## Introduction¶

This is the part 3 of our Python for Stock Market Analysis series and here, we will explore some of popular growth rates that can be used to see how well is our value is changing over the period of time. Lets take some of scenarios:

• If we want to know by what rate is our current month's closing price is changed compared to the previous, we could simply divide change of values by the values at base month.
• If we want to know the compounding change rate of our closing price compared to the base period.
• We want to predict how much will the growth rate be on the next month or to achieve the constant growth rate, what should be the value.

The scenarios can be many more but lets focus on some.

Again we will be using the data reading part's code from the previous blogs.

## Rate of Return¶

Lets suppose that we bought a stock 2 months ago and we want to find out how much profit we currently have then we might subtract the price at the time we bought from the current price. And it can be simply called return. The rate of return is simple measurement that tells us how much has been the price increase from the base period. It is calculated as:

$$ror = \frac{V_{current}-V_{initial}}{V_{initial}} * 100$$

RoR is the simplest growth rate and it does not take external factors like inflation into consideration.

## Month Over Month (MOM) Growth Rate¶

This is the simple measurement of the growth rate where we simply calculate the rate of change from the previous month.

$$rate = \frac{v_t - v_{t-1}}{v_{t-1}} * 100$$

Where,

• v(t) is value at month t.
• v(t-1) is value at month t-1.

Lets calculate this in our python. But first, lets make a dataframe to store the closing price of the month only.