...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 Table 4...
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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, the...
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...2013. Throughout the paper, RBLK-RFR stands for the excess monthly return for BlackRock stock, RM-RFR stands for the market excess return, SMB stands for the return on Small Minus 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...
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...Case a) US Mutual Fund Performance Maastricht University | | | | School of Business & Economics | | | | Place & date: | Maastricht, 5th December 2013 | | | | Name, initials: | Müller-Wilmes, VEMCroughs, ZWFWang, WCGerdsen, BHARack, MD | | For assessor only | | ID number: | I6076025I6075042I6117368I6075829I6064576 | | 1. Content | | Study: | International Business Economics | | 2. Language structure | | Course code: | EBC | | 3. Language accuracy | | Group number: | 1 | | 4. Language: Format & citing/referencing | | Writing tutor name: | Mike Langen | | Overall: | | Writing assignment: | | | Advisory grade | | | | | Assessor’s initials | | Your UM email address: v.muller-wilmes@student.maastrichtuniversity.nl, m.rack@student.maastrichtuniversity.nl, wwc.wang@student.maastrichtuniversity.nl, z.croughs@student.maastrichtuniversity.nl, b.gerdsen@student.maastrichtuniversity.nl Table of Contents Everyone has to include at his part how we did it in excel and what the conclusion of his subpart implies for the problem statement Bas (introduction, and question 1) Introduction Descriptive Data, net of cost etc Are the returns percentage? What do we expect 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 ...
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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 results...
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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 Rcvs-Rf...
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...Financial Econometrics- Assignment 1 Name: Kwan Siu Pong Lecturer’s name: Jérémie Lefebvre 3 October, 2011 Introduction The January effect is a calendar-related anomaly in the financial market where financial securities tend to raise in January. It is a well known model to explain the anomalous behavior in security markets throughout the world. In the past January, stocks with small market capitalization tended to generate higher returns than stocks with greater market capitalization. It provides an opportunity for investors to generate a profit through buying stocks for lower prices before January and then selling them after their values rise. The January effect was first introduced by investor banker Sindey B. Wachtel in, or before 1942. Its reintroduction in 1972 had brought a greater impact than the initial article and further proved its existence. However, many people expected that this phenomenon would disappear quickly as investors would attempt to exploit it. Through different adjustments and investors’ behaviors, the effects of January effect were expected to reduce. In order to prove the existence of January effect again, Haugen and Jorion had made a study in 1996. They used a simplistic model to analyze the impacts of January effect before and after its reintroduction respectively. Interestingly, the result was not same as the expectation of the public. January effect still existed. To further verify January effect, we would introduce a more sophisticated pricing...
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...INDIVIDUAL / GROUP PROJECT COVER SHEET For Office Use Only Programme: ________________Batch: _________________ Semester:__________________Block ___________________ (if any):______________ Module Name: _____________________________________ Module Code: ______________________________________ Date of Submission: _________________________________ Due Date: _________________________________________ For Evaluator’s Use Only Instructions to Students: 1. Fill all the details clearly with ink / ball point pen. 2. Do not write on the back of this page. Comments: TOTAL MARKS % Evaluator’s Name Evaluator’s Signature Declaration: I / We hold a copy of this assignment / report if the original is lost or damaged I / We hereby certify that no part of this report / assignment has been copied from any other student’s work or from any other source except where due acknowledgement is made in the assignment / report. No part of the assignment / report has been written / produced for me / us by any other person except where collaboration has been authorised by the module leader concerned I / We am / are aware that this work will be reproduced and submitted to plagiarism detection software programs for the purpose of detecting possible plagiarism. Programme: ________________Batch: _________ Semester:__________________Block (if any):______ Module Name: _____________________________ Module Code: _____________________________ Date of Submission:...
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...ABC analysis (Inventory) In supply chain, ABC analysis is an inventory categorization method which consists in dividing items into three categories, A, B and C: A being the most valuable items, C being the least valuable ones. This method aims to draw managers’ attention on the critical few (Aitems) and not on the trivial many (C-items). Prioritization of the management attention Inventory optimization is critical in order to keep costs under control within the supply chain. Yet, in order to get the most from management efforts, it is efficient to focus on items that cost most to the business. The Pareto principle states that 80% of the overall consumption value is based on only 20% of total items. In other words, demand is not evenly distributed between items: top sellers vastly outperform the rest. The ABC approach states that, when reviewing inventory, a company should rate items from A to C, basing its ratings on the following rules: A-items are goods which annual consumption value is the highest. The top 70-80% of the annual consumption value of the company typically accounts for only 10-20% of total inventory items. C-items are, on the contrary, items with the lowest consumption value. The lower 5% of the annual consumption value typically accounts for 50% of total inventory items. B-items are the interclass items, with a medium consumption value. Those 15-25% of annual consumption value typically accounts for 30% of total inventory items. 1 The...
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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...
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...Firm Analysis Step 1 we choose the following two stocks: Netflix Sector: Services. Industry: Music & Video Stores. Industry info : In this industry there is a huge competition between all the companies however, the music and video industry is very far away from declining. Growth is always occuring to that industry new companies are always introduced and old companies struggle to stay in the market. Consumers never lose their apetite. However, technologies change new companies get in and old get out. but in my point of view the industry will always be profitable. Netflix is an example of an expanding company in an expanding industry for various reasons: Since Netflix is still getting revenues despite of the fierce comptetion in the movie sector. They still have a growing base of customers and consumers. Netflix did also manage in creating the new movie/tv shows rentals mentality after shifting from sending DVDs through mail into unlimited streaming. And now netflix expanded even more and began producing their own shows. That growth in their profit and Netflix's ability to adapt with the the market and consumer habbits netflix is still far away from stagnation. Sprint Sector: Technology. Industry : Wireless Communications. We need to fill this Step 2 we went to the Wharton Research Database Services (WRDS): http://wrds.wharton.upenn.edu/ and downloaded the following data for our two stocks: • ACT = current assets, total; • LCT = current liabilities...
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...Overview Momentum is a phenomenon shows that well-performed stocks continue to outperform their peers while poor-performed stocks continue to underperform. Thus, more mutual funds use this powerful strategy to draw a broad range of investors by getting higher risk-adjusted returns. AQR is a hedge fund based in Greenwich, Connecticut, offering investing products that applies price phenomenon known as momentum. This case study enables investors to get a closer look at AQR’s momentum fund. Comparison of momentum specifications In order to analyze the momentum effect of different specifications, stocks were divided into ‘winner’ stocks and ‘loser’ stocks according to their rankings. From the data we can see that the decile spread portfolio return is the highest among these momentum specifications. One may argue that the spread between decile 10 and decile 1 is largest and it also has the highest volatility. After adjusting for volatility (using Sharpe Ratio), the decile spread momentum advantage is reduced but still very significant. According to Figure 1.1 and 1.2, raw spread returns witness a sharp decrease as the chosen percentile of highest and lowest stock return increases. After adjusted by volatility, the difference becomes flatter but still significant. From the graphs we can see that the volatility-adjusted return of decile spread is higher than the UMD spread, which means buying top 10% winner stocks and shorting bottom 10% loser stocks earns more profit than buying...
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...Question 1 For 4 Dow Jones stocks for a 15 year period, compute quarterly realized betas from daily data. Find firm specific and macroeconomic variables that help explain quarterly beta. Answer 1.1 Factors Introduction Factor model survey the sensitivity of a stock return as a function of one or more factors. There are single-factor and multi-factor models. In factors model, based on the type of factors used, it can be classified to economic and fundamental factor models. Economic factor models use macroeconomic and financial markets variables as factors, while fundamental factor models use firm-specific microeconomic variables, such as financial indicators. In recent research shows that the change in macroeconomic factors could be reflected in the change of systematic risk which impacts a stock’s expected return (Humpe & Macmillan 2007). Macroeconomic factors included industry production index, CPI, GDP, unemployment rate, inflation rate, risk premium, default premium, business cycle index and so on. From Chen (1986) notable study which uses variables include industrial production, inflation, risk premium, term structure, market index, consumption and oil prices to found out that industrial production, unanticipated change in the risk premium, unanticipated inflation, and, a slightly weaker, the unanticipated change in term structure, are the most important factors affecting expected stock returns. The 15 macroeconomic variables used as factors in our model are...
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...Warren Buffett indicator Group 13 Members: Zerui Yang Qingjin Wa Szeyeung Chan Junping Zhang Introduction = ∗ Introduction 1. One factor model: S&P500~Market Cap/GDP 2. Four factor model: S&P500~Market Cap/GDP+Unemplyment rate +CPI+Gold Price 3. Three factor model S&P500~Market Cap/GDP +CPI+Gold Price 4. Tests • Normality • Non-linearity • Multicollinearity • Other diagnostic(leverage, outlier) 5. Benchmark • CAPM • FF 3 factor model • Market Cap/GNP S&P500 INDEX~US Market Cap/GDP S&P500 Return~Market Cap/GDP Correlation of Market Cap and S&P500 Regression SP500~BUFFET Indicator One factor model > summary(fit1) Call: lm(formula = spreturn ~ usratio) Residuals: Min 1Q Median 3Q Max -0.13343 -0.01206 0.00430 0.01842 0.06994 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.006738 0.002406 2.800 usratio 0.347598 0.051016 6.814 0.00568 ** 1.48e-10 *** --Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.03197 on 175 degrees of freedom Multiple R-squared: 0.2097, Adjusted R-squared: 0.2051 F-statistic: 46.42 on 1 and 175 DF, p-value: 1.475e-10 ACF of residuals One factor model after fitting residuals auto.arima(residuals(fit1),ic="bic") Series: residuals(fit1) ARIMA(0,0,1) with zero mean Arima(x=spreturn,order=c(0,0,1),xreg=cbind(usratio,ur, cpi,gold)) Non-linearity ...
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...Journal of Banking & Finance 35 (2011) 2217–2230 Journal of Banking & Finance journal homepage: www.elsevier.com/locate/jbf Liquidity and asset pricing: Evidence from the Hong Kong stock market Keith S.K. Lam ⇑, Lewis H.K. Tam Department of Finance and Business Economics, Faculty of Business Administration, University of Macau, Av. Padre Tomas Pereira, S.J. Taipa, Macao, China a r t i c l e i n f o a b s t r a c t This study investigates the role of liquidity in pricing stock returns in the Hong Kong stock market. Our results show that liquidity is an important factor for pricing returns in Hong Kong after taking well-documented asset pricing factors into consideration. The results are robust to adding portfolio residuals and higher moment factor in the factor models. The results are also robust to seasonality, and conditional-market tests. We also compare alternative factor models and find that the liquidity four-factor model (market excess return, size, book-to-market ratio, and liquidity) is the best model to explain stock returns in the Hong Kong stock market, while the momentum factor is not found to be priced. Ó 2011 Elsevier B.V. All rights reserved. Article history: Received 10 June 2010 Accepted 17 January 2011 Available online 22 January 2011 JEL classification: G12 G15 Keywords: Liquidity Asset pricing Hong Kong stock market Factor model Fama French three factors Higher moment Momentum 1. Introduction Investors face liquidity risk when they transfer...
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