Welcome to the first section on financial portfolios.
If you're not too much familiar with financial portfolios then he has a short definition.
So our financial portfolio is a collection of investments held by an investment company a financial
institution or an individual and actually an investment can be everything so it can be stocks bonds
funds real estate commodities and many more.
And in this course we will focus on stocks.
So how can we characterize their portfolio.
And.
First of all we have the assets are the investments that are included in the portfolio.
And second we have the votes of the portfolio assets.
And with this information we can analyze portfolios and also calculate the return and risk of portfolios.
And actually the performance of a portfolio is not simply the weighted average of the single investments.
So this holds true for the return but typically the portfolio risk is less than the evaded average risk
of the assets or investments included in the portfolio and that's the portfolio diversification effect.
And the major reason why an investor should hold diversified portfolios rather than single investments
are right.
And we have some implicit assumptions here in the section.
So we are working with a small subset of the total market and we are creating portfolios with only six
stocks.
For example we have Amazon and Microsoft.
And sometimes that might seem like we pretend that these are the only available stocks in the market.
So for example when we are going to search the best or the optimal portfolio then it's meant the best
portfolio that we can create with these six stocks.
And for these six stocks that we are analyzing the past performance.
So typically portfolio construction is in most cases a forward looking task to create appropriate portfolios
that are successful in the future but forward looking means that we have to make a lot of assumptions
involving quite a lot finance theory.
And that would actually take the focus from our pandas coding.
And that's why we simply analyze the past and the coding workflow so actually pretty much the same.
And once you have understand how to grade and analyze portfolios based on historical data it's only
one step further to go into the future and this section we are going to create many random portfolios
and analyze the performance of these portfolios and random means that we randomly selected the weight
of our six stocks saw for example and one random portfolio the weight of the Amazon stock is 20 percent
and in the other random portfolio the rate for Amazon is 60 percent.
And finally there's one assumption that is in real world to hard to maintain.
So we are analyzing our random portfolios over the last five years based on daily data and we assume
that the rates remain constant over time.
Typically at the end of each day we would need to sell winners and buy losers to maintain the weights
and the the explicit assumption is that we can do this without costs and taxes.
So this might not be a realistic and simplifying but this enables us to put the focus on the more interesting
things.
All right.
This is enough introduction and in the next video we start coding our portfolios.
So to see it there by.