The History And Debate Of Euthanasia Philosophy Essay

The History And Debate Of Euthanasia Philosophy Essay Euthanasia has been a controversial issue for a very long time. The ancient Romans and Greeks supported euthanasia after the interpretation of the Hippocratic Oath that was written around 400 B.C. They believed that the persons life should not be preserved if this person has no interest in life. Hence, voluntary euthanasia was not banned in the ancient Greek and Roman civilizations. However, committing Suicide as well as helping others to commit suicide was considered as a criminal act by the English jurisdiction during the 1300s. Euthanasia faced the first direct law against it in New York at the 1828 which was known as the Anti-Euthanasia law. Euthanasia like Abortion had become a major issue for debating in the following decades until the recent days .(Sandhyarani, 2001). Nowadays, all dictionaries and references define euthanasia as mercy killing of patients in severe incurable pain. Oxford dictionary for example, has defined euthanasia as: the painless killing of a patient suffe ring from an incurable and painful disease or in an irreversible coma. The way these definitions were defined came from the origin of the word Euthanasia, where Euthanasia is a Greek word came from the 17th century to combine two words, Eu which means well and easy and Thanatos meaning death.(Oxford dictionary, 2010). Euthanasia nowadays can be categorized into many different forms and types, the first and most common one is the active voluntary euthanasia where the patient is mercifully killed with his own will and request, its also known as the assisted suicide. Other different form is known as the involuntary passive euthanasia that let the patients die without their own request, this kind is known for patients who are in comas or unable to talk or communicate with others.(BBC, 2009). With the current debates and developments in the world, euthanasia is being discussed globally, legalized in some countries and still discussed in others. Netherland was the first country in the world to legalize Euthanasia in 2002, followed by Belgium at the end of 2002 and some parts in the United States of America. Switzerland on the other hand allows the physician assisted suicide in special cases but the euthanasia is still not legal in this country. What must be known about the laws of these countries is that they are strictly standardized for euthanasia as euthanasia is being applied only to specific kinds of patients. Euthanasia is indeed one of the most controversial issues to date. Taking both sides, supporting and opposing euthanasia in the society, doctors and governments into considerations, the main question now centers on whether Euthanasia is the right act to consider on the cases with no cure and whether euthanasia should be legalized. The process of painlessly helping a terminally ill person to die should be legalized as its a merciful act that offers dignity and compassion at lifes cruel end. People who are euthanized are going to die anyway. However, by legalizing euthanasia, they can be saved from suffering terrible pain.(Friedman, 2010) Therefore, governments should not stand in the way of letting severely ill people with no chances in getting cured to end their lives legally by Euthanasia. The following research project will hereby focus on the reasons why euthanasia should be legalized, what we can prevent and gain by legalizing euthanasia, effects of euthanasia and its future. The time frame used in this research project is from 2000 till date, the research is showing the latest ideas and arguments presented in the world where euthanasia is still developing and arguments involved in this issue are leaning more towards legalizing euthanasia day after day. 2.0 Body of Content 2.1 Euthanasia is a Freedom of Choice: Just as I shall select my ship when I am about to go on a voyage, or my house when I propose to take a residence, so I shall choose my death when I am about to depart from life. Lucius Annaeus Seneca (Roman Stoic Philosopher, orator and statesman) Every person was born free and has the right to die free with his own will. Moreover, dignified death is one of the fundamental rights people are supposed to have as being part and parcel of the fundamental rights to life. According to the Daily Telegraph (2008), Chantal Sebire was a French woman diagnosed with a rare type of cancer (a malignant neoplasm of the nasal vault) in 2002, her tumor developed and reached a point where it cannot be stopped or cured, it made sever deformities in Sebires face taking away the senses of smelling, tasting and eventually sight from her. Moreover, Sebire was suffering from horrible pain; she said a normal human would not allow an animal to go through. She appealed to the French court asking for a permission to have an assisted suicide as she could not bear the pain anymore. However, her appeal was rejected as euthanasia is not legal in France. Two days later Chantal Sebire was found dead as she committed suicide in her house after her appeal was re jected. It can be seen from that case that this way of crossing into death was unfair as it was more scary and painful experience than a regulated euthanasia. In Sebires case as well as other similar cases where cure is not found for patients, patients are going through horrible pain and they are going to die anyway, governments should not stand in the way of those severely ill people with no cure or treatment to end their lives legally by assisted suicide. Therefore, the life of those patients is their choice and they have the right to continue living or die peacefully. The pursuit of happiness is the pursuit of relief from pain and suffering. (Cockeram, 2007). By having the superior power in keeping the lives of patients with no chance in getting well or having cure is not giving them relief or even happiness, it is like holding their lives, watching them suffer at the last moments of their lives and locking them in a life they are not having since they have lost their senses, feelings or even conscious. Opponents of euthanasia argue that euthanasia is a cruel act and a human enforcement to end other people lives without their permission. Lozano mentioned that the Vatican believes that ending lives of severely ill people even the premature babies who are gravely ill by euthanasia is an illicit act as well as act of cruelty. (CNA, 2006). Therefore, opponents believe that euthanasia would violate the Gods gift of life and enforce in ending lives of people who are not able to communicate with others. However, according to the article 3 of the Universal Declaration of Human Rights, Everyone has the right to life, liberty and security of person. Article 5 adds No one shall be subjected to torture or to cruel, inhuman or degrading treatment or punishment. If no one shall be subjected to torture, then why do we have to watch them suffer? (Euthanasia UK, 2007). Most of the people who choose euthanasia are those patients who suffer from diseases that cause a lot of pain and cannot be treated. If those patients choose not to bear the pain, they should have the right to do so. (Bose, 2011). It is the right of those patients with no cure to choose their own life and death. Other kind of brain dead patients who are in irreversible coma and cannot communicate have no chances in getting back to life or even getting well as in most cases their brains are damaged, it is in their favor as well as other patients favors with chances in getting cured to be euthanized, providing them with the mercy killing doesnt mean it is a cruel act against their will but it is ending their suffer, releasing their locked souls in no life and lowering the expenses their parents or relatives have to pay for hospitals only for keeping them alive but unconscious through machines. Euthanizing such patients can be by shortening the amount of oxygen or food given to them through machines and tubes. Therefore, euthanasia should not be considered as a cruel act but an act of mercy that gives patients and even their parents the right to choose life or death at the time of suffer and inevitable death.

Censorship in Literature and Music Essay -- Censoring Laws Essays

Censorship in Literature and Music What is censorship? An encyclopedia defines censorship as "the control of what people may say or hear, write or read, or see or do1." There are many reasons why people censor entertainment such as literature and music. Many governments or groups try to preserve their standards of morality by preventing people from learning about or following other standards2, commonly found in the two previously mentioned mediums. There are different ways to censor things. It can be on a local level, such as libraries refusing to carry a controversial book. It can also be on an entirely larger scale. In the 1770’s, French author Beaumarchais had two of his plays, The Barber of Seville and The Marriage of Figaro, banned by King Louis XVI.2 Back then, these plays were considered outrageous and sometimes blasphemous. To fully understand how our system of censorship works today, we have to look into history to see how censorship got started. Johann Gutenberg invented the moveable type printing press and published his first bible sometime around 1450. By 1500, an estimated 20 million books were circulating throughout Europe. The Church's monopoly over the written word was destroyed. Responding to this new technology of freedom, the Church developed a number of control mechanisms. The most obvious, and perhaps famous of these was the Index librorum prohibitum (Index of Prohibited Books). The Index listed hundreds of banned authors and books.3 Less famous than the Index but absolutely central to the rise of censorship, was Church and government use of exclusive printing privileges, which granted favored printers a monopoly right to publish books so long as they were approved by official censors4. In Italy, censorship was primarily a response to the spread of printed Protestant propaganda.5 On October 31, 1517, the Day of all Saints, Martin Luther posted his 95 statements, of theses, to the doors of a church. This was revolutionary, because nobody ever successfully questioned the Church’s authority. Soon after that, others began to follow his example. Eventually, the Church lost power over people’s daily lives. Their local and national government took its place as the authority leader. As the government’s grew, it eventually started monitoring what the public could be exposed to. This included music and books. Eventually, in the 20th c... ... Bibliography Webpage Hunter, Christopher D.. "Copyright and Culture" 03 Dec. 2002 "An Interview with Alex Domokos" Dowse 03 Dec. 2002 http://www.dowse.com/interview-alex-domokos.html Nuzum, Eric. "A Brief History of Banned Music in the United States" 03 Dec. 2002 http://ericnuzum.com/banned/ "Controversial Music: The Beat Goes On" 03 Dec. 2002 http://teenmusic.about.com/library/weekly/aa022301a.htm "The 100 Most Frequently Challenged Books of 1990-2000" American Library Association. 03 Dec. 2002 http://www.ala.org/bbooks/top100bannedbooks.html Blume, Judy. "Judy Blume Talks about Censorship" 09 Dec. 2002 http://www.judyblume.com/censors.html "Censorship in the Renaissance" 13 Dec. 2002 http://130.238.50.3/ilmh/Ren/bokt-censor.htm "Constitutional Amendments 1-10: The Bill of Rights " 02 Jan. 2003 http://www.archives.gov/exhibit_hall/charters_of_freedom/bill_of_rights/amendments_1-10.html Anne, Rapin. "Beaumarchais" 31 Jan. 2003 Books Marsh, Dave. 50 Ways to Fight Censorship & Important Facts to Know About the Censors. New York, NY: Thunder's Mouth Press, 1991. Encyclopedia "Censorship." The World Book Encyclopedia. 1989 ed.

Work Ethics

According to Webster’s Dictionary â€Å"work ethics is a belief in work as a moral good.† Which is basically saying you do your work because you want to, not because you have to, and maybe get noticed for it? Some people work because they have to but if you have work ethic for what you work for it’s because you are doing it because you like and thinks it is a good job. If you do job just because then it is not considered a good work ethic, it is considered a work ethic but you only do the job because you have to do it.Those with a good work ethic often also possess generally strong character. This means they are self-disciplined, pushing themselves to complete work tasks instead of requiring others to intervene. They are also often very honest and trustworthy, as they view these traits as befitting the high-quality employees they seek to become, to demonstrate their strong character, these workers embody these positive traits daily, likely distinguishing themselv es from the rest.I have a work ethic. My work ethic is working in the library at my high school. I work in the library because I have to. I don’t like to work in the library but I do because it is a good work ethic. I work here because they need help.Many students leave school ill-prepared for the workplace. Poor academic skills and work habits limit their understanding of how they might fit into the adult world. Work-based learning addresses this problem by extending the walls of the classroom to include the whole community, giving students real world experiences and opportunities to apply academic skills in the workplace. Work-based learning is an integral part of school to careers transition, combining school-based learning and work-based learning into an integrated experience for all students.Through work-based learning, â€Å"Employers reinforce academic lessons, schools emphasize career applications, students gain experience in the adult world of work and connections t o a range of post-secondary options, including college, technical training and skilled entry level work.† The National Center for Career and Technical Education (NCCTE) defines career development as â€Å"the total constellation of psychological, sociological, education, physical, economic, and chance factors that combine to influence the nature and significance of work in the total lifespan of any given individual.†Work-based learning is defined as a coherent sequence of job training and work experience that involves actual work experience and connects classroom learning to work activities. One of the key elements that lead to the success of a school to careers system is work-based learning. Students must have access to a range of developmentally appropriate work-based learning experiences. Schools and employers need flexibility to develop a school to careers transition that builds on local strengths and is tailored to local needs and circumstances.The work-based compo nent may include a variety of activities including job shadowing, school based enterprises, entrepreneurial programs, dual enrollment, mentorships, career pathways, and service learning to name a few. Using a range of in-school and out-of-school strategies – paid or unpaid work experiences during the school day or after school – with programs customized to fit the needs of young people, school, businesses, and the local community, the main focus of any of these work-based learning experiences is that they must offer academic study, professional/technical skills, and work related experiences.Although most people have wanted to concentrate their efforts related to work-based learning on students in the upper years of high school, they should realize that programs that do not start until the 11th grade miss the chance to make a significant impact on many students. Work-based experiences need to take a progressive sequential approach that includes preparation (feeder) expe riences starting as early as elementary or middle school.It is crucial to include younger students before they become discouraged and disengaged or drop out of school altogether. â€Å"Feeder† experiences expose young people to a range of career opportunities through such options as summer internships, job shadowing, and career exploration workshops, all of which are geared to the connection between school and work and the integration of academic and occupational training. Ideally the work-based learning component is delivered through a planned program of job training and other employment experiences related to a chosen career.

Behavior Around Earnings Announcement Events For Emerging Markets - Free Essay Example

Sample details Pages: 17 Words: 5216 Downloads: 9 Date added: 2017/06/26 Category Statistics Essay Did you like this example? 1. INTRODUCTION 1.1 Background of Research Question Stock prices show a tendency to behave in a manner not consistent with what current finance theory proposes or expects. This gap may be the result of flawed assumptions presently used as a basis in existing theory. Don’t waste time! Our writers will create an original "Behavior Around Earnings Announcement Events For Emerging Markets" essay for you Create order Conventional modern finance theory rests on the assumption of investor rationality. Efficient market hypothesis (EMH), representing an integral part of conventional finance theory, further assumes security prices reflect (to varying degrees) all available information as processed by rational investors. Modern portfolio theory (MPT) suggests[1], a stockà ¢Ã¢â€š ¬Ã¢â€ž ¢s price represents the present value of future expected cash flows and therefore, is dependant on investorà ¢Ã¢â€š ¬Ã¢â€ž ¢s expectations of estimated forecasts of earnings growth rates into the future. Actual short-term stock price behavior indicates anomalies and significant divergences of prices away from fundamental intrinsic values, long-term averages, or expectations as implied by MPT. Behavior finance takes the approach of understanding these price anomalies through a study of cognitive and emotional biases. This thesis investigates the possibility of investorà ¢Ã¢â€š ¬Ã¢â€ž ¢s making mistakes (through irratio nal behavior) in their forward expectations of future corporate cash flows, resulting in short-term overreaction to earnings information releases due to influence of representative bias (a cognitive bias). This proposal attempts to examine the existence, relationship and, impact of overreaction as a determinant of securities price behavior in emerging markets of Far East. The aim of this study is to discover representative bias, a tendency of investors to overweigh most recent information in making future forecasts, as one possible cause leading to overreaction in securities prices. This thesis tests investor responses to corporate earnings announcements, specifically surprises, to determine overreaction behavior and to identify representative bias as the cause of such overreaction. The results may contribute, by offering a missing piece of the puzzle, of understanding stock price behavior (towards the search for a unified theory), into existing research work for behavior finance. In addition, a better understanding of what drives stock prices would be a highly useful forecasting and policy tool for participants concerned with asset pricing. 1.2 Motivation for Research The field of behavior finance focuses on the question; what drives investor behavior?. It is divided into two main groups. Cognitive and emotional biases, which are further sub-divided into two sub-groups, individual and collective biases. Behavior finance has been seeking to discover the causes of investor irrationality within the investment decision-making framework. Significant empirical and theoretical studies have been conducted, which suggest cognitive and emotional biases affect investor rationality. Indeed, the field of behavior finance directly challenges the conventional finance framework, which uses within its paradigm the assumption that investors are rational decision makers, and securities prices reflect all available information (EMH[4]. According to Fung (2006), it seems clear that EMH and CAPM[5](pillars of the current financial theory), despite mounting evidence against their validity, remain widely in use. One reason for this is the fact that these models cannot be empirically falsified due to their dependence on layers of assumptions, which support each other. The other reason is a lack of an alternative asset-pricing model taking cognitive biases into consideration, which does not exist so far. Behavior finance offers just such an alternative, and after observing price anomalies as a trader in the financial markets, I have become interested in pursuing empirical work in this direction to understand and discover a better way to price financial assets. Furthermore, only limited research exists for capital markets covered by this thesis namely Malaysia, Thailand, and Singapore, despite the fact these markets have outperformed western markets recently and continue to offer potential for future growth. 1.3 Statement of Problem This thesis focuses on the following problem: à ¢Ã¢â€š ¬Ã…“What is the relationship between representative bias and overreaction, as it relates to individual as well as a series of earnings surprise announcements, on investor behavior in the stock markets of Far East?[6]à ¢Ã¢â€š ¬? 1.4 Research Questions Objectives This proposal consists of three components. First, the study aims to discover existence of investor overreaction (derived and tested from stock price behavior) in the stock markets of Malaysia, Thailand, and Singapore based on overreaction hypothesis (ORH) as proposed by Thaler (1985). Thereafter, the second objective of this thesis is to test for overreaction in response to corporate earnings surprises (positive and negative). Third and last objective of the study is to determine if representative bias (cognitive bias) is a source of this investor irrationality, in response to earnings surprises, as demonstrated through price behavior in the respective stock markets. In other words, to contribute an answer to the key question behavior finance is seeking; what drives investor behavior in the stock markets?, this study tests emerging stock markets of far east for investor overreaction. Subsequently, this thesis focuses on representative bias as one cognitive attribute of investor behavior, which may cause overreaction to occur. Following are some of the research questions this study will attempt to answer. Research Questions: This study attempts to answer the following research questions: Research Question 1: Does investor overreaction exist in emerging stock markets? Research Question 2: Do investors overreact to positive earnings surprises? Research Question 3: Do investors overreact to negative earnings surprises? Research Question 4: Is representative bias present during investor overreaction to earnings surprises? Research Question 5: Does representative bias cause investor overreaction when earnings surprises are positive? Research Question 6: Does representative bias cause investor overreaction when earnings surprises are negative? Research Question 7: What is the relationship between representative bias and overreaction as it relates to a series of earnings surprise announcements, both positive and negative? 1.5 Research Significance The motivation behind this research is to contribute a better understanding of determinants of stock pricing in the context of investor decision making. The results of this study will be useful in furthering current empirical research on cognitive biases affecting stock price behavior, specifically investor overreaction and representative heuristic, (as it relates to earnings surprises), as well as provide useful understanding for investment decision making, forecasting, and policy making for financial market participants in the asset management field. In addition, contribution towards a piece of the stock-pricing puzzle, as well as further research questions may also be discovered. 2. LITERATURE REVIEW 2.1 Cognitive Biases Overreaction Hypothesis (ORH) Behavior finance seeks to understand effects of psychology on financial behavior. However, a unified model, which can replace the conventional mainstream models such as the efficient market hypothesis and CAPM (capital asset pricing model), has yet to be discovered, Fung (2006). Keynes (1973, original publication 1936) wrote in his General Theory: à ¢Ã¢â€š ¬Ã…“ Day-to-day fluctuations in the profits of existing investments tend to have an altogether excessive, and even absurd, influence on the marketà ¢Ã¢â€š ¬? (1973, pp. 153-154). This became the starting point for a study on overreaction by De Bondt and Thaler (Fung 2006, p.29). Keynes remark infact implied the possibility of systematic mispricing of securities by investors. Indeed, this argument led to directly challenging the efficient market hypothesis, a mainstream concept used for financial asset pricing invented by Fama (1970). Fama created an empirically testable model to price securities based on this concept called the à ¢Ã¢â€š ¬Ã…“fair gameà ¢Ã¢â€š ¬? which implied the notion that markets cannot have expected returns in excess of equilibrium expected returns (ibid., p.385). This particular notion is questioned in De Bondt and Thalerà ¢Ã¢â€š ¬Ã¢â€ž ¢s, 1985 paper, proposing instead the stock market overreaction hypothesis (ORH). They suggest, stock prices fluctuate from their intrinsic values (PV of expected future cash flows of the firm) due to optimism and pessimism prevailing amongst investors. In addition, De Bondt and Thaler (1985) suggeste d two other hypotheses: 1. à ¢Ã¢â€š ¬Ã…“Extreme movements in stock prices will be followed by subsequent price movements in the opposite directionà ¢Ã¢â€š ¬? 2. à ¢Ã¢â€š ¬Ã…“The more extreme the initial price movement, the greater will be the subsequent adjustment. Both hypotheses imply a violation of weak form market efficiencyà ¢Ã¢â€š ¬? (1985 p.795). In other words, their hypothesis suggested the existence of the possibility of earning excess returns above equilibrium returns. This may be accomplished by investing in stocks, which have performed poorly relative to the average (a new contrarian strategy). In addition to casting a doubt on the EMH, De Bondt and Thalerà ¢Ã¢â€š ¬Ã¢â€ž ¢s paper (1985) also brought into question the validity of the CAPM (Fung, 2006). According to CAPM, an assets return is a function of the asset risk premium and the risk free rate. In other words, higher the systematic risk (Beta) of an asset, higher the assets return (represented by a linear relationship between risk and return). However, the findings of De Bondt and Thaler discovered low Beta (low systematic risk) portfolios (L) generating higher returns and high beta portfolios (W) producing low returns. This result contradicts the basic risk-return relationship as proposed by CAPM. Fama and French in their 1992 paper were the first to confirm this CAPM contradiction through empirical study based on discovering the (positive) relationship between size and beta. They suggested CAPM did not fully measure and adjust for the higher risk of smaller(size) firms. In summary, empirical research has discovered that consistent anomalies exist between risk-return relationship as proposed by the widely used EMH and CAPM (the traditional financial theory paradigm). However, no comprehensive theory exists to explain why some stocks do better than others. Lakonishok et al. (1994) confirm in their paper that value stocks outperform glamour stocks. Thereby they suggest cognitive bias as a possible explanation for this anomaly. à ¢Ã¢â€š ¬Ã…“Putting excessive weight on recent past history, as opposed to a rational prior, is a common judgment error in psychological experiments and not just in the stock marketà ¢Ã¢â€š ¬? (ibid., p.1575). It seems clear that EMH and CAPM (pillars of the current financial theory), despite mounting evidence against their validity, remain widely in use. Behavior finance seems to offer an alternative to the current financial theory, and therefore it is imperative to pursue empirical work in this direction in order to understand and discover a better way to price financial assets. 2.2 Investor Overreaction Earnings Surprises Research in Behavior Finance has taken different approaches to discovering cognitive biases and their impact on asset pricing. Odean (1998) and Daniel, Hirshleifer and Subrahmanyam (1998) focused on overconfidence, while Hong and Stein (1999) investigated mispricing by positive feedback trading. De Bondt and Thaler (1985) have empirically investigated overreaction, and concluded investors overreact to information. Their research focused on NYSE stocks and cumulative returns for three years (event window). They concluded specifically that stocks with previous abnormally low returns performed better than those with previous abnormally high returns. This return reversal indicated investor reaction to be over-weighted in response to information, which was later corrected in prices over the longer term. The overreaction anomaly has been empirically established in finance through multiple studies. Kaestner (2006) points out that Poteshman (2001) tests overreaction in the options market; Cutler, Poterba, and Summers (1991) for gold market; Chui, Titman and Wei, (2000) and Bhojraj and Swaminathan (2001) for international stock markets. In addition Chopra, Lakonishok, and Riter (1992) as well as De Bondt and Thaler (1987) have confirmed overreaction in stock markets. The main gap, which remains in empirical research, is establishing the determinant or driver of overreaction (Kaestner 2006, pp.3). This particular cognitive bias has recently been tested as a driver of overreaction in a few markets only. Kaestner (2006) further points out that Poteshman (2001) has researched for representative bias and overreaction in the options markets through investor responses to changes in variance of the underlying asset. De Bondt and Thaler, (1985 and 1987), Chopra, Lakonishok, and Ritter, (1992) look at representative bias as the potential driver of overreaction basing their studies on current earnings, forecasted changes in earnings and past performance without directly testing for it. Kaestner (2006) directly tests for this link between representative bias and overreaction for NYSE stocks related to earnings surprises. 2.3 The Representative Heuristic Representative heuristic belongs to the family of cognitive biases within the field of behavior finance. Tversky and Kahneman (1974) suggest this bias may affect an investorà ¢Ã¢â€š ¬Ã¢â€ž ¢s decision-making framework by causing the investor to over weight most recent fundamental information regarding a stock while making estimations of future earnings forecasts. Because of representative bias, investors may over/under estimate a stockà ¢Ã¢â€š ¬Ã¢â€ž ¢s intrinsic value[7]resulting in subsequent anomalous security price behavior. According to Tversky and Kahneman (1974), representative bias involves estimating: à ¢Ã¢â€š ¬Ã…“ the probability of an uncertain event, or a sample, by the degree to which it is similar in its essential properties to the parentsà ¢Ã¢â€š ¬Ã¢â€ž ¢ populationà ¢Ã¢â€š ¬? (ibid.). In other words, investors place too much weight on recent small sample datasets (law of small numbers) when projecting future cash flows for a stock. Kaestner (2006) suggests that when a series of such recent earnings data (in the same direction) is presented to the investor, this series is interpreted as a pattern and thereby becomes the basis for future projections of a stockà ¢Ã¢â€š ¬Ã¢â€ž ¢s performance. Such projection thus leads to over/under estimation of future projected probability distributions of expected price performance. Kaestner (2006) argues that if representative bias affects investors then evidence of two related phenomenon would be present. à ¢Ã¢â€š ¬Ã…“First, statistical results would indicate a marketà ¢Ã¢â€š ¬Ã¢â€ž ¢s overreaction to some disclosed information. Second, the overreaction will be increasing in the extent to which the series of similar information is longà ¢Ã¢â€š ¬? (ibid., pp.10). Although De Bondt and Thaler (1987 and 1990) only tested for overreaction in their paper, they do mention the representative bias as a possible reason. Poteshman (2001) tests for representative bias and relates it to overreaction in the options markets. However, Kaestner (2006) for the first time directly tests the representative heuristic and links it to overreaction in the U.S. stock markets. 3. METHODOLOGY 3.1 Methodology Overview The literature has identified a variety of research methodologies, used to test for cognitive biases. This proposal extends the methodology of testing for overreaction used by De Bondt and Thaler (1985) for U.S markets, to Asian markets. Subsequently, the main focus of this study, à ¢Ã¢â€š ¬Ã…“representative biasà ¢Ã¢â€š ¬? as a driver of investor overreaction in response to earnings surprises is tested based on Kaestnerà ¢Ã¢â€š ¬Ã¢â€ž ¢s paper (2006) which also only explored the US stock markets. De Bondt and Thaler (1985) tested the overreaction hypothesis by constructing winner(W) and loser (L) portfolios of NYSE stocks. These were selected based on past three yearà ¢Ã¢â€š ¬Ã¢â€ž ¢s residual stock performance, which was, defined as monthly returns minus monthly market returns. The hypothesis test was run for the period January 1926 through December 1982. Positive results constituted the (W) and negative results the (L) portfolios. The two residual return results of prior and post three-year event periods (total of six years) were tested using the cumulative residual returns (CRS), a sum of the post formation monthly residual returns over three years. CRS was calculated for all sets of portfolios from 1930-1982 and each portfolioà ¢Ã¢â€š ¬Ã¢â€ž ¢s component residual returns were averaged to determine the cumulative average residual returns (CARS). Average of the CARS was calculated as the next step for all (L) and (W) sets of portfolios. CAPM was used in determining the ma rket portfolio return. Overreaction hypothesis suggested that (L) portfolios would perform better than market and vice versa. Results supported the hypothesis. This finding directly challenged the EMH implicit assumption that arbitrage gaps are filled rapidly by rational investors, since markets find an equilibrium price where excess returns are not possible and occur only as a result of luck. In essence, De Bondt and Thaler (1985) discovered the possibility of earning better than market returns. 3.2 Sample Data Characteristics Data for stocks will be acquired for Malaysia, Thailand, and Singapore stock exchanges directly, and/or from other information providers for the period 1990-2008. A total of eighteen years will cover three event windows and two economic cycles. This would also include the 1990 U.S. recession, 1997 Asian crises, and current 2008 global market fallout data. Daily stock prices in the form of open, high, low, and close would be used to determine daily returns and same data set would be required for the market index to get a proxy for market returns. In addition, quarterly EPS (earnings per share), earnings announcements by the company, and earnings consensus estimates of analysts would be gathered. For time line analysis, EPS estimates and actual EPS values will need to be obtained for preceding 4 quarters. To adjust for size, company capitalization data (shares x stock price) will be recorded at the beginning of each year in the sample set through the companyà ¢Ã¢â€š ¬Ã¢â€ž ¢s financia l statements. In addition, Beta estimates for all companies would also be required for the sample period. Most of this data is available in digital form through research sources including online internet subscriptions and the stock exchange itself. Stock selection criteria are detailed in the following sections. 3.3 Dependant and Independent Variables Dependant variable is the excess return derived as the difference between stock raw daily return and market daily return as an indicator of investor reaction. Independent variable is the data of actual earnings surprises in relation to analystà ¢Ã¢â€š ¬Ã¢â€ž ¢s estimates. 3.4 Overreaction Test Overreaction will be tested using sort-ranking procedure, as used by De Bondt and Thaler (1985) and repeated by Kaestner (2006) based on past stock return performance. Stocks will be ranked according to their past performance over three year event windows as a proxy for prior information and will be included into portfolios based on this performance. Post-formation performance will be assessed to test the ORH. 3.5 Portfolio Construction Portfolios of best performing (W) and worst performing (L) stocks will be constructed using past performance as a criteria. Each stockà ¢Ã¢â€š ¬Ã¢â€ž ¢s historical monthly closing prices will be used to determine past performance over three-year event windows. The monthly return (Rist ) for stock i, based on its monthly closing stock price minus the monthly market return (Rmt ) derived from the closing market index price (for the same period) will generate the excess return (ARiet ) for stock i. ARiet = Rist Rmt This excess return (ARiet) will be defined as a performance measure for stock i, at time t, where the à ¢Ã¢â€š ¬Ã…“eà ¢Ã¢â€š ¬? represents excess and à ¢Ã¢â€š ¬Ã…“sà ¢Ã¢â€š ¬? represents stock specific return. Positive excess return (+ARiet) stocks would indicate best performers (W) while negative excess return (-ARiet) stocks would become part of the worst performers (L). The next step will calculate the cumulative excess (abnormal return denoted by AR) returns (CARi(p,q)) by summing the monthly returns for the 36 month formation event window for all stocks. q CARi(p,q) = à ¢Ã‹â€ Ã¢â‚¬Ëœ ARiet t=p p = time at beginning of event window (t=0) q = time at end of event window (t=3 yrs) Thereafter, stocks will be ranked from high to low, based on their cumulative excess returns of previous three years (formation period), and subsequently be added to either the best performers (W) portfolio or the worst performers (L) portfolio. To provide for progressive multiple tests, this portfolio formation process will be repeated for all non-overlapping event windows covering the entire test period with new portfolios formed every 3 years to be tested against their performance in the post-formation 3-year periods. Overreaction hypothesis suggests that (L) stocks should perform better than the market in the subsequent three-year post-formation event window as compared with the previous three years, whereas the reverse should be true for (W) stocks. This implies (L) stocks should have positive excess returns (+ARiet) while (W) stocks should generate negative excess returns (-ARiet) in the post event three-year period. A total of six years data will be required for the stocks und er consideration. For the market return (Rmt), an equally weighted monthly arithmetic average of stock returns in the sample will be used as proxy for market return (De Bondt and Thaler 1985). For each (W) and (L) portfolio, cumulative excess return (CARi(p,q)) for three-year post-formation event window will be calculated and repeated for all sets of portfolios. Finally, a mean of all member stock CARi(p,q)à ¢Ã¢â€š ¬Ã¢â€ž ¢s in each portfolio will be computed and referred to as MCARi(p,t). Thus, two MCARi(p,t)à ¢Ã¢â€š ¬Ã¢â€ž ¢s will be obtained for each of these portfolios during the formation period of 36 months, and this will be repeated for all progressive portfolios over different event windows of the entire test period. In summary, CARi(p,q) and MCARi(p,t) will be calculated for the post-formation 3-year event window to test with formation event portfolios. If overreaction exists, then it is expected worst performing portfolios (L) should generate positive excess returns and vice versa for the best performing portfolios (W), i.e. negative excess returns. This return reversal would imply initial prices had overshot rational values (investor overreaction) as a short-term reaction and therefore adjusted back to rational values subsequently. Here, W-MCARi(b,t) = best performer portfolios L-MCARi(w,t) = worst performer portfolios The overreaction hypothesis (De Bondt and Thaler, 1985) expects post-formation results as follows: W-MCARi(b,t) 0 L-MCARi(w,t) 0 Such that, L-MCARi(w,t) à ¢Ã¢â€š ¬Ã¢â‚¬Å" W-MCARi(b,t) 0 3.6 Overreaction Due to Earnings Surprise Events Test Overreaction in response to earnings surprises will be tested based on sort-rank for single event and selection-ranking event method, for series analysis, as used by Kaestner (2006). The study will use portfolio-study approach as proposed by Ball and Brown (1968). Portfolios will be constructed based on stock earnings announcement events, ranked on highest to lowest surprises, for stocks in the sample. Highest earnings positive surprise stocks will be added to portfolio (+Ä ¤p ) and highest negative surprise stocks will constitute portfolio (-Ä ¤n ). Standardized Earnings Surprise criteria Quarterly earnings surprises will be computed based on standardized surprise earnings (Ã…Å ¾SÄ–q) represented by the difference between the actual released earnings (AÄ–q) and the one month prior to announcement consensus estimate for earnings (EÄ–q), scaled by the standard deviation of the individual estimates for each stock (à Ã†â€™est-q). Therefore, quarterly standardized surprise would be as follows: Standardized Surprise measurement: Ã…Å ¾SÄ–q = (AÄ–q EÄ–q) / à Ã†â€™est-q Excess Returns Measurement Excess returns (ARiet) for each stock will be computed on daily basis using closing stock prices as described in section 3.5. These returns will use size-adjusted approach[8](Kaestner 2006) and will be calculated as the difference between stock daily return (Rist ) and the equally weighted daily return of the stockà ¢Ã¢â€š ¬Ã¢â€ž ¢s own portfolio (Rmt ). Thus excess return will be, as explained earlier in section 3.5: ARiet = Rist Rmt Thereafter, post-formation cumulative excess returns (CARi(p,q) ) will be derived based on event windows of 0 (0 represents the announcement date) to 1, 3, 10, 30 and 60 days for each portfolio Ä ¤p and Ä ¤n . The cumulative excess return for an event window will be: q CARi(p,q) = à ¢Ã‹â€ Ã¢â‚¬Ëœ ARiet t=p where; q= 1,3,10,30 or 60 day post-formation event window Overreaction to earning surprises expects stock prices to overshoot in the direction of the surprise, represented as follows: Positive cumulative excess returns for positive surprise portfolios and vice versa. CARi(p,q) 0 for +Ä ¤p CARi(p,q) 0 for -Ä ¤n Such that, Positive surprises (+Ã…Å ¾SÄ–q 0): CARi(p,q) 0 Negative surprises(-Ã…Å ¾SÄ–q 0): CARi(p,q) 0 Null Surprises ( Ã…Å ¾SÄ–q = 0): CARi(p,q) = 0 3.7 Representative Bias a Driver of Overreaction to Earnings Surprises According to Kaestner (2006), two expected phenomenon should be present, if representative bias plays a role in investorsà ¢Ã¢â€š ¬Ã¢â€ž ¢ overreaction to earnings surprises. First, a confirmation of overreaction to released information events, as tested earlier in this thesis, must exist. Second, this overreaction phenomenon should be increasing in relation to a similar series of earnings surprises over consecutive event windows[10]of future expected cash flows). This tendency to misjudge future prospects of a company causes investors to overreact in response to earnings surprises (both positive and negative), and would be reflected through securities prices. As a companyà ¢Ã¢â€š ¬Ã¢â€ž ¢s actual earnings information is released, investors would have to readjust their expectations (if they had overreacted) of cash flows based on the new real (actual) information and therefore the stock price must correct itself over time, if overreaction had occurred in the first instance. Representative bias causing overreaction due to earnings surprises will be tested by measuring investor reaction to a series of same sign surprises (Kaestner 2006). A non-parametric significance test will be used as proposed by Foster, Olsen, and Shevlin (1984) and later reviewed by Lyon, Barber, and Tsai (1999)[11]. This test relaxes the assumption of normality, constant variance of security returns over time, and cross-sectional independence in residuals (Kaestner 2006). The test focuses on establishing a companion empirical sample distribution, and then comparing its cumulative excess return (CARi(p,q)) with the observed CARi(p,q) to assess for statistical significance. Empirical distribution is generated by randomly selecting one event in the parent population for each event, computing equally weighted CARi(p,q)à ¢Ã¢â€š ¬Ã¢â€ž ¢s for the companion sample, and ranking the companion sample CARi(p,q)à ¢Ã¢â€š ¬Ã¢â€ž ¢s from highest to lowest based on a repetition of the first two s teps 2,500 times in order to obtain the empirical distribution. If representative bias exists and investors rely heavily on most recent earnings surprise information, then in the event of positive surprises, they will project overly optimistic future surprises and overreact immediately after the event on the positive side, subsequently reversing the price direction upon next earnings release (should the actual surprise not meet their expected higher surprise, (Ã…Å ¾SÄ–q =0). To test a series of events of earnings surprises, the sequential sort-ranking procedure will be used (Kaestner 2006). The initial portfolio is constructed for all individual null-surprise event stocks (Ã…Å ¾SÄ–q,t=0) and then ranked based on their most recent surprise events (Ã…Å ¾SÄ–q,t-1) relative to this null-surprise, as compared with the stockà ¢Ã¢â€š ¬Ã¢â€ž ¢s last surprise (t-1). In fact all subsequent sorting will be in relation to a starting most recent null-surprise (Ã…Å ¾SÄ–q =0), so that post null-surprise period can be studied. These rankings are used to construct three equal sized portfolios labeled as positive, neutral, and negative (Ã…Å ¾SÄ–q,t-1) surprise portfolios. These three portfolios are then each sub-divided into three more portfolios based on rankings of stock surprises at (Ã…Å ¾SÄ–q,t-2) which means 2 quarters behind in a series. Similarly, these are sub-divided into three more portf olios at t-3, and t-4, giving rise to a total of 5 quarters backwards surprise series-related portfolios. In this way, series of similar earnings surprises in sequence are separated for testing while keeping the current surprise at zero (null). For each family of series of portfolios (including one null surprise) which gives rise to different number of stocks in the sample, CARi(p,q)à ¢Ã¢â€š ¬Ã¢â€ž ¢s are computed for different event windows (i.e 0;1, 0;3, 0;30 and 0;60). This will allow for a study of the impact of similar preceding surprises on overreaction to the most recent earnings surprise. Portfolios will be tested for investor reactions for four event windows. Representativeness hypothesis (Kaestner 2006), suggests investors will overreact initially to a surprise due to overweighting of this information in future projections. And in the case of a series of such surprises, the overweighing of future expectation will be stronger, giving rise to greater overreaction. However, when the actual earnings number results in no surprise, investors readjust their view in the opposite direction, causing a correction to the initial overreaction. This hypothesis suggests then, that a correction will ensue post the current null-surprise event causing CARi(p,q)à ¢Ã¢â€š ¬Ã¢â€ž ¢s to be of the opposite sign of the surprise and also, this reversal should be stronger for longer series of such similar surprises as opposed to shorter series, implying that investors overreaction is affected by the representative bias. Thus, for positive surprises, CARi(p,q)à ¢Ã¢â€š ¬Ã¢â€ž ¢s should be negative in relation to the empirical distribution generated by the null-surprise sample portfolio and positive for the negative surprise portfolios. For the empirical distribution sample and null-past surprise series, the CARi(p,q) should be = 0. 3.8 Empirical Results Statistical Significance Representativeness hypothesis expects: Non-surprise portfolios: Where  : (Ã…Å ¾SÄ–q = 0) CARi(p,q) = 0 (no significant market reaction) For positive surprise portfolio: (Ã…Å ¾SÄ–q 0) CARi(p,q) 0 (negative subsequent post event correction beyond null-surprise) For negative surprise portfolio: (Ã…Å ¾SÄ–q 0) CARi(p,q) 0 (positive subsequent post event correction beyond null-surprise) Such that: For positive surprise portfolio: (Ã…Å ¾SÄ–q 0) CARi(p,q-3) CARi(p,q-2) CARi(p,q-1) CARi(p,q) For negative surprise portfolio: (Ã…Å ¾SÄ–q 0) CARi(p,q-3) CARi(p,q-2) CARi(p,q-1) CARi(p,q) 3.9 Summary of Research Objectives Note: details of methodology appear in relevant sections above. This section indicates only a summary of already discussed objectives for ease of reading. Objective 1: Does investor overreaction exist in emerging stock markets? W-MCARi(b,t) 0 L-MCARi(w,t) 0 Such that, L-MCARi(w,t) à ¢Ã¢â€š ¬Ã¢â‚¬Å" W-MCARi(b,t) 0 Objective 2: Do investors overreact to earnings surprises? Null Surprises: (Ã…Å ¾SÄ–q =0) q CARi(p,q) = à ¢Ã‹â€ Ã¢â‚¬Ëœ ARiet = 0 t=p Objective 3: Do investors overreact to positive/negative earnings surprises? Positive surprises: (+Ã…Å ¾SÄ–q 0) q CARi(p,q) = à ¢Ã‹â€ Ã¢â‚¬Ëœ ARiet 0 t=p Negative surprises: (-Ã…Å ¾SÄ–q 0) q CARi(p,q) = à ¢Ã‹â€ Ã¢â‚¬Ëœ ARiet 0 t=p Objective 4: What is the relationship between representative bias and over- reaction as it relates to earnings surprises? Is representative bias present during overreaction? Non-surprise portfolios: (Ã…Å ¾SÄ–q = 0) q CARi(p,q) = à ¢Ã‹â€ Ã¢â‚¬Ëœ ARiet = 0 t=p (No significant market reaction) Objective 5: Does representative bias cause overreaction when earnings surprises are positive? For positive surprise portfolio: (Ã…Å ¾SÄ–q 0) q CARi(p,q) = à ¢Ã‹â€ Ã¢â‚¬Ëœ ARiet 0 t=p (Negative subsequent post event correction beyond null-surprise) Objective 6: Does representative bias cause overreaction when earnings surprises are negative? For negative surprise portfolio: (Ã…Å ¾SÄ–q 0) q CARi(p,q) = à ¢Ã‹â€ Ã¢â‚¬Ëœ ARiet 0 t=p (Positive subsequent post event correction beyond null-surprise) Objective 7: What is the relationship between representative bias and overreaction as it relates to a series of earnings surprise announcements, both positive and negative? For positive surprise portfolio: (Ã…Å ¾SÄ–q 0) CARi(p,q-3) CARi(p,q-2) CARi(p,q-1) CARi(p,q) For negative surprise portfolio: (Ã…Å ¾SÄ–q 0) CARi(p,q-3) CARi(p,q-2) CARi(p,q-1) CARi(p,q) 4. EXPECTED CONCLUSION The contribution this research would make is extending current empirical research on cognitive biases affecting stock price behavior, and testing for investor irrationality, specifically investor overreaction and representative heuristic, as it relates to earnings surprises. This study may also provide further understanding of the drivers of stock prices for investment decision-making, forecasting, and policy making relevant to financial market participants. In addition, contribution towards a piece of the stock pricing puzzle, as well as further research questions may be discovered. It seems clear that EMH and CAPM (pillars of the current financial theory), despite mounting evidence against their validity, remain widely in use. Behavior finance seems to offer an alternative to the current financial theory, and therefore latest empirical research in asset pricing, is towards this direction in order to understand and discover a better way to price financial assets. This thesis expects, based on Thalerà ¢Ã¢â€š ¬Ã¢â€ž ¢s (1985) ORH, investor overreaction in the emerging markets (Malaysia, Thailand, and Singapore) as demonstrated through securities prices to be confirmed through empirical testing. In addition, this study expects to identify representative bias as a source of such overreaction. This study offers to contribute empirical research towards the field of behavior finance in understanding better the notion of investor irrationality and its consequent impact on asset pricing. 5. PROPOSED RESEARCH OUTLINE THESIS OUTLINE Sections: Introduction Brief History of Research Question Theoretical Framework Research Objectives Statement of Problem Research Significance Literature Review 1) Prior Research and Gaps 2) Investor Overreaction 3) Representative Heuristic 4) Research Questions Methodology 1) Methodology Overview 2) Research Hypothesis 3) Model Configuration 4) Dependent and Independent Variables 5) Sample Data and Characteristics 6) Construction of Test Portfolios 7) Measuring Abnormal Returns and Earnings 8) Empirical Results and Statistical Significance 9) Does Representative Bias drive Overreaction? 10) Limitations of Study Conclusions References Appendix/Exhibits