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Task 3: Regression Analysis Use regression analysis to estimate the single index model for MSFT. For input data, use monthly excess returns from January 1990

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Task 3: Regression Analysis Use regression analysis to estimate the single index model for MSFT. For input data, use monthly excess returns from January 1990 through November 2022. The Y-variable is the monthly excess return of MSFT and the X-variable is the monthly excess return of the S\&P 500 stock index (remember the definition of excess return and use the risk free rate to calculate it). Performing regression analysis using Excel's Data Analysis package; feel free to use any other software for this analysis. i) On the Tool menu, click Data Analysis. If Data Analysis is not available, load the Analysis ToolPak. How? In Excel - In File Manu click Options and click Add-Ins. At the bottom of the screen, chose Manage Excel Add-ins. Then, chose the Analysis TollPak and click Ok. ii) In the Data Analysis (under Data tab) dialog box click Regression and then click OK. iii) To run a simple (univariate) regression, in the Regression dialog box, you must specify the inputs. For Input Y Range, highlight the monthly excess returns of MSFT (including the column heading) as the Y-axis variable. For Input X Range, highlight the monthly excess returns of SP500 (including the column heading) as the X-axis variable. For a multivariate regression, highlight more than one variable. iv) Check the Labels box. v) Click OK. If you have questions at any stage, you can use the Help button on the dialog box to get more information about the options. Presentation of your work: Provide a one-page printout of the regression analysis results and a discussion of what you found. In the discussion please elaborate on what the coefficients of the regression mean and what they imply regarding the performance of MSFT as compared to the overall stock market. Please explain how you identified the highest monthly return and, more importantly, how did you test whether such a return was abnormal (show the model you used)

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