All language subtitles for 3. Beta ratio computation (CAMP metric)

I have run and one coming this new with you.

In this video, we will talk about the beta of an asset or portfolio.

The beta is the statistic that indicates the relationship between the variation of portfolio, trading

strategy, asset, etc. and dose of market, which is represented by an index.

In our case, we're going to choose the S&P 500 when we have computed the beta.

There are two possible case.

The first, the absolute value is below to one.

So the absolute value is just the value we vote the same.

For example, the absolute value of minus three is free of minus one is one of minus two is true, etc.

So if the absolute value of the beta is below one, it is good because it means that your portfolio

of less variation than the index.

For example, if we have a beta equal to zero point nine, it means that if the index of our of one

person's the portfolio varies by zero point nine in average, and if the absolute value of the beta

is above one, it's not a good thing accepted for some strategy.

Very specific, but usually we don't want to have a beta two higher because it means that if there is,

for example, or crisis or a very systemic impact, your portfolio will have a very big difficulties

and you will lose a lot of money.

So let me show you how to compute a beta.

First, we need to import the data for the S&P 500, so to do it, we do exactly the same thing as before.

So the symbol for the S&P 500 is this one.

So if you don't know how to find the symbol of an asset, I ring by you to see the video, how to import

data in the chapter imported data,

then we want that adjusted gross price and we want the variation of this asset

here.

We don't need to put a drop any because we are going to put it later.

But if you put it, now is not an.

We change the name of the S&P 500

and then we are CONCATENATE, their regional series containing also with aging and the returns of the

market.

So to do it.

We are going to use the conquered function.

From fundus.

And now we drop all the missing value.

Using this data frame, we're going to compute the various components metrics, so it is a very complex

term for a very easily thinks this matrix is just a table containing the covariance between the S&P

500 and our liturgy and the variance of also 2G and the variance of the S&P 500.

So the covariance of the S&P 500 and also 2G is just the relationship between the two time series.

So.

To compute the variance coronae on this matrix, we're going to use the curve function from number.

Then we transform all those that frame into and narrow to give.

And now I turn them away because Nampai works with Aura usually.

And we just need to specify the parameter which is raw power, and we need to put as false because all

I will show you, it's really better.

All that frame contain thousands of row and just two colors.

If we do the covariance on the row, it means that we're going to have a covariance matrix between all

the data and all, not with all the assets, because we're going to compute the Koreans by zero.

So by the day.

And here we want the covariance between all times to is, which are the region of uncertainty and the

region of the market.

So we need to put all of our equal force.

Then we can compute the covariance.

Which is.

This value.

Because we need to imagine that here we have.

On this call and the return of our strategy on this war, the return of us 40g So this number is the

governments between the return of the strategy and the return of the strategy.

So is the variance of the regional strategy.

This coefficient is at the intersection between the return of the strategy and the return of the markets.

So it means that will be the covariance between the markets and also teaching.

So it is exactly the same coefficient as Hugh because variance covariance matrix is a symmetric matrix.

So it means that this value and this value are always the same.

And here we are at the intersection between S&P 500 and S&P 500.

So this is the variation of the market.

So that is why we need to take route zero and column one to have the covariance between these two.

It so it's then we need the variants of the market, so we're going to use this word you.

And then we can compute our bitter dividing the governments of the regional strategy and the region

of the markets.

By the variants of the market.

And we can print it using f string.

And we don't need to.

Annualize a beta and oral strategy, so actually, Google has a beta in the past around 1:00, so it

means that this assists in average bear is of zero point ninety six percent when the market variance

of one percent.