Welcome to the final conclusion covering actually the last three sections.
And in the last couple of videos we have seen that there are severe pitfalls when forecasting future
portfolios and what we did in the last videos is also quite a mean variance optimization and actually
Modern Portfolio Theory asset pricing models and the mean variance optimization are one of the most
misunderstood concepts in finance because typically they are explained and derive with the forecasts
or expectations in a forward looking scenario.
And this is done because typically you require research for model assumptions to derive these models
and these assumptions only make sense and forward looking scenarios.
However we have seen that explaining these theories and models with past performance works as well and
it's even more intuitive as we didn't need any of those unrealistic assumptions.
So we simply analyzed past data and tried to find some patterns in the past data.
And this worked pretty good actually.
Again these theories and models are typically explained in a forward looking scenario.
However it is actually a rarely used for real world asset allocation problems because there are severe
pitfalls.
Well at least they are not used in their simplest form.
So let's have a look now at the pitfalls and we have pitfalls.
Number one.
So forecasting the future is always trickier.
And actually the outcomes of the mean variance optimization are highly sensitive to the inputs.
So the general rule garbage in garbage out also applies here.
And first of all it's hard if not impossible to forecast the future stock returns then we used the past
variances and covariance also for future predictions.
However the variance of some covariance is are not stable over time.
So this is a simplification.
And to sum it up forecasting the future is no exact science.
And we've also seen that that mean variance optimization in a forward looking scenario typically leads
to highly concentrated positions and portfolios.
So we have a high concentration and only a few stocks or assets and we have zero allocation to many
other stocks or assets so actually the target of the mean variance optimization is to create diversified
portfolios.
However optimal in a mean variance framework does not necessarily mean practical diversification
and we've also seen that the most sensitive factor are actually the forecasts that returns and again
it's hard if not impossible to forecast the stock returns and actually small adjustments in our forecasts.
Predictions that lead to large changes in allocations and weights and if we were to react to small adjustments
and rebalance or adjust the allocations then uh the possible efficiency gains by having a more efficient
portfolio.
So of course there's actually no guarantee to get a better portfolio but uh the potential efficiency
gains are typically offset by costs and taxes that become due when we heavily adjust and rebalance.
The allocations in our portfolios.
So excessive trading our turn over in our portfolio is in many cases not a good idea because potential
positive effects are typically offset or more than offset by costs.
And Texas
so let's have a look at portfolios of institutional investors like pension funds insurances and so on.
And typically they are highly constrained.
So there are regulatory or legal constraints and typically they're very restrictive limits on the allocation
or rates of asset classes or assets.
So for example a pension funds are insurances have had limits for stock so for example 15 percent only
so these investors pretty much have predetermined buckets.
Let's say 15 percent stocks their off maximum 5 percent foreign stocks and at least the 60 percent high
quality is some rain and corporate bonds.
And that's a typical workflow when evaluating whether an additional asset class will be added and a
few years ago I advised that German insurance companies and pension funds on whether to add a new asset
class to the portfolio in this case that was aircraft backed mortgage loans and bonds.
And this is typically done with a backward looking portfolio construction or to say a backward looking
mean variance optimization.
And we actually added up to 5 percent of this new asset class to the past the portfolio and we created
the past the efficient frontier.
And in case that the efficient frontier moved significantly to the upper left corner then the asset
class will be added.
So let's have a look at an example here.
We know that graph already from the last video and this is actually forward looking.
But let's assume that the blue portfolios are past portfolios without the new asset class and the red
portfolios are also simulations of the past including the new asset class or maximum 5 percent.
And here we can see that the efficient frontier significantly moves to the upper left corner here.
So this is a significant difference here.
And consequently the new asset class will be added to the portfolio.
So that's a typical application of the mean variance optimization for institutional investors.
And again this is based on past performance and it's not about predicting the future all right and I
said before that the mean variance optimization is uh rarely used in its simplest form.
But uh there are some advanced approaches that can be observed in the market but they are definitely
beyond the scope of this course.
However at least I want to mention them here.
So we have a reverse optimization.
Then we have the black letter man model.
Then we have read sampling or Monte Carlo simulations and also adding constraints to our mean variance
optimization.
And in particular for individual investors that s also the concept of gold based investing.
And actually many more advanced concepts.
So explaining those advanced approaches that would take another 20 hours.
And uh the question is uh whether those advanced methods do guarantee better results.
And uh this is definitely not the case and the final question is here now what should I do with my stock
portfolio.
And at least for an individual investor it might be best to simply trust the market expectations.
So current stock prices that you can observe in the market are formed by the ever it's market expectations
or market consensus.
And if the market believes that the stock price is overpriced or will perform poorly in the near future
then the price would probably fall immediately.
So each stock price reflects the fair price of that stock based on market expectations.
And of course this does not necessarily mean that the market is right but uh it is probably the best
forecast that you can get.
And as a consequence the debates are proportions of each and every stock in your portfolio should be
determined by the proportional market capitalization.
And that means that you should simply hold and fund or an ETF that closely follows a proto market cap
weighted index.
So for example the S&P 500 index and this is actually the optimal forward looking stock portfolio based
on market consensus.
However this does of course not mean that this will be the optimal portfolio from an ex post perspective.
Actually that is pretty unlikely.
But as I said before it's still the best possible forecast you can get unless you know it better.
And finally this is the Perry toe principle which leads me to one of the first videos of this costs.
So typically 85 percent of the portfolio performance or the success of your portfolio are driven by
a few simple rules that can be actually explained in two minutes you might know this comparison already
on the left hand side.
We have an aggregation of the past performance of the S&P 500 index and on the right hand side the same
analysis for the T stock.
And from that comparison we can derive some simple rules.
So holding a diversified portfolio is better than holding a concentrated portfolio.
And the second rule is constructing an appropriate portfolio based on your individual investment horizon.
So with a short horizon of one or two years you should to better reduce the proportion in stocks and
to allocate your wealth to short term high quality bonds.
And on the other hand side if you have a long investment horizon then you will benefit from the time
diversification effect.
And as a consequence you should increase the proportion of stocks.
And finally as a third rule avoid excessive trading and rebalancing as this only results in costs and
Texas so that's my personal view and of course feel free to form your own opinion and make your own
analysis.
You are now equipped with all tools to create analyze and optimize portfolios for past portfolios but
also far forward looking portfolios.
And we are finishing now so thanks for watching the last sections and looking forward to seeing you
in the next section on interactive plots.