Welfare Should Not Be Removed Completely - 1721 Words

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

Capital Asset Pricing Model and International Research Journal Free Essays

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

The Doctoral Research Process â€Æ

Question: Write an essay on The Doctoral Research Process ? Answer: Source: Maleki, M., Akbarzadeh Pasha, M. (2012). Ethical Challenges: Customer's Rights., 9(4), 5-21. The ecommerce business has opened up potential business opportunities. But there are various ethical issues that are arising which mostly surround the customers. There are various ways in which the ethics has affected the ecommerce business. This has increased the responsibility of the IT professionals towards increasing the trust of the consumers towards the e-commerce business by introducing certain ethical standards and abiding by those standards. This will make the ecommerce business more reliable and trust worthy (Jimerson, 2013). But the most worrisome activity for the customers as well as the developers is the trusting on the ecommerce sites (Fabregat, 2013). Technology has opened up new opportunities but it has also introduced certain illegal and immoral activities. The general society including the IT professionals has to abide by the ethical standards. There are some ethical challenges that are faced by the engineers while providing the service to the customers. But the IT professionals involved in the e-commerce business has to abide by the ethical principles so that the trust of the consumers increases and any kind of risk related to the ethics is avoided. This will increase the trust of the consumers and they will be able to trust more on the e-commerce sites. The e-commerce business activity will grow ('eCommerce', 2013). It is utmost important that the IT professionals follow the ethical guidelines which will increase the confidence of the customers. They must seek for possible solutions to encourage the trust of the consumers (Guptara, 1999). Source: Kenneth McBride, N. (2014). ACTIVE ethics: an information systems ethics for the internet age. J Of Inf, Com Eth In Society, 12(1), 21-44. doi:10.1108/jices-06-2013-0017 Ethical standards in the information systems are an important aspects and it increases the responsibility of the IT professionals to abide by the ethical guidelines. The information system forms an essential part of the social systems and the ethical and the social issues will be addressed by the cooperation of the IT professionals (Scott, 2002). It will lead to the successful implementation of the information system services. There are certain codes of ethics that the IT professional has to abide. These codes of ethics form an important part of the business. The customer trust and reliability towards the various information systems will increase as a result of the participation of the IT professionals in maintenance of the integrity of the IT industry. IT has opened up new opportunities but it has also introduced potential hazards. It has introduced various forms of illegal activities. It has also introduced various illegal activities regarding the use of the Internet. It is the res ponsibility of the IT professionals to build firewall protection and provide password protection so that the illegal activities can be prevented in an adequate manner. This will increase the trust and reliability of the customers. The illegal activities can be reduced to a considerable extent (Jin, Drozdenko Bassett, 2006). Source: Monk,,. (2015). Good engineers. European Journal Of Engineering Education, 22(3), 235. The IT professionals are responsible for making judgments and promoting the decisions. The code of practices is used by the IT professionals for the purpose of decision making. But the decision making process of the IT professionals is a tough job. The IT professionals have to keep a balance between the good judgment and the bad judgment. They have to use their wisdom to make decisions. The customers face certain ethical dilemmas which tend to break their trust. But the IT professionals have to ensure that the ethical values are preserved. The customers tend to be swayed away by the advertisements regarding the performance of a particular electronic product but it is the responsibility of the IT professionals to prepare the product in such a manner so that the trust of the customers is maintained. The products must deliver the high performance and it must be efficient (Derbyshire, 2008). This will make the device efficient and user friendly. The reliability and trust of the consumers will increase. Thus the IT professionals have to abide by the code of conduct and they must not suffer from ethical dilemmas. This increase the confidence of the customers and the reliability will increase (Bauml, 2013). Source: Monk, J. (2008). Ethics, Engineers and Drama. Sci Eng Ethics, 15(1), 111-123. doi:10.1007/s11948-008-9099-9 The IT professionals are seen to suffer from ethical dilemmas and they are not able to make wise choices between the available alternatives. The engineers must possess the skills to deal with the ethical issues that are faced by them. They must be able to deal with the ethical issues and make their engagement with people that are persuasive and know how to make negotiations. While IT professionals have obligations regarding the successful creation, establishment, operation, upkeep or transfer of hardware they are prone to have extra allegiances inside of the more extensive group and duties towards closer weave gatherings, for example, work associates and crew (den Bergh Deschoolmeester, 2010). These diverse gatherings have distinctive intrigues and worth things distinctively and this can create circumstances that set one obligation or worth against another. Defied by such circumstances the designer needs to make his or her own particular judgments and pass on them convincingly. Freq uently the circumstance is developing quickly so judgments lay on fractional or confounding information shaded by forceful feelings and passed on with differing degrees of ability and flurry. By and by, unpalatable results can rise in light of justifiable misjudgments or since a proficient's pride or willfulness makes they disregard choices, since different hobbies are purposely and untrustworthily distorted or on the grounds that activity to accommodate a problem is in light of flawed rationale. Such settings have the potential to prompt individual tragedies, and in this way recommend situations for screenwriters to abuse. While numerous contextual investigations barely concentrate on designing issues, dramatization is generally kept in touch with location a wide crowd and ordinarily conveys to tolerate a more extensive scope of human concerns which are obligated to impact the honing proficient. Periodically the sensational connection has a designing flavor and the outcome is valua ble instructive asset for building understudies. Formalized implicit rules, for example, are an aide and endeavor to suspect troublesome circumstances however they are likewise a rich hotspot for the playwright (Brunswick, 2009). The screenwriter's assignment is not to develop the code, in any case, to design circumstances where the tenets of the code create inconsistencies. The ethics and code of conduct of the IT professionals are as important as the code of ethics of the doctors. Violation of the ethical codes is subjected to punishment. There must be transparency between the customers and the IT professionals. The transparency of the procedures will make the system legal and it will not be subjected to any kind of illegal activity. The moral principles have to be followed. The reliability of the customers will increase. They will trust in ecommerce sites and the information that is presented in the internet. The awareness of the IT professionals has to be increased that will promote the awareness of the IT professionals towards the ethical values and guidelines. This will make the IT professionals more disciplined and they will focus towards the information users. The IT professionals have experience on the professional development and they must focus on the customer service (Cofta, 2006). References Bauml, U. (2013). eCommerce - Der Kunde ist im Netz.CON,25(6), 281-283. doi:10.15358/0935-0381_2013_6_281 Brunswick, S. (2009). eCommerce fraud time to act?.Card Technology Today,21(1), 12-13. doi:10.1016/s0965-2590(09)70019-2 Cofta, P. (2006). Convergence and trust in eCommerce.BT Technol J,24(2), 214-218. doi:10.1007/s10550-006-0063-4 den Bergh, J., Deschoolmeester, D. (2010). Ethical Decision Making in ICT: Discussing the Impact of an Ethical Code of Conduct.CIBIMA, 1-11. doi:10.5171/2010.127497 Derbyshire, S. (2008). The Ethical Dilemma of Ethical Committees.Sociology Compass,2(5), 1506-1522. doi:10.1111/j.1751-9020.2008.00143.x eCommerce. (2013).CON,25(6), 311-311. doi:10.15358/0935-0381_2013_6_311 Fabregat, J. (2013). Explicit Training in Human Values and Social Attitudes of Future Engineers in Spain.Sci Eng Ethics,19(4), 1551-1556. doi:10.1007/s11948-013-9487-7 Guptara, P. (1999). Ethics and Values.Business Ethics,8(3), 196-198. doi:10.1111/1467-8608.00150 Jimerson, R. (2013). Values and Ethics.Journal Of Information Ethics,22(2), 21-45. doi:10.3172/jie.22.2.21 Jin, K., Drozdenko, R., Bassett, R. (2006). Information Technology Professionals Perceived Organizational Values and Managerial Ethics: An Empirical Study.Journal Of Business Ethics,71(2), 149-159. doi:10.1007/s10551-006-9131-4 Scott, E. (2002). Organizational Moral Values.Business Ethics Quarterly,12(1), 33. doi:10.2307/3857647