Software Tools for Qualitative Research Assignment

Software Tools for Qualitative Research - Assignment Example The latest version is the NVivo 10 designed to interact with social media platforms (Bazeley & Jackson, 2013). NVivo is able to preserve styles in their original documents forms including documents in non-English language. NVivo has containers called nodes, which can be assigned demographic data or attributes of features (Bazeley & Jackson, 2013). According to Bazeley & Jackson (2013), the nodes can further be rearranged in hierarchies, or be merged with similar nodes to form a single node for general representation. The occurrence of multiple sources which share common characteristics can also be grouped together to form various distinct classifications (Bazeley & Jackson, 2013). Schà ¶nfelder (2011) wrote that demographic information such as gender and age can be easily imported from external sources in form of text file or spreadsheet formats. NVivo10 further integrates automatic connection to face book, twitter, and LinkedIn datasets for the purposes of patterning data (Bazeley & Jackson, 2013). Bazeley & Jackson (2013) further added that, the package also has querying tool, which can be used to interrogate qualitative data to test theories or generate new information. With NVivo10, one can run dynamic modelling system to represent a project in real time or capture the project at a specified point in time using the static model (Flick, 2009). MAXQDA was developed from winMAX software tool, which had been designed in late 80s (Schà ¶nfelder, 2011). With MAXQDA, one is able to create and import texts in rich text format (Schà ¶nfelder, 2011). Referring to Flick (2009), MAXQDA software tool is able to extract text document from the internet by just dragging the documents from the websites and dropping them on the programs interface. Most objects and documents can also be imported in the form of embedded objects of the file

Proposal paper; Claim of policy Essay Example | Topics and Well Written Essays - 750 words

Proposal paper; Claim of policy - Essay Example â€Å"Contentious Objectors† have no right to claim this status in America today. The United States Armed Forces, every branch, only inducts volunteers. Unlike many foreign countries, Turkey, China, and Israel, where military service is mandatory, the draft is not in effect today in America. Since joining the American military is voluntary, a soldier cannot later claim they did not want to be a soldier. The Iraqi war is not a popular one with American citizens or even Iraqi Veterans. One soldier, Senior Airman Tim Goodrich, even felt compelled to create a website called Iraq Veterans Against the War at http://www.ivaw.org/ (Dahr). Although Tim Goodrich created this site, he served his time in Iraq (Dahr). He is against the war, but felt the need to fulfil his duty. Tim Goodrich did not go AWOL like some of his fellow servicemen. Another soldier has entered politics to protest the war. Tammy Duckworth â€Å"is the only seriously wounded combat veteran running this year for Congress, whose ranks of members with military experience are at their lowest since World War II, according to Congressional Quarterly† (Stone). Creating a website and running for Congress are productive ways of protesting the war, unfortunately not all soldiers protest the war in these healthy ways. Sixty Minutes II reported â€Å"hundreds of American soldiers have broken the law and gone AWOL† since the beginning of the Iraqi war (Rather). These â€Å"Contentious Objectors† are numerous, but on Sixty Minutes II the focus was on Staff Sgt. Camilo Meji (Rather). Staff Sgt. Meji refused to return to Iraq because he felt President Bush and other leaders lied about weapons of mass destruction, but his platoon leader Tad Warfel responded by saying â€Å"His duty’s not to question myself or anybody higher than me,† and â€Å"We’re not paid in the military to form personal opinions or to doubt what our leaders say† (Rather). Both men feel very strongly about their opinions.

Concepts in Nursing Research Methods

Concepts in Nursing Research Methods Critique criteria The criteria used for this critique were derived from relevant nursing literature (Feninstein Horwitz, 1997; Cormack, 2000; Khan et al, 2003). About a dozen criteria were specified: design, sample, inclusion/exclusion criteria, time frame of study, data collection, reliability validity, and data analysis. Study Design Catlette (2005) used a qualitative design. While this approach has its merits, principally a greater degree of realism and richer data, it has a number of significant drawbacks (Coolican, 1994). Observations are typically unreliable. In other words, if the same nurses were interviewed on several different occasions, about workplace violence, using the same open-ended interview protocol, their responses may vary somewhat. Various biases creep in, often caused by situational factors (e.g. open-ended questions, a very violent week followed by a particularly calm week), or personal considerations (e.g. memory deficits). Furthermore there is low internal validity. This means that it is difficult to establish with any certainty the relationship between variables, due to the lack of statistical analysis (which can estimate the probability that results occurred by chance). For example, Catlettes interview data suggests a link between workplace violence and feelings of vulnerability amongst n urses. However, the extent to which the former variable causes the latter cannot be reliably established in a qualitative study. Winstanley and Whittington (2004) enjoy the precision of a quantitative design. While internal validity is high, the level of realism is questionable. Participants were ‘forced’ to respond to predetermined questions (e.g. on physical assault) using a fixed response format (e.g. ‘Once’, ‘More than once’). Thus, the data obtained was heavily influenced by the kind of questions asked and the particular response format used. In the real world, health care staff may perceive the level of aggression in terms that don’t match the questionnaire format. For example, a nurse may perceive physical assaults as ‘sporadic’ or ‘once in a blue moon’. Since these categorisations weren’t available in the questionnaire, the study effectively lacks a certain degree of realism. In a qualitative desi gn, subjects describe the world as they see it, rather than via terms imposed by the researcher. Sample Ideally a sample should be randomly selected so that it is representative of the population from which it was drawn, in this case nurses or health care professionals. This allows findings from a single study to be generalised to the wider community. Catlette (2005) used a convenience sample, meaning it wasn’t representative of nurses in general. Granted there are considerable practical and logistic difficulties in trying to recruit a random sample of nurses. Their busy schedules and irregular shifts, for example, hamper proper scientific selection. It is also quite common for small convenience samples to be used in qualitative studies, since it is often impractical to conduct in-depth interviews with large groups. Nevertheless, Catlette’s findings, while relevant to the particular trauma centres involved, are unlikely to apply to nurses in general. This is a serious limitation, since Catlettes stated objectives suggest a general interest in the level of violence in hosp ital emergency departments, rather than the particular trauma units from which subjects were drawn. Winstanley and Whittington (2004) also appear to have a used a convenience sample: they simply invited health care staff working in a general hospital, and who had regular contact with patients, to participate. Although the target sample was quite large (a bigger sample improves representation), only a minority of staff actually completed and returned questionnaires. All in all, participants weren’t recruited randomly, therefore the findings cannot be generalised to the wider population of health care staff. Inclusion/Exclusion Criteria Both studies seemed to have clear inclusion/exclusion criteria. Catlette (2005) only recruited and interviewed nurses who were registered, worked in a level 1 trauma centre, and had experienced workplace violence. A clear definition of what constituted violence was developed, helping to minimise any ambiguities about eligibility. Winstanley and Whittington (2004) also specify inclusion criteria. Only health care staff that had regular and substantial contact with patients were invited to participate. What constituted ‘regular’ and ‘substantial’ contact was well defined (e.g. daily contact with patients). The advantage of having clear inclusion/exclusion criteria is that it helps the researcher recruit a homogenous sample. If the participants in a study are too diverse, this effectively introduces additional sources of error that may obscure interesting themes, or relationships between variables. Findings may be more difficult to interpret. However, a major d isadvantage of a homogenous sample is that it is invariably ‘ad-hoc’, that is special or unique, and hence unlikely to reflect the wider community. Nevertheless, it can be argued that sample homogeneity isn’t problematic if the wider community of interest exactly matches the inclusion/exclusion criteria. For example, Winstanley and Whittington’s (2004) study was about patient aggression towards health care staff. Thus, the population of interest was invariably going to be staff that had regular contact with patients. In this respect the sample selected corresponds with the population of interest. However, randomly selecting nurses from the designate population would have provided a representative sample that permits useful generalisations. Simply using volunteers, as Winstanley and Whittington did is unscientific. Time frame of study Winstanley and Whittington’s (2004) study was effectively a retrospective (i.e. cross-sectional) survey. This means that data was collected at one point in time, specifically an 8-week period. Retrospective designs are considered inferior to prospective (i.e. longitudinal) designs in which data is collected on two or more occasions, over several weeks, months, or even years (Coolican, 2004). This method allows tentative causal inferences to be made – if a variable measured at Time 1 predicts or correlates with a factor measured at Time 2, then there is a possibility that former variable affected the latter, but not vice versa. Retrospective designs don’t allow for such inferences. Any correlations between variables are just that – correlations! There is no sequence that may help delineate possible causality. For example, in their introduction and statements of study aims, Winstanley and Whittington imply that particular professions (e.g. nurses, doctors) a nd hospital departments (e.g. medical, A E) may elicit different levels of physical aggression experienced by staff. Thus, profession/department seemed to be conceptualised as causal factors. However, although data analysis revealed relationships between these factors and physical aggression, there is no provision in the retrospective design to infer causality, since all the variables are measured simultaneously. A prospective method in which profession/department predicts experiences of physical assault several weeks subsequently would be more conclusive. Catlette (2005) doesn’t explicitly state the time frame for her study, albeit interviews typically take several days, weeks, or perhaps months to complete. Notions of prospective and retrospective designs are typically associated with quantitative studies, and rarely applied to qualitative research. This is because qualitative studies are often exploratory, merely seeking to identify interesting phenomena rather than estab lish causal relationships between variables. Nevertheless, interviewing participants on two or more separate occasions can be used to demonstrate the robustness and reliability of any themes observed. For example, if the same themes emerge during interviews conducted at two different points in time, this would suggest that the themes are significant rather than fleeting. Data Collection Catlette (2005) appears to have used semi-structured interviews for data collection (Coolican, 1994). By asking every interviewee pre-set but open-ended questions in a particular sequence, she avoided the inconsistency and sloppiness often associated with wholly unstructured (i.e. casual) interviews. It is possible the interviews were informal but guided, meaning that pre-set questions were asked, albeit in no particular order. Either way, a guided or semi-structured interview suffers from certain constraints. Asking specific questions, albeit open-minded ones, restricts the interviewers flexibility to ask follow-up questions depending on the interviewees response. Interviews are also heavily affected by interpersonal factors, such as lack of rapport, physical attraction, and psychological manipulation. Winstanley and Whittington (2004) collected data via a questionnaire. This method has a number of limitations. One is the typically low response rate. Of 1141 questionnaires posted ou t to participants, only 375 (33%) were returned, denoting a considerable waste of resources. Often the questionnaires returned represent an unusually keen sub-sample that may differ in key respects from the original target group. This means that the researcher has to devote time and resources establishing what these differences are, and how they might affect the results. Furthermore, because the final sample is smaller, statistical power is reduced, increasing the possibility of a type II error. Another limitation of questionnaires is the use of restricted (or ‘forced choice’) response format. For example, subjects in Winstanley and Whittingtons’ (2004) study were forced to choose from three options – ‘none’, ‘one’ or ‘more than one’. Thus, there is no room for participants to qualify their answers, for example by pointing out memory lapses (e.g. ‘I can’t remember’), or indicating ambiguous experie nces (e.g. ‘not sure’). All in all, these restrictions reduce the realism and richness of data collected. Interviewing subjects on the same issues, but using open-ended questions, will probably yield slight different outcomes to those reported by Winstanley and Whittington (2004). Another limitation is that the bulk of questionnaire communication is written. There is no provision to measure visual cues and gestures, which typically account for much of human communication, or even auditory cues. For example, a frown or grunt, may signify a particularly traumatising experiencing, which simply can’t be detected from questionnaire responses. Finally, questionnaires are often completed in the absence of the researcher (e.g. postal questionnaire), making it difficult to supervise the proceedings, or verify whether the subject is the same person who completed the questionnaire. Overall, these constraints negate the conclusiveness of Winstanley and Whittington’s ( 2004) findings. Data Analysis In line with standard procedure in qualitative research Catlette (2005) performed thematic analysis to identify recurring patterns in the data. Meaningful information was extracted from the interview transcripts, after which themes were identified using a coding system. Although a highly useful procedure, Braun and Clarke (2006) note that thematic analysis has certain disadvantages. One is the possible overlap between themes. Catlette identifies two themes – vulnerability and inadequate safety measures. Categories, and subcategories reported suggest considerable overlap between these dimensions (e.g. the sentiment ‘feeling unsafe’ may depict both feelings of vulnerability and an unsafe environment). Another weakness is the high correspondence between the data collection questions (i.e. interview guide) and themes identified. In other words, the themes reported merely reflect the questions asked during the interview (e.g. questions on safety, such as â€Å"How do you feel about the safety of your workplace?† are bound to produce safety-related responses, and hence themes). This suggests very limited analytic work was done to identify themes independent of the interview format. Another shortcoming of thematic analysis is failure to incorporate alternative or contradictive data in the results reported. Catlette offers little if any account of oddities in the data that don’t necessarily fit the two emerging themes. For example, the interviews revealed that violence wasn’t a concern during interactions with co-workers. Clearly this revelation is incompatible with the notion of vulnerability and lack of safety in the workplace. Yet, little is made of this inconsistency, making Catlettes’ rather ‘tidy’ themes appear rather suspicious. Few data sets in qualitative research are completely harmonious with no contradictions, so a study that fails to report these oddities is highly questionable. Winstanley and Wh ittington (2004) employed an inferential statistical test to analysis their data, consistent with the quantitative design of their study. Chi-square was used to test for significant trends in the frequency of physical assaults as a function of different health care professionals (e.g. nurses and doctors) and hospital departments (e.g. medical, surgical, A E). Chi-square was appropriate given that the data was categorical (i.e. in the form of frequencies). However, as a non-parametric test, chi-square lacks sensitivity. This combined with the limitations of frequency data (e.g. it fails to account for subtle degrees of variation between individual subjects or groups; for example, asking nurses if they’ve experienced aggression ‘once’ or ‘more than once’ fails to take into account any differences in the intensity and duration of these aggressive episodes), increases the risk of wrongly accepting the null-hypothesis. Reliability Validity A major methodological concern in scientific research is reliability and validity. Reliability refers to the consistency of observations, while validity depicts the authenticity of observations. Both issues are particularly pertinent in qualitative studies, due to the lack of structure, precision, and quantification. Catlette (2005) appears to have taken steps to enhance reliability/validity. She kept a journal throughout the duration of the study, in order to identify any biases that may corrupt the data. Interviews were conducted using a standard protocol, then the data was transcribed verbatim, and analysed using regular procedures. However, these measures may be inadequate. Coolican (1994) identifies several procedures for ensuring good reliability, none of which appears to have been used by Catlette: triangulation, analysis of negative cases, repetition of research cycle, and participant consultation. Triangulation involves verifying emerging themes using another data collection method other than open-ended interviews. For example a questionnaire measure of perceived workplace violence and safety strategies could have been administered or close-ended interviews conducted. Data from these alternative methods could then be compared with the original observations to gauge the degree of consistency in emerging themes. Analysis of negative cases involves scrutinising cases that don’t fit the emerging themes. Repetition of research cycle entails repeatedly reviewing assumptions and inferences, to further verify emerging themes. Finally participant consultation involves communicating with participants to see if observations from the study match their own experiences. None of these measures seem to have been applied in Catlette’s study, raising serious concerns about the stability and authenticity of her observations. Winstanley and Whittington’s’ (2004) study doesn’t appear to have fared much better. Although the numerical preci sion inherent in quantitative designs offers some degree of reliability and validity, this is by no means guaranteed, and has to be demonstrated empirically. They fail to report any Cronbach Alpha reliability coefficients for the questionnaire used. Thus, it is unclear if the items in this instrument were internally consistent. Test-retest reliability wasn’t reported either, again raising questions about the consistency of participants responses over time. A badly designed questionnaire (e.g. one with ambiguous statements, or grammatical errors) could easily confuse participants, leading to irregularities in their responses over time. No information on validity is provided either. Normally, validity could be demonstrated by correlating data from the questionnaire with data from another measure of experiences of aggression (a high correlation would indicate good validity), submitting the questionnaire to a team of judges to ascertain if the content addresses all forms of human aggression (e.g. indirect forms of aggression, such as spreading rumours or social exclusion don’t appear to have been assessed), and even performing factor analysis to establish construct validity (i.e. verify the dimensions of aggression assumed to be measured by items in the questionnaire). These inadequacies render the findings from Winstanley and Whittington’s (2004) study inconclusive. For example, the claim that aggression is â€Å"widespread† is questionable because not all forms of aggression were measured. Overall, both studies are fairly categorical in their conclusions. Winstanley and Whittington (2004) surmise that their data demonstrates the significant levels of aggression to which hospital staff are exposed. Catlette (2005) reaches a similar conclusion, emphasising the vulnerability and lack of safety perceived by nurses. However, both studies suffer from various analytic and methodological constraints. Perhaps the most serious of these is the apparent absence of reliability and validity measures that may reveal any volatility or misrepresentations in the data. These limitations mean that any conclusions have to be regarded as tentative, subject to further research. Bibliography Braun, V. Clarke, V. (2006) Using thematic analysis in psychology. Qualitative  Research in Psychology, 3, pp.77-101. Catlette, M. (2005) A descriptive study of the perceptions of workplace violence and  safety strategies of nurses working in Level I trauma centres. Journal of  Emergency Nursing, 31, 519-525. Coolican, H. (1994) Research Methods and Statistics in Psychology, London, Hodder   Stoughton. Cormack, D. (2000) The Research Process in Nursing: Fourth Edition. London:  Blackwell Science. Eastabrooks, C.A. (1998) Will evidence-based nursing practice make practice perfect.  Canadian Journal of Nursing Research. 30, pp.15-36. Feninstein, A. R., Horwitz, R. I. (1997) Problems in the evidence of evidence-based medicine. American Journal of Medicine 103, 529-535. Khan, K., Kunz, R., Kleijnen, J. Antes, G. (2003) Systematic Reviews to Support  Evidence-based Medicine: How to Review and Apply Findings of Healthcare  Research. Oxford: Royal Society of Medicine Press. Winstanley, S. Whittington, R. (2004) Aggression towards health care staff in a UK  general hospital: variation among professions and departments. Journal of  Clinical Nursing, 13, pp.3-10.

uniforms in school :: essays research papers

Most teenagers, when asked about wearing uniforms say they hate the idea. I am a teenager myself and before researching this topic I thought I would never wear them. I have found in my research numerous reasons uniforms should be implimented. First, they protect us. You may be asking how an article of clothing can protect you. It is much more difficult to conceal a weapon in a uniform that it is to conceal it in a traditional baggy pair of jeans. Second, uniforms eliminate competition. Students need to learn that school is not a fashion show. I know it's fun to go shopping for the latest trends and fads but school is a learning environment thus we should be there to learn, not check out who is wearing what. Next, uniforms are much less expensive than traditional clothing worn by teens. Some schools provide programs where kids can recieve the uniforms at little or no cost. In addition, it prepares students for the workplace by requiring them to dress in attire suitable for work. You may have heard you are what you wear. If you are dressed conservatively you are much more likely to act conservatively. If all students wore uniforms, intruders to the school could be easily recognized. This could prevent unauthorized visits as well as incidents that could be more severe. The biggest argument from opponents of uniforms is that they eliminate individuality. That is a bunch of crap. How is it that a person can be an individual when all kids dress alike? Okay so there are a few who get a little crazy with the multicolored hair or ripped jeans, to those people, I give props. I know they take a lot of shit from other kids. But, there are other ways to express yourself.

Capital Asset Pricing Model and International Research Journal

International Research Journal of Finance and Economics ISSN 1450-2887 Issue 4 (2006)  © EuroJournals Publishing, Inc. 2006 http://www. eurojournals. 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: [email  protected] gr Tel: 00302310891889 Stavros Tsopoglou University of Macedonia, Economic and Social Sciences Department of Applied Informatics Thessaloniki, Greece E-mail: [email  protected] r Tel: 00302310891889 Demetrios Papanastasiou University of Macedonia, Economic and Social Sciences Department of Applied Informatics Thessaloniki, Greece E-mail: [email  protected] 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 return s 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 CA PM 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 t o 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 signifi cant 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 succe ssful 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.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 th eir 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 fo r 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 wheth er 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 p ositive 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 c onsiders 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 c haracterized 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 trading day, and are chosen according to pr especified 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 sp an 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 Share 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 retur ns 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 the portfolio and the return on a risk-free asset), ? p is an estimate of beta of the portfolio p , ?1 is th e 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 > 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 signifi cant 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 . 82 90 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. 4369The 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 â €“ 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 examine 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 Val ue . 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) . 1034However, 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 ret urn 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 on 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. 1607According 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 Fi nance 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 Fi nance 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 stu dy 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.

Striving for Personal Success

Striving for Personal Success University of Phoenix Gen 200 Eleanor Roosevelt once said, â€Å"In the long run, we shape our lives, and we shape ourselves. The process never ends until we die. And the choices we make are ultimately our own responsibility† (Goodreads Inc. , 2013). Although life can be a major deterrent on success, it also can be one factor in obtaining college success. The personal responsibility a student holds is based on their motivation and self- sacrifices.Success in life and in college can only be measures by the hard work, drive, and dedication. Personal responsibility is defined as relating or pertaining to oneself and the state of responsibility as moral, legal, or mental accountability (Merriam-Webster, Incorporated, 2013). One way of applying personal responsibility in a student's life is to continue to push the limits that other people have placed on them and pushing those boundaries to the max. With huge success can come countless opportunities, th rough these opportunities comes dedication and challenges.It is in how each person deals with those dedication and challenges that makes the sacrifices worth everything. When the idea of quitting occurs is when an individual must lean on his or her support systems to pull them through that state of mind. It is family, friends, and amazing instructors who can be the factors between persevering through the hard times and giving up. The question a student will then have to face is, what is most important to that person? By fguring out that question and realize the time and dedication that has already been applied would be a waste.At this final point the individual has to find the motivation and drive to re light that fire under them and ontinue to strive on. It is that personal responsibility of holding oneself accountable for the hard work that will lead that person to college success. Personal responsibility is a factor in achieving success in life and in college, but personal respon sibility also can be a hinder. By being a college student people not only have dedication to their schoolwork, but also their outside school, such as family, friends, and a Job.The struggle is trying to find that balance needed to succeed in all fields. Alexandra Escobar said, â€Å"Professional women often struggle as they try to alance their work and personal roles, while still trying to grow in both,† she currently holds a master's degree in education and holds a chair for the College of Education at University of Phoenix South Florida Campus (â€Å"How women in business can â€Å"lean in†,† personal roles, but as shown through her degree and position held in her college now the success for a degree is possible.The major factor is time management. Every person must learn to create time for what matters, whether that is to wake up an hour earlier or turn the television off to complete that one assignment. 0. 1. Simpson once said, â€Å"The day you take comple te responsibility for yourself, the day you stop making any excuses that is the day you start to the top† ([email  protected], 2001). If this degree and college success is what matters, that person will find a way to make it work, or that person will find a way to make excuses.The main question every person must ask themselves is success at this very moment more important than success for growth that can better one's future or not? Personal responsibility and college success are proving to be one and the same. Success in college and in life can only be achieved by one's own self-sacrifice and elf-dedication. Winston Churchill once said, â€Å"The price of greatness is responsibility' ([email  protected], 2001). Winston Churchill is right.It is the self-sacrifice of choosing to miss out on social events, school events, or pull extra work hours so that person can finish his or her assignments needed to succeed in their classes. The self-dedication will need to be applied to obtain such greatness. By applying self- dedication and personal sacrifices one is allowing themself to grow in personal success. Each hurdle completed in their college Journey is another step closer to obtain ollege success as well as being able to apply the knowledge obtained into their lifestyle and in return gain success in the workforce.It is each individual's personal responsibility to apply what is being learned in and out of the classroom through their college Journey that will truly allow them to have the greatest form of college success. An educational success will be obtained by a college degree, but also a workforce knowledge and personal growth that can be achieved only by experiencing that balancing act between personal responsibility and college success. In conclusion, personal responsibility is the key to success. Success is also determined by self-sacrifice and self-dedication.