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 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.
Step 2: Get some data to make calculations on
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.
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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Would you like to get 12% in return of your investments?
D. A. Carter promises and shows how his simple investment strategy will deliver that in the book The 12% Solution. The book shows how to test this statement by using backtesting.
Did Carter find a strategy that will consistently beat the market?
Actually, it is not that hard to use Python to validate his calculations. But we can do better than that. If you want to work smarter than traditional investors then continue to read here.
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