In this video we will do the same as in the last video simulating one million random portfolios.
But this time we were slightly wary.
The most sensitive fact on our forecast the expected return of our six stocks and actually forecasting
stock returns was a very difficult task and some experts would argue that you cannot forecast them at
all.
Nevertheless there exists a few approaches and methods how to make forecasts.
However this is clearly beyond the scope of this course and in the end it's not an exact science.
So variations of 2 3 or 4 percentage points that can happen easily.
And that's exactly what we'll do in this video.
So we were very aid each and every forecast that returned by 3 percentage points.
And then we will have a look what happens to the efficient frontier and to the max Sharpe Rachel portfolio.
And still we have implied that the summary constituents data frame and on the right hand side we have
the expected returns for our first trial and now it's the second round.
And we very late each expect that returned by three percentage points or families instead of having
25 percent.
We have 22 percent then for Boeing instead of having 15 we have 18 then for this NEI instead of 8 percent
we have 11 and also for IBM we have eleven instead of eight then for Coca-Cola we take 7 percent instead
of 10 percent.
And finally for Microsoft.
We predict that 12 percent instead of 15 percent.
So we created the new column
and one more time we calculate the expected return of one million portfolios by using here the DOT method
on the expected return to her column and we pass again the transpose the weights matrix and then the
next step the calculator the expected risk and also here we use the covariance matrix assuming that
the the variances then to the covariance is off for the past also persist in the future.
So even if we changed here the expected returns.
So the expected risks of our 1 million portfolios do not change.
So this remains unchanged.
And then we also calculate these sharp ratio and we create a summary to data frame with the columns
return risk and Sharpe Ratio
so here we have 1 million portfolios and again we can visualize them.
And this time we create the risk return cloud for our summary to data frame as well as for our first
trial with uh the different return forecasts.
So we have our initial portfolios in red color and now our new portfolios based on our revised the return
forecasts in blue color and let's have a look here
and here we can see the efficient frontier of our first trial and red and the efficient frontier based
on our revised the return expectations in blue.
And secondly a significant difference here.
So we only changed it's an area of return expectations by 3 percentage points.
But this makes actually a huge difference and we can also analyze our revised summary to data frame.
So the max Sharpe ratio has now a sharp ratio of four point eight two.
And we can also search the max Sharpe race for portfolio.
It's with the indexed label eight hundred twelve thousand four hundred fifty two.
And we can also select the max Sharpe Ratio portfolio.
So we have a return of sixteen point eighty three percent and a risk of 18 percent.
And we can compare this to our farmer Max drop rates or portfolio so the difference is actually not
too substantial.
So our farmer Max operational portfolio showed a return of sixteen point one.
And he and our revised the portfolio we have sixteen point eight.
And also the risk is here a slight slightly higher.
But to sum it up to a risk and return of our max operator our portfolio did not change substantially.
However let's have a look at the debates of our new Max Sharpe Ratio portfolio and that's create a panda
series.
So that's the weights of our new Max Sharpe Ratio portfolio.
And let's compare those weights to our farmer Max Sharpe Ratio portfolio and we can see that the weight
of Amazon decrease the slightly from 35 percent to 29 percent.
Then we can see that the weight of the Boeing stock increased substantially from 18 percent to 53 percent
and also the weight of the DNA and the IBM stock significantly increased from actually 0 or 1 percent
to 10 and 7 percent.
And in contrast that the weight off the Coca-Cola stock significantly decreased from 45 percent to 9
percent.
And finally for the Microsoft stock we can see only a little change in the weight but as a summary we
can say that to be only slightly changed our return expectations and also the performance of the max
Sharpe Ratio portfolio in terms of risk and return did not change substantially.
However the rates in our max Sharpe racial portfolio those changed substantially.
And that's another pitfall of using forecast that portfolios.
So the best are the optimal portfolios and the rates and the best and optimal portfolios are highly
sensitive to the return forecasts and this pretty much reduces the practical ability of this approach
here.
All right.
We are finished with this video and then the next video we will summarize the pitfalls of using a forecast
that portfolios for asset allocation.
So hope to see also there by.