QUESTION 3 PORTFOLIO ANALYSIS (35 Marks) Suppose you are considering an investment in a two-factor portfolio. You wanted to analyse the stock return data for five years from 21st August 2013 to 20th...

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QUESTION 3 PORTFOLIO ANALYSIS (35 Marks) Suppose you are considering an investment in a two-factor portfolio. You wanted to analyse the stock return data for five years from 21st August 2013 to 20th August 2018, of four companies listed on the New Zealand Stock Exchange (www.nzx.com). In any of the possible two-factor portfolio, the weight of each security in the portfolio will be 50%. The possible portfolio combinations are A&B; B&C; A&C; C&D; A&D and B&D. Four companies: 1. Abano Healthcare Group Limited (ABA) 2. Ryman Healthcare Limited (RYM) 3. Briscoe Group Limited (BGP) 4. Hallenstein Glasson Holdings Limited (HLG) Calculate stock returns for the above four companies for the period from 21st August 2013 to 20th August 2018. Stock return can be calculated using the formulae: Ri= (Pt - Pt-1)/Pt-1). To obtain the stock price data, go to https://finance.yahoo.com/quote/%5ENZ50/history?p=%5ENZ50 Required: 1. Determine, using the appropriate Excel function (see fx) the average monthly return, the standard deviation, and variance for each of the companies. (Use 60 month returns data and use Excel functions “Variance P” and “Standard Deviation P.”) (4 marks) 2. Determine, using the appropriate Excel function the covariance between securities A&B; B&C; A&C; C&D; A&D and B&D. (Use the 60 months returns data and use Excel function “COVARIANCE P.”) (4 marks) 3. Using two-factor portfolio equations, calculate the portfolio returns and risks (both standard deviation and variance) for the following portfolios: 1. A and B 2. B and C 3. A and C 4. C and D 5. A and D 6. B and D (12 marks) 4. By computing the ratio of select the best investment that you should undertake, assuming you ?? are a risk averse investor. Explain the rationale for your choice of investment. (4 marks) 5. Draw a portfolio graph (showing Risk on the X-axis and Return on the Y-axis) for the investment portfolio that you have chosen in (4) above for a range of investment weights that you could choose from (i.e., You could invest 0% in one company and 100% in the other or 5% in one company and 95% in the other and so on). Determine from the portfolio graph, the minimum risk that you could obtain for this portfolio and the respective weightings that should be invested in each of the securities in the portfolio. (6 marks) 6. Determine the betas for the four securities by regressing the returns of each of the company on the returns for the S&P/NZX50 Index (close) for the same period. The S&P/NZX50 index data can be obtained from https://finance.yahoo.com/quote/%5ENZ50/history?p=%5ENZ50 (Regression Calculation: Go to Data > Data Analysis and choose regression. The company returns constitute the Y input, and the index returns the X input. Alternatively, the “slope” found in f(x) also represents Beta. If Data Analysis does not appear, you can add the tool. It is available in Excel. Go to File > Options and choose Add-ins and go to Manage, then select click on the drop-down button and select Excel Add-ins, then click on Go. Select Analysis Toolpak and OK.). Explain what the values of the betas (the slope coefficients in the regression) indicate and discuss the factors that might explain the differences in the values of the betas of the four companies. (5 marks)
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Answered Same DaySep 10, 2020

Answer To: QUESTION 3 PORTFOLIO ANALYSIS (35 Marks) Suppose you are considering an investment in a two-factor...

Aarti J answered on Sep 12 2020
142 Votes
Regression
    SUMMARY OUTPUT - ABA
    Regression Statistics
    Multiple R    0.3057343911
    R Square    0.0934735179
    Adjusted R Square    0.0775695446
    Standard Error    0.0483844668
    Observations    59
    ANOVA
        df    SS    MS    F    Significance F
    Regression    1    0.0137592533    0.0137592533    5.8773688661    0.0185309335
    Residual    57    0.1334402277    0.0023410566
    Total    58    0.147199481
        Coefficients    Standard Error    t Stat    P-value    Lower 95%    Upper 95%    Lower 95.0%    Upper 95.0%
    Intercept    0.0019050748    0.0069834318    0.2727992308    0.7859934534    -0.0120790062    0.0158891559    -0.0120790062    0.0158891559
    X Variable 1    0.6272695824    0.2587395117    2.4243285392    0.0185309335    0.1091526473    1.1453865175    0.1091526473    1.1453865175
    Beta =    0.6272695824
    SUMMARY OUTPUT - RYM
    Regression Statistics
    Multiple R    0.5658063826
    R Square    0.3201368626
    Adjusted R Square    0.3082094392
    Standard Error    0.0425233405
    Observations    59
    ANOVA
        df    SS    MS    F    Significance F
    Regression    1    0.0485337439    0.0485337439    26.8404038521    0.0000030129
    Residual    57    0.1030693659    0.0018082345
    Total    58    0.1516031098
        Coefficients    Standard Error    t Stat    P-value    Lower 95%    Upper 95%    Lower 95.0%    Upper 95.0%
    Intercept    0.001097343    0.0061374832    0.1787936401    0.8587337345    -0.0111927551    0.013387441    -0.0111927551    0.013387441
    X Variable 1    1.1780906037    0.2273967058    5.180772515    0.0000030129    0.7227365546    1.6334446527    0.7227365546    1.6334446527
    Beta    1.1780906037
    SUMMARY OUTPUT - BGP
    Regression Statistics
    Multiple R    0.1389340648
    R Square    0.0193026743
    Adjusted R Square    0.0020974581
    Standard Error    0.0438171456
    Observations    59
    ANOVA
        df    SS    MS    F    Significance F
    Regression    1    0.0021539991    0.0021539991    1.1219082678    0.2939758909
    Residual    57    0.1094367081    0.0019199422
    Total    58    0.1115907072
        Coefficients    Standard Error    t Stat    P-value    Lower 95%    Upper 95%    Lower 95.0%    Upper 95.0%
    Intercept    0.0093483174    0.0063242208    1.4781769571    0.1448653354    -0.0033157162    0.022012351    -0.0033157162    0.022012351
    X Variable 1    0.2481872985    0.2343154241    1.0592017125    0.2939758909    -0.2210212449    0.7173958419    -0.2210212449    0.7173958419
    Beta =    0.2481872985
    SUMMARY OUTPUT- HLG
    Regression Statistics
    Multiple R    0.185130863
    R Square    0.0342734364
    Adjusted R Square    0.0173308651
    Standard Error    0.0766376918
    Observations    59
    ANOVA
        df    SS    MS    F    Significance F
    Regression    1    0.0118812781    0.0118812781    2.0229182354    0.160390596
    Residual    57    0.3347801405    0.0058733358
    Total    58    0.3466614186
        Coefficients    Standard Error    t Stat    P-value    Lower 95%    Upper 95%    Lower 95.0%    Upper 95.0%
    Intercept    0.0006647009    0.0110612792    0.0600925918    0.9522920893    -0.0214851286    0.0228145305    -0.0214851286    0.0228145305
    X Variable 1    0.5828923765    0.4098257201    1.4222933015    0.160390596    -0.2377694724    1.4035542254    -0.2377694724    1.4035542254
    Beta =    0.5828923765
    The beta values in regression are the estimated coeficients of the explanatory variables indicating a change on response variable caused by a unit change of respective explanatory variable keeping all the other explanatory variables constant/unchanged. Whereas correlations coefficient is the overall estimated value of correlation measuring the strength and the direction of the relationships between the response and...
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