Quick script to compute CAPM-derived cost of equity

capm.py

import yfinance as yf
import numpy as np

def get_data(ticker, start_date, end_date):
    stock_data = yf.download(ticker, start=start_date, end=end_date)
    return stock_data['Adj Close']

def compute_beta(stock_prices, index_prices):
    stock_returns = stock_prices.pct_change().dropna()
    index_returns = index_prices.pct_change().dropna()
    
    covariance_matrix = np.cov(stock_returns, index_returns)
    covariance = covariance_matrix[0, 1]
    variance = covariance_matrix[1, 1]
    
    beta = covariance / variance
    return beta

def capm(r_free, r_market, beta):
    return r_free + (beta * (r_market - r_free))


stock_ticker = 'PLTR'  
index_ticker = '^GSPC'  # Yahoo calls the S&P 500 GSPC instead of SPX like everyone else normal
start_date = '2020-09-30'
end_date = '2023-01-01'
spx_return_10y = 0.1086 # 10 year 10.86% annual return per https://www.spglobal.com/spdji/en/indices/equity/sp-500/#overview
us10y = 0.042 # US10Y yield per FRED/St Louis Fed
stock_prices = get_data(stock_ticker, start_date, end_date)
index_prices = get_data(index_ticker, start_date, end_date)

beta = compute_beta(stock_prices, index_prices)
coe = capm(us10y, spx_return_10y, beta)
print(f"The beta of {stock_ticker} relative to {index_ticker} is: {beta} for a cost of equity of: {coe}")