In the portfolio optimization problem discussed in class, we minimized the portfolio's standard deviation subject to a constraint on the portfolio's expected return. Here we will require the portfolio's expected return to be at least 12%. In the table below are the closing prices of four stocks on the last Friday of October in each of the last six years (taken from finance.yahoo.com). Stock1 is General Motors (GM); Stock2 is Bank of America (BAC); Stock3 is Merck (MRK); and Stock4 is Starbucks (SBUX). Date Stock1 Stock2 Stock3 Stock4 10/28/2016 31.32 16.68 56.11 53.53 10/27/2017 44.64 27.80 55.53 54.88 10/26/2018 32.65 26.39 67.13 58.07 10/25/2019 36.74 31.72 78.44 83.38 10/30/2020 34.53 23.70 71.71 86.96 10/29/2021 54.43 47.78 88.05 106.07 Note that there are 4 stocks here instead of just 3. First, copy the above table into the "RawData" sheet and calculate the annual returns, means and standard deviations, and all of the pairwise correlations among the stocks. Then carefully expand the model to include Stock4. Pay close attention to the covariance C and the X matrices, which must be square, i.e., 4 rows x 4 columns. Then get Solver to find the new optimal solution