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Submitted By twilson121

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Words 1231

Pages 5

Thomas L. Wilson

University of Maryland University College: HMLS 312

Professor Steven Woodall

06 September 2014

Term Paper

Security vs. Privacy

Executive and legislative measures implemented to strengthen the security of the United States and territories within directly contributed to an increase in privacy concerns following the 9/11 terrorist attacks in the United States and ignited debate, discussion, and study regarding balancing security and privacy thereafter. (Parker, 2004) I am concerned whether or not citizens of the United States will have to forfeit a significant amount of privacy due to intelligence gathering against terrorist activity directed at this country. In my mind, this cannot be answered directed, tucked away and blindly followed. Every step and implementation of new procedures and technologies in direct support of intelligence gathering and identification of terrorist suspects must be debated and understood before going forwarded to ensure due process for privacy concerns. They may have to be an understanding or “give-and-take. (Jenkins, 2012)

Many of our security vs. privacy concerns stem from processes implemented after 9/11 for ensuring the United States was more prepared to detect and combat terrorist activities. Of course immediately after, many disagreed and argued that a balance between states security and civil liberty has to be maintained. Additionally, there was (and still is) a considerable requirement for the United States government to collect, process, and understand large amounts of video surveillance, biometrics, and Private Personal Information (PPI) in our efforts in combating terrorist activities. With respect to video surveillance (specifically in public areas), in the last 15 years, security/video cameras have been installed in major cities with the…...

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...and from the website of Professor Kenneth French at the Dartmouth College and we downloaded the following series: • Fama/French Factors, and • Fama/French Factors [Daily]. We held the daily data to use in project 2, and for the monthly data we only collected the SMB (Small minus big) and HML (High minus low) monthly. Step 8 we deleted any data that occur before December 1958. And kept everything that occurs on December 1958 and later. Step 9 then we convert all the monthly data into quarterly using the following formulas • For the Consumer Price Index, we computed quarterly log-inflation as ln[ (value at the end of the quarter)/(value at the beginning of the quarter) ]. Note: The CPI data are reported as of the beginning of the month. Thus a CPI value reported for 1-Dec-05 is actually an end-of-November value. Therefore, the log-inflation for the fourth quarter of 2005 would be calculated as ln[ (CPI for 1-Jan-06) / (CPI for 1-Oct-05) ]. • For the Stock Market Return, the value premium (HML) and the size premium (SMB), we computed quarterly logarithmic return as ln[ (1 + return in month 1)*(1 + return in month 2)*(1+return in month 3) ]. Note that the HML and SMB data are given in percentage terms. Therefore, before this computation, we did convert the data into decimals by dividing them by 100. • For the 3-Month T-Bill Yield, we computed quarterly logarithmic yield as ln[ (1 + T-Bill rate at the end of the previous quarter)^(1/4) ]. Note......

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...Big stocks and HML stands for the return on High Minus Low stocks. 1) The mean, standard deviation, skewness, kurtosis and excess kurtosis for the excess return variables are: Excess Variables | Mean | Standard Deviation | Skewness | Kurtosis | RBLK - RFR | 2.1396 | 9.7730 | 0.2610 | 1.6934 | RM – RFR | 0.3207 | 4.7273 | -0.5442 | 0.5824 | SMB | 0.5039 | 3.2677 | 0.5920 | 2.8920 | HML | 0.3007 | 4.4211 | -0.1769 | 6.4445 | For a sample data to be normally distributed, it should have a skewness of 0 and a kurtosis of 0 (as calculated in Excel). For our data, we can see that RBLK – RFR and HML are not significantly different from a normal distribution; while RM-RFR and SMB are not normally distributed since they are unusually skewed (skewness does not lie within ±0.5). RBLK-RFR, RM-RFR, SMB and HML are all leptokutic. The Histograms for these descriptive statistics can be found in the appendix. 2) The correlation matrix for the four excess return variables is: | RBLK-RFR | RM-RFR | SMB | HML | RBLK-RFR | 1 | | | | RM-RFR | 0.4189 | 1 | | | SMB | 0.1783 | 0.3380 | 1 | | HML | 0.1141 | 0.1174 | -0.1551 | 1 | In the above correlation matrix, a positive correlation signifies that if one variable increases, the other variable also increases and vice-versa. We can see that BlackRock’s excess monthly return RBLK-RFR is positively correlated to the excess market return RM-RFR. RBLK-RFR is also slightly positively correlated to SMB and HML. RM-RFR......

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...1.The mean, standard deviation, skewness and kurtosis for each of the excess return variables are shown in Table 1. The skewness of RSOHU-RFR, SMB and HML are all greater than 0 which means that RSOHU-RFR, SMB and HML are all positively skewed. The skewness of RM-RFR is smaller than 0, which means that RM-RFR is negatively skewed. The kurtosis of RSOHU-RFR, RM-RFR, SMB and HML are all greater than 0, which means that the distributions of RSOHU-RFR, RM-RFR, SMB and HML are leptokurtic distributions. Based on the skewness and kurtosis, which are all not closed to zero, my research conclude that all the four variables do not appear normally distributed. Moreover, the Chi-test of RSOHU-RFR, SMB and HML are 56.2353, 42.5281 and 22313.4718, which are all greater than 23.6. The Chi-test of RSOHU-RFR, SMB and HML means that RSOHU-RFR, SMB and HML do not appear normally distributed. And the Chi-test of RM-RFR is 16.1203, which is smaller than 23.6. The Chi-test of RM-RFR means that RM appears normally distributed. 2. The correlation matrix for the four excess return variables is shown in Table 2. Most of the correlations are all positive, except the correlation between RSOHU-RFR and HML, which is -0.0357. Moreover, all the correlations are not very big. The absolute value of all the correlations are all smaller than 0.5. 3. The intercept of the CAPM model is 3.0111, which is greater than 0 and means there is an arbitrage opportunity. The coefficient of RM-RFR is 2.0544. Therefore,......

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...'Inventory Turnover' A ratio showing how many times a company's inventory is sold and replaced over a period. the Periodic Review To make the most effective use of ABC classifications, the analysis should be completed at least on an annual basis, and more often as necessary. 4 Other Inventory Classification Techniques HML Classifications The High, medium and Low (HML) classification follows the same procedure as is adopted in ABC classification. Only difference is that in HML, the classification unit value is the criterion and not the annual consumption value. The items of inventory should be listed in the descending order of unit value and it is up to the management to fix limits for three categories. For examples, the management may decide that all units with unit value of Rs. 2000 and above will be H items, Rs. 1000 to 2000 M items and less than Rs. 1000 L items. The HML analysis is useful for keeping control over consumption at departmental levels, for deciding the frequency of physical verification, and for controlling purchases. VED Classification While in ABC, classification inventories are classified on the basis of their consumption value and in HML analysis the unit value is the basis, criticality of inventories is the basis for vital, essential and desirable categorization. The VED analysis is done to determine the criticality of an item and its effect on production and other services. It is specially used for classification of spare parts. If a part is......

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...leading management consulting, technology service and outsourcing company. It went public in 1998 and is listed on the New York Stock Exchange. In this report, I analyzed factors that relate to Accenture’s excess return, including the single index model (CAPM), three-factor model, and tests of the assumptions of OLS. II. Data and data description I collected monthly adjusted closing price of Accenture from July 2001(the earliest available date) to September 2014 and that of 13-week Treasury Bill, as well as Fama French Benchmark Factors (Rm-Rf, SMB, HML) of the same time period. The data description is showed in Table 2, I then tested properties of those variables. 1. Normal Distribution The test statistics of histogram presented in Table 2 follows a chi-square distribution with 9 degrees of freedom. The critical value at 5% significance level is 23.6. So the results show that Rm-Rf and SMB are normally distributed; while RACN-Rf and HML are not normally distributed. 2. Correlation (Multicollinearity) The correlation matrix presented in Table 3 shows that the excess return of Accenture is positive correlated with the three factors. Positive correlation with SMB indicates that Accenture behaves more like a small stock. The positive correlation between independent variables indicates there may be problem of multicollinearity, which need further test. III. Single Index Model (CAPM) I first built the single index model: RACN-Rf=α+βRm-Rf+ε. The results are presented in......

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...model, have been born. Among all the multi-factor models, Fama-French three factor model is a most popular model. In this model, another two factors, which are the size of firm and book value to market, are added to the CAPM. The size factor (SMB) can distinguish large firms with small firms; and the book value to market factor (HML) can be represented by the different ratios of book value to market. Fama-French three factor model can be illustrated by the below formula: E(Ri) - Rf = bi[E(Rm) - Rf] + siE(SMB) + hiE(HML) in which: Rf is risk-free rate bi, si, hi are the sensitivities of respective factor Although the benefits of the Fama-French three-factor model are well-known, the Fama-French model has been subject to further improvement. Therefore, in the following analysis, hypothesis and validation method will be applied to find whether Fama-French three factor model is the most appropriate methodology for the US retail and oil industries. 2.2 Data and Methodology A group of data, which are directly related to US retail and oil industries, such as monthly returns (value weighted), risk-free rate, market risk premium, SMB, HML and momentum, can be obtained from Ken French’s Data Library. In addition, we also download the other group of data regarding macroeconomic information from FRED website. We choose three macro-economic factors - CPI, IPI and unemployment rate, because they are closely related to the retail industry and oil industry. IPI or CPI can......

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...ensure that group member names are entered onto the worksheet tab “Tally Sheet”. When posting please rename the workbook so that it includes the family names of the group members. For example, if Smith, Jones, and Brown work together, their submission would be named “Smith Jones Brown Assignment 2.xlsx” There are 20 available points on this assignment. Your tasks are as follows: 0) Please enter group member name and ID information on spreadsheet tab “Tally Sheet”. 1) Factor models, Fama-French factors, and active management: You will find monthly excess return series for 3 actively managed portfolios on tab "Q1". You will also find excess returns for a market capitalization weighted index and returns on the Fama-French (FF) factors SMB and HML. You will likely want to review the text’s discussion of FF starting on p. 399. a) Comment on the excess returns of the three active portfolios relative to those of the market cap index. How do they compare to those of the index? Considering the index alone as a benchmark, do the managers of the respective funds appear to demonstrate any skill? [3 points] b) The manager of Portfolio 1, who has been lauded in the press for his brilliant management, is (a bit too) proud of his accomplishments, stating "Since the beginning of this century I've killed the index, beating the passive portfolio by, on average, more than 30 basis points per month. This is way more than can be explained by the risk of the benchmark. My proprietary stock......

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...the actual return and obtain a more optimal portfolio. This in turn provides the analyst with the tools to determine how well the model can predict the overall variance in the actual return of an investment in a particular fund. Once the optimal weights are determined, the variances stated above are used to determine the percentage of the return variance that can be explained by the model (the style/r^2 value) and the percentage that can be explained by the investor’s selection strategy. This is completed for all three funds to determine whether or not the investor is using a passive or active strategy for investing in these funds. In addition the three FAMA FRENCH factors (mktrf – Market Return – Risk Free Rate), smb (Small minus Big), and hml (High minus Low)) are used to run a multi-variable regression analysis using Excel’s Toolpak tool. From this analysis the Jensen alpha and appropriate beta coefficients can be calculated to determine how the Risk Premium depends on each of the factors and also the degree of security selectiveness of the investor. Finally, over an eight year period the same optimal portfolio analysis is completed for each year to determine if there was any style drift evident throughout the investor’s tenure investing in a particular fund. For this the percentage of the return variation that can be explained by the model is observed, and if it changes drastically, evidence of style drift is present. Other parameters such as the Sharpe Ratio and the......

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...(conceptually analogous to the CAPM beta but not equal to it due to the presence of the two other coefficients in the regression) ------------------------------------------------- (Km- Rf) = market risk premium ------------------------------------------------- bs = sensitivity of expected return to size factor ------------------------------------------------- SMB = Small (market capitalisation) minus big ------------------------------------------------- bv = sensitivity of expected return to value factor HML = high (book to market ratio) minus low Fama and French (1992a) found that the historical-average returns on stocks with small market capitalisations and higher book-to-market ratios are higher than what the security market line would predict (Bodie, Kane and Marcus 2014). They assumed that any factors which corresponded to higher or lower returns consistently are indicators of a source of systematic risk and hence included the size premium (SMB) and value premium (HML) in their model along with the market risk premium found in the CAPM. To calculate the size and value factors, stocks are first grouped according to their market capitalisation and book-to-market ratios. Market capitalisation is equal to the number of shares outstanding multiplied by the share price. In their original calculations, for the size factor, Fama and French (1993) ranked all the stocks on the NYSE according to their market capitalisation in June of each year from 1963 to 1991, found......

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...Report of CVS Pharmacy’s Excess Return I. company introduction CVS Pharmacy (styled as CVS) is an American pharmacy retailer and currently stands as the second largest pharmacy chain, in the United States, with more than 7,600 stores, and is the second largest US pharmacy based on total prescription revenue. II. Purpose To determine if there are excess returns on CVS’ stock. III. Time period of data Monthly Fama/French Benchmark Return(SMB, HML, Rm-Rf), Monthly risk-free rate and Monthly price of CVS from Oct 2005 to Oct 2015. IV. Test of the correlation The correlation matrix for the excess return variables (SMB, HML, Rcvs-Rf, Rm-Rf) | Rm-Rf | SMB | HML | Rcvs-Rf | Rm-Rf | 1 | | | | SMB | 0.394264 | 1 | | | HML | 0.424707 | 0.305891 | 1 | | Rcvs-Rf | 0.597283 | 0.305935 | 0.197747 | 1 | Analysis: V. Serial correlation tests Method: Durbin-Watson statistic Procedure: first, we use the formula DW=t=2Tεt-εt-12t=1Tεt2 to calculate the DW value. It is 2.2161. It is higher than 2.0 so we use (4-DW=1.7839) to compare with the criteria. Then we find that the critical values dl and du for 121 observations when there is one independent variable are 1.65 and 1.69. Because the DW statistic of 1.7839 for the regression is higher than du, we can’t reject the null hypothesis of no serial correlation. Conclusion: There is no serial correlation on the CAPM model. VI. CAPM model: Rcvs-Rf=α+β(Rm-Rf) 1. Model The......

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...In this assignment I will be explaining the reasons for selecting the particular method of data collection for a selected product/service. The selected product that I will be collecting data for is an anti-depression medication made my HML (Hutchinsons Medical Limited). The data collecting method I’ve chosen is face to face interviews in order to get the respondents full attention and collect data without any distractions. I intend on finding out the respondent’s age, whether they suffer from depression/anxiety, whether they take medication for their depression/anxiety, and which form of our product they would prefer to purchase (roll on, cream, liquid, pills). The sampling method that I chose was probability sampling. I chose this method because it means everyone gets the chance to be picked meaning that the results will represent the whole group of interest. Another example of the fairness in this method is someone reaching into a box full of names and picking one out. The selection process ensures that everyone has a chance of winning. The advantages are that it’s not bias and as aforementioned it represents the whole group of interest. The results can also be used to calculate any further statistical analysis. I rejected non random sampling as it’s an unfair selection process as you get to pick people which makes in an unequal selection process. The people that are picked out might not represent the rest of the group and unlike probability sampling you can’t use the......

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...Comparison of both strategies under different measures In our group’s opinion, AQR’s retail funds are more suitable for investors who believe in momentum but lack the ability to access similar strategies, and for investors who are more risk-averse and require more secured investments. In Exhibit 5 in the case, it is observed that UMD has correlations close to zero with Market risk premium and SMB, and a negative correlation with HML. AQR might promote its momentum portfolio by emphasizing that it’s feasible to gain higher returns with lower risks if investor simply adds momentum into portfolios using any of the three strategies, in other words, diversification. However, it is noteworthy that AQR’s retail momentum strategy used a long-only position (U) rather than a long-short one (UMD), thus the correlation mentioned above might vary in the former case. The more appropriate way to estimate the correlations is to use the performance of “U” stocks only. The re-calculated results are shown as below. Correlation Coefficients (1927-2008) RMRF SMB HML U U 0.934 0.607 0.098 1.000 Table 2.3 Correlation Coefficients It is not surprised to see that the actual correlations between U with the other strategies are all positive with relatively significant magnitudes, indicating that the potential selling point of diversification is not applicable to AQR’s retail momentum strategy. Purpose of AQR’s Momentum Indexes It is natural to draw question like what is the purpose of AQR’s......

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...selection Jack treynor(1962), william sharpe(1964), John Lintner(1965) and Jan Mossin(1966) gave the CAPM model which was proposed to quantify the relationship between beta of an asset to its respective return, given the appropriate assumptions. Over the years many researchers have tried to contribute to the CAPM equation for evaluating the performance of stocks. Eugene Fama and Kenneth French developed the Fama-French three factor model that described value and sized to be the most important factors outside of market risk to explain the return of publicly traded stock. Bhavna Bahl(2006) studies the Fama-French model of stock returns along with its variants for 79 stocks on BSE100 for India. She concluded that factor portfolios SMB and HML are better in explaining the asset returns than one factor CAPM. Vanita tripathi(2008) and Yash pal Taneja(2010) also studied various Indian companies and concluded that the size and value variables do improve the traditional CAPM. Even internationally, Palo Rogers(2007) conducted tests on portfolios for the Sao Paulo stock exchange. Veysel Eraslan(2013) have also validated the model for the Istanbul Stock Exchange. Faff(2001) tests the model in the Australian stock exchange using sheff Index. Results support the three factor model. S.A.P.M 1 The use of OLS to find the betas of the three factors in the model has been used to calculate the return of stocks. To make the model more robust quantile regression can be......

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...from momentum? Do we need a table of contents? Do we need to copy the excel sheets into the paper ? or just copying the numbers as we did so far? 1. Best suitable model to measure Performance and Investigation of the different investment strategies Investigate the different investment style Pvalues anything remarkable What does this imply for our problem statement 2. Spread Portfolio (ziloy) compare the performance spread active –passive what does the beta tell us alpha etc why the r square does not matter 3. Investigation whether funds are invested in the right investment style based on the styles stated in the in the spreadsheet (Veronika) Aggressive Growth – High coefficient SMB / negative HML Growth - Postive SMB / negative HML Income - Growth/Income – Small cap – High coefficient SMB 4. Impact on Market Efficiency, Investment advice considering the impact of costs (Jessica/Miro) Hot hand effect Survivorship bias Appendix Single Factor model E(rpassive) – rf = α+β RMarket+ ε R = E - rf Passive | Excess Return | Standard Deviation | Alpha | Market | R2adj | SSGA S&P 500 index | 1,0 | | 0,07 | 1,00 | 0,95 | FIDELITY Spartan index fund | | | 0,04 | 1,01 | 0,94 | DREYFUS S&P 500 index fund | | | -0,02 | 1,01 | 0,95 | VANGUARD 500 index funds | | | 0,00 | 1,01 | 0,95 | BGI index funds | | | 0,09 | 1,01 | 0,95 | All funds | 1,17 | 3,10 | 0,00 | 1,01 | 0,95 | E(ractive) – rf =......

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...these stocks on the basis of total market capitalization at the beginning of each year and then grouped them into equally weighted deciles. Within each decile, the following time-series regressions were run: Rit= α0+ α1RMRF+ Jt+ εit (1) Rit= α0+ α1RMRF+ Jt+α2SMB+ α3HML+ εit (2) where Rit = monthly rate of return to decile i in month t RMRF= premium between market return and risk free rate Jt = dummy variable taking a value of 1 if t is January and 0 otherwise SMB = return of small stocks minus return of large stock HML = return of value stock minus return of growth stock portfolio The coefficient on the variable Jt measures the difference between the average return for deciles in January and average returns in the other months. The SMB measures the excess return of small caps over big caps. The HML measures the excess return of value stock over growth stock. Results Table 1. Test of January effect: Rit= α0+ α1RMRF+ Jt+ εit | | D1 | D2 | D3 | D4 | D5 | D6 | D7 | D8 | D9 | D10 | EW | R² | 0.6196 | 0.7317 | 0.8015 | 0.8211 | 0.8720 | 0.9046 | 0.9228 | 0.9449 | 0.9620 | 0.9683 | 0.8549 | α0 | 0.0555 | 0.1187 | 0.2227 | 0.2926 | 0.2976 | 0.3433 | 0.3619 | 0.3595 | 0.3279 | 0.3281 | 0.2708 | α1 | 1.4331 | 1.3831 | 1.3300 | 1.2484 | 1.2359 | 1.2035 | 1.1520 | 1.1057 | 1.0635 | 0.9253 | 1.2080 | ......

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