In this lesson we will learn about the CAPM and how to calculate it.
The objectives of this tutorial is:
The CAPM 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.
Let’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.
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.
This is part of a 2.5 hour full video course in 8 parts about Risk and Return.
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