Welcome to the final step seven of our project.
Even if we were able to explain Marilee portfolio diversification effect and to convince her to hold
a more diversified stock portfolio.
We were not able to convince her to hold a widely diversified portfolio but at least she's willing to
add some more sectors to her portfolio.
And that's pretty typical when dealing with private investors who have their own mind and who are not
easy to handle.
In these cases that might not be the best idea to force them into portfolios that are maybe optimal
from an adviser view but that are simply not accepted by the client.
And it always depends on the client's worth compared to the client's lifestyle.
So if the client's portfolio is large compared to the costs of living then more individual declined
requests can be accepted and less needs to be moderated by the adviser.
And that's the case here with Maria.
So she has a pretty large portfolio compared to her living costs.
And finally we agreed to with Maria to add sectors that showed a positive.
I find the most recent five year period and again that's no guarantee that the positive IFR will persist.
Also in the future and also Maria expects some market turbulence in the near future and therefore it's
appropriate to select more defensive sectors containing low market risk and these sectors have a beta
of less than 1.
So as a summary we as advisors can live with the outcome Sierra of the first advisory round even if
there's no guarantee that positive alpha will persist but at least that's pretty likely that the low
beta stocks and sectors remain low paid stocks and sectors in the future.
And again there's only one guarantee or one free lunch in the investment market.
It's the diversification effect.
So in Step 7 here we have to identify sectors with positive alpha and a beta effect below 1.
And as a market portfolio we should use the S&P 500 Total Return Index.
And with this we have to calculate the following metrics annualized risk and return.
Then the sharp ratio than the annualized total risk and variance units are the systematic risk the and
systematic risk the beta the cap m return and the alpha in nexus and also for the market portfolio.
And we have still imparted our returns data frame with the daily returns of our eleven indexes here.
And also we have still important the normalized prices of our indexes.
So here we are and then we import the S&P 500 Total Return Index price data from the UCSC file S&P 500
Total Return and we are only interested in the close column so let's import unsafe the data and the
variable S&P 500
and actually here for our 11 indexes we have price and return data for the most recent five year period.
And therefore we can re index the S&P 500 data frame by the index or the timestamps that we have in
our index this data frame.
So we re index the S&P 500 by the timestamps for the day time index of the index data frame and VOA.
Right.
The S&P 500 data frame and then we can also calculate that daily returns.
So let's have a look here.
So that's the daily returns of the S&P 500 Total Return Index from 250 into to 18.
And finally we can also add another column to our returns data frame.
The column S&P 500 with the returns of the S&P 500 Total Return Index and let's have a look here.
So here on the very right hand side we have also the daily returns of the market portfolio the S&P 500
and then the next slide we calculate annualized risk and return for the eleven indexes and the S&P 500
index so here we are and then our next step we create the new column with the sharp ratio and we also
calculate the total risk and variance units by actually squaring the risk in standard deviation units.
So this is here.
Nothing new
and then the next step we create the covariance matrix with annualized core variances
so let's have a look at our covariance matrix here
and on the very right hand side we have the core variances of the indexes with the market portfolio.
And this is actually the market risk or systematic risk.
So here in the next step we can create the new column systematic risk and variance units by taking here
the very last column here of our covariance matrix
and then we can also calculate the end systematic risk for our eleven indexes and for the market portfolio
by subtracting the systematic risk from the total risk and let's have a look here at our summary data
frame.
So here we have annualized the risk return the shop ratio or total risk systematic risk and and systematic
risk and then we can also calculate debate affect which is the systematic risk normalized by the systematic
risk or the variance of the market portfolio
then we can calculate the cap m return which is the return of the risk free asset plus the return of
the market portfolio minus the risk free asset multiply it with uh the beta factor and then finally
we can calculate the alpha which is actually the actual return of our indexes the minus the cap m return
so let's have a final look here on our summary data frame with a beta cap m return and Alpha and now
we want to filter all sectors that meet the two conditions so the alpha must be positive and the beta
must be below 1.
So both conditions must be met and therefore we combine both conditions with then sign and uh finally
we feel or we our summary data frame with the local operator.
So let's have a look here.
And here we have the four industries that are short form Mary's portfolio.
Still we have the healthcare sector and also we have public utilities consumer durables and basic industries.
So all sectors have a beat off below 1 and a positive.
I find the most recent five year period we have finished with the final project.
I hope you enjoyed it and I'm looking forward to see you also in part 4 by.