In particular, we will see how we can run a simulation when trying to predict the future stock price of a company.

There is a group of libraries and modules that could be imported when carrying out this task.

But the good news is you have already used all of them.

Besides the classical numb pie in pandas, we will need norm from Sai Pie and some specific Matt Plant live features.

Once again, the company we will use for our analysis will be P and G.

The timeframe under consideration reflects the past 10 years, starting from January the 1st 2007 We want to forecast P and G's future stock price in this exercise, so the first thing we'll do is estimate its historical log returns.

There is a second way to obtain simple or log arrhythmic returns, and we will discuss it in more detail in the notebook document attached to this video.

The method will apply here is called percent change and you must write percent underscore change open and close parentheses to obtain the simple returns from a provided data set.

We can create the formula for long returns by using numb pies, log and then type one, plus the simple returns extracted from our data.

And here's a table with P and G's Log returns.

Awesome in the first graf weaken C, P and G's price, which has been gradually growing during the past decade.

In the 2nd 1 we plant the log returns, not the price of P and G.

The picture tells us the returns are normally distributed and have a stable, mean great.

Now let's explore their mean and variance as we will need them for the calculation of the brownie in motion we talked about in our previous lecture.

Remember, we already know how to calculate, mean and variance, don't we?

After a few lines of code, we obtained these numbers.

So what are we going to do with them?

First, I'll compute the drift component we studied in our previous lecture.

It is the best approximation of future rates of return of the stock.

The formula to use here will be you, which equals the average long return minus half its variants.

All right, we obtained a tiny number and that need not scare you because we'll do this entire exercise without annual izing are indicators.

Why?

Because we will try to predict P and G's daily stock price good.

Next, we will create a variable called Esti Dev and we will assign to it the standard deviation of log returns.

We said the Brownie in motion comprises the son of the drift and standard deviation of adjusted by E to the power of our.

So we will use this block in the second part of the expression.

Okay, we've set up the first brownie in motion element in our simulation.

In the next lesson, we will create the second component and we'll show you how this would allow us to run a simulation about a firm's future stock price.