## What will we cover?

In this lesson we will learn about the **CAPM** and how to calculate it.

The objectives of this tutorial is:

- Understand the
**CAPM**(Capital Asset Pricing Model). **Beta**and**CAPM**calculations.**Expected return**of an investment.

## Step 1: What is the CAPM?

The CA**P**M calculates the relationship between systematic risk and expected return. There are several assumptions behind the **CAPM** formula that have been shown not to hold in reality. But still, the CAPM formula is still widely used.

The formula is as follows.

## Step 2: Get some data to make calculations on

Letâ€™s get some data and calculate it.

```
import numpy as np
import pandas_datareader as pdr
import datetime as dt
import pandas as pd
tickers = ['AAPL', 'MSFT', 'TWTR', 'IBM', '^GSPC']
start = dt.datetime(2015, 12, 1)
end = dt.datetime(2021, 1, 1)
data = pdr.get_data_yahoo(tickers, start, end, interval="m")
data = data['Adj Close']
log_returns = np.log(data/data.shift())
```

Feel free to change the tickers to your choice and remember to update the dates to fit your purpose.

## Step 3: How to calculate CAPM with Python (NumPy and pandas)

The calculations are done quite easily.

Again, when we look at the formula, the risk free return is often set to 0. Otherwise, the 10 years treasury note is used. Here, we use 1.38%. You can update it for more up to date value with the link.

```
cov = log_returns.cov()
var = log_returns['^GSPC'].var()
beta = cov.loc['AAPL', '^GSPC']/var
risk_free_return = 0.0138
market_return = .105
expected_return = risk_free_return + beta*(market_return - risk_free_return)
```

Notice, you can calculate it all simultaneously.

## Want to learn more?

This is part of a 2.5-hour full video course in 8 parts about Risk and Return.

This was the last lesson. In the first lesson you Get started with Pandas and NumPy for Finance for Risk and Return.

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