s. Using the estimated regression equation for estimation and prediction Market
ID: 3311062 • Letter: S
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s. Using the estimated regression equation for estimation and prediction Market model is a term used in finance to describe a linear regression model in which the dependent variable is the return on a stock and the independent variable is the return on the overall market. The market model is sometimes extended to includse other independent variables-for example, the returm on a specific industry sector. Company A is one of the leading software companies in the world. Suppose an analyst in an investment bank is creating a market model to predict returns on Company A stock from both market and industry returns. The multiple regression model is where y daly returns for Company A ock " xi daily returns for the Dow Jones Industrilal Average xdaily returns for the NASDAQ Computer Index Returns for the Dow Jones Industrial Average (DJIA) will indicate market retuirms, while those for the NASDAQ Computer Index (NCI) will indicate industry retums. The analyst estimates the parameters fe. . and B2 using daily returns for the period January 3, 2005, through December 30, 2005. The estimated multiple regression equation is: y " 0.0006 + 0.6404 + 0.5869x2 The coefficient 0.6404 in the estimated multiple regression equation is O The estimated change in average Company A stock retum for a one-unit change in DJIA return, keeping the NCI returm constant O The estimated increase in average Company A stock return when the DIIA and NCT returns change by one unt O The estimated change in average Company A stock returm for a one-unit change in NCI return, keeping the DJLA retum constant O The estimated average Company A stock retum when the DJIA and NCI returns are zero Based on his study, the analyst expects an upturn for the overall market and the computer industry. He expects a 2%-0.02 return for the DnA and a 2% , 0.02 return for the NC. when the DHA return is 2% and the NCt return is 2%, the avrage Company A stock return for all trading days is estimated to be and the predicted Company A stock returm for one specific trading day is This predicted returm has the average return Using computer software, the analyst generates 95% confidence intervals and 95% prediction intervals for return values of the DIIA and the NCI. The intervals are shown in the following table; different Table of 95% Confidence Intervals and Prediction Intervals Confidence Interval Prediction Interval Lower Limit Upper Limit Lower Limit Upper Limit 14.37% 1378% 13.04% -12.88% -13.64% , 12.88% -11.84% -11.53% 13.02% a 11.94% -10.23% -9.61 % 13,08% %22% 10.00% 12.01% 13.22% 9.77% 10.39% 12.09% 13. 16% 11.71% 12.00% 13.04% 13.80% 13.05% -4.69% "5.35% 0.91% 4,32% 6.23% -0.07% 0.09% 3.21% 5.16% 3.73% 3. 15% 2.75% 3.93% 6.08% -2% 2% -3.80% -259% -2.96% -3.54% 5.04% 3.09% 0.06% 0.26% 1% 1% 1% 1% 1%Explanation / Answer
The image is too blurred. You can repost again. Below is the procedure for regression in excel.
The data can be fed into excel, where then under the data tab above, regression option can be found wherein you can do multiple regression at once, along with a published report of standard errors, R coefficient etc. Also, 95% confident intervals can be calculated as once we have the standard errors, then intervals are simply +-1.62*Se from the mean is the interval.
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