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PLEASE NOTE - I MADE AN ERROR IN THE VIDEO: you don't have to take the square root when calculating the correlation matrix, that was an error on my part sorry! This error didn't effect the overall results because the correlation matrix was for informational purposes in this video. Download stock data, construct a variance-covariance matrix and use Excel Solver to optimize portfolios. Video demonstrates how to randomize investment weights in order to graph the efficient frontier of a three-stock portfolio. Here's the link to the file I used: https://bit.ly/2ZIjMsk Important Notes: After posting this video I found out that it doesn't automatically refresh when you press F9, usually only up to a few thousand rows. /* These Excel Lambda functions calculate various portfolio statistics from a list of prices (P) sorted in ascending order (from old to new), and portfolio weights (w) */ /* Please note: */ /* 1) Expected values are not guaranteed values, they are based off of historical data */ /* 2) Results will differ with frequency (daily, monthly, etc) and number of observations */ /* 3) David W. Johnk assumes no responsibility or liability for any errors or omissions. This is provided in a "as is" basis with no guarantees of completeness, accuracy, usefulness or timeliness */ /* see my YouTube video; https://youtu.be/7CIkj9NIS0s */ periodicreturns = LAMBDA(P,OFFSET(P,1,0,ROWS(P)-1)/OFFSET(P,0,0,ROWS(P)-1)-1); expectedrets = LAMBDA(P,BYCOL(periodicreturns(P),LAMBDA(array, AVERAGE(array)))); stdevrets = LAMBDA(P,BYCOL(periodicreturns(P),LAMBDA(array,stdev.p(array)))); varrets = LAMBDA(P,BYCOL(periodicreturns(P),LAMBDA(array,var.p(array)))); excessrets = LAMBDA(P,periodicreturns(P)-expectedrets(P)); varcovarmatrix = LAMBDA(P,MMULT(TRANSPOSE(excessrets(P)),excessrets(P))/(ROWS(P) - 1)); correlmatrix = LAMBDA(P,varcovarmatrix(P)/mmult(transpose(stdevrets(P)),stdevrets(P))); portexpectedret = LAMBDA(w,P,sumproduct(w,expectedrets(P))); portstddev = LAMBDA(w,P,SQRT(MMULT(MMULT(w,varcovarmatrix(P)),TRANSPOSE(w))));

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