world. It was developed about 5000 years ago. Hinduism is a colorful religion with a lot of rituals. People who follow this religion believe in a lot of Gods and Goddesses. About 12% of India s population is Islam. This is a religion which was notShow MoreRelatedDifference Between American And Indian Cultures Essay1092 Words   |  5 PagesAs we know, all cultures have their differences. Cultural diversity is the quality of diverse or different cultures. I have chosen to discuss the difference between American and Indian cultures. Particularly, the culture surrounding pregnancy and birth. Thanks to globalization, there are Indians giving birth in America and Americans giving birth in India. 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In â€Å"Doors†, Preeti struggles to balance her American beliefs with the Indian beliefs of her husband, similarly to AnjuRead MoreEssay Differences Between Indian and American Culture1472 Words   |  6 Pages AMERICAN CULTURE VS INDIAN CULTURE Cultural comparison (West vs East) â€Æ' Everybody has their own definition of Culture – and when this word is used generally, most audiences have a rough idea of its meaning. Culture usually refers to the beliefs, ideas, languages, rituals and traditions by certain communities, that are passed from generation to generations continuously over the past many centuries. In society, two cultures cannot be same if one is located on the west coast and the other one isRead MoreWestern Culture And Indian Culture983 Words   |  4 PagesCulture is the characteristics and knowledge of people, the way of life, defined by everything from language, religion, cuisine, social habits, music and arts. Every country has its own culture and traditions. 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The Legalization Of Medical Marijuana - 1314 Words

Marijuana is the most frequently abused illegal substance worldwide. Not only is there no legitimate medical use, it has been tied to physical, mental, and emotional damages. â€Å"Marijuana refers to the dried leaves, flowers, stems, and seeds from the hemp plant Cannabis sativa, which contains the psychoactive (mind-altering) chemical delta-9-tetrahydrocannabinol (THC), as well as other related compounds† (National Institute on Drug Abuse). There are many supporters of the legalization of medical marijuana that deem it is safe and of benefit. Plenty of states have passed laws for the use of medical marijuana. Many advocates claim it will treat symptoms of severe illnesses, but there isn t enough studies to prove it. The legalization of medical marijuana will make it easier for young adults to get their hands on this dangerous drug. Also, many people will abuse the law and receive this drug for no particular medical reason. 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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

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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