Understanding Beta: How to Calculate It Using Python Regression
Hello
In today’s edition of Intrinsic AI, we’re diving into Beta Calculation—a fundamental concept in finance. Beta measures a stock’s sensitivity to market movements. If you’re into valuation, investment analysis, or risk management, understanding how to calculate beta using Python regression is a must-have skill.
So, let’s break it down step by step!
What is Beta?
Beta (β) quantifies a stock’s volatility relative to the market:
β > 1 → more volatile than the market
β < 1 → Less volatile than the market
β = 1 → Moves in sync with the market
β < 0 → moves inversely to the market
Mathematically, Beta is calculated using the formula:
β=Cov(Rs,Rm)Var(Rm)\beta = \frac{Cov(R_s, R_m)}{Var(R_m)}
where:
Cov(Rs, Rm) = Covariance between stock returns and market returns
Var(Rm) = Variance of market returns
Regression helps us estimate Beta by modelling stock returns as a function of market returns.
Step-by-Step: Calculating Beta Using Python
Step 1: Import Required Libraries
import pandas as pd
import numpy as np
import yfinance as yf
import statsmodels.api as sm
Step 2: Fetch Stock and Market Data
Let's take TCS (Tata Consultancy Services) as an example and compare it with NIFTY 50.
# Define stock and market index
ticker = 'TCS.NS'
market_index = '^NSEI' # NIFTY 50
# Download data
start_date = '2022-01-01'
end_date = '2024-01-01'
stock_data = yf.download(ticker, start=start_date, end=end_date)['Adj Close']
market_data = yf.download(market_index, start=start_date, end=end_date)['Adj Close']
Step 3: Calculate Daily Returns
# Convert prices to percentage returns
stock_returns = stock_data.pct_change().dropna()
market_returns = market_data.pct_change().dropna()
Step 4: Run Regression to Get Beta
# Add constant for intercept
X = sm.add_constant(market_returns)
y = stock_returns
# Perform OLS Regression
model = sm.OLS(y, X).fit()
beta = model.params[1]
print(f"Beta of {ticker}: {beta:.2f}")
Step 5: Interpret the Result
If β = 1.2, TCS is 20% more volatile than NIFTY 50.
If β = 0.8, TCS is 20% less volatile than NIFTY 50.
If β is negative, TCS moves opposite to the market.
Why is Beta Important?
✅ Helps in Portfolio Risk Management
✅ Essential for CAPM (Cost of Equity) Calculation
✅ Key in Valuation & Investment Decisions
Final Thoughts
Calculating Beta using Python is straightforward—it requires just a few lines of code! Whether you're a finance enthusiast or an investor, understanding Beta is a powerful tool for making better investment decisions.
Would you like me to cover leveraged & unleveraged Beta in the next post? Let me know your thoughts in the comments!
🔔 Stay tuned for more valuation insights!
Best Regards
Akash Pandey
Intrinsic AI