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    Calculate the market (S&P 500) BETA with Python for any Stock

    What will we cover?

    In this lesson we will learn about market Beta with S&P 500 index, how to calculate it, and comparison of calculations from last lesson.

    The objective of the tutorial is:

    • Understand what market Beta tells you.
    • How to calculate the market (S&P 500) Beta.
    • See how Beta is related with Linear Regression.
    Watch lesson

    Step 1: What is BETA and how to interpret the value

    Beta is a measure of a stock’s volatility in relation to the overall market (S&P 500). The S&P 500 index has Beta 1.

    High-beta stocks are supposed to be riskier but provide higher potential return. While, low-beta stocks pose less risk but also lower returns.


    • Beta above 1: stock is more volatile than the market, but expects higher return.
    • Beta below 1: stock with lower volatility, and expects less return.

    The formula for Beta is Covariance divided by variance.

    This sound more scary than it is.

    The Beta on financial pages, like Yahoo! Finance, are calculated on the monthly price.

    Step 2: Get some historic stock prices with Pandas Datareader

    Let’s make an example here.

    import numpy as np
    import pandas_datareader as pdr
    import datetime as dt
    import pandas as pd
    from sklearn.linear_model import LinearRegression
    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())

    Where we notice that we read data on interval=”m”, which gives the monthly data.

    Step 3: Calculate the BETA

    Then the Beta is calculated as follows.

    cov = log_returns.cov()
    var = log_returns['^GSPC'].var()
    cov.loc['AAPL', '^GSPC']/var

    For Apple, it was 1.25.

    If you wonder if it is related to the Beta value from Linear Regression. Let’s check it out.

    X = log_returns['^GSPC'].iloc[1:].to_numpy().reshape(-1, 1)
    Y = log_returns['AAPL'].iloc[1:].to_numpy().reshape(-1, 1)
    lin_regr = LinearRegression()
    lin_regr.fit(X, Y)
    lin_regr.coef_[0, 0]

    Also giving 1.25. Hence, it is the same calculation behind it.

    Want to learn more?

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

    In the next lesson you will learn how to Calculate the CAPM with Python in 3 Easy Steps.

    12% Investment Solution

    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.

    Python for Finance: Unlock Financial Freedom and Build Your Dream Life

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