How to Estimate Cost of Equity Using CAPM in Python

May 16, 2026

What’s the question?

The cost of equity is the return that shareholders require as compensation for bearing the risk of owning a stock. It is a critical input to discounted cash flow (DCF) models, capital budgeting decisions, and weighted average cost of capital (WACC) calculations. The Capital Asset Pricing Model (CAPM) provides a formula: Cost of Equity = Risk-Free Rate + Beta x Equity Risk Premium. The risk-free rate (Rf) represents the return on a riskless asset — typically the 10-year Treasury yield. Beta measures the stock’s sensitivity to market movements. The equity risk premium (ERP) is the additional return investors demand for holding equities instead of risk-free bonds. The current 10-year Treasury yield is approximately 4.3%, and the long-run ERP is conventionally estimated at 5.0–6.0%.

The approach

Select 10 stocks across risk profiles — from high-beta technology names to low-beta consumer staples. Estimate beta via ordinary least squares (OLS) regression of each stock’s daily returns against SPY over 3 years. Report the standard error of the beta estimate and R-squared to quantify estimation precision. Apply the CAPM formula with Rf = 4.3% and ERP = 5.5% to compute the implied cost of equity for each stock.

import xfinlink as xfl
import pandas as pd
import numpy as np
from scipy import stats

xfl.api_key = "YOUR_API_KEY"  # free at https://xfinlink.com/signup

# -- Configuration ----------------------------------------------------------
tickers = ["AAPL", "MSFT", "NVDA", "TSLA", "AMZN", "JPM", "XOM", "UNH", "PG", "JNJ"]
benchmark = "SPY"
rf = 0.043        # 10-year Treasury yield (annualized)
erp = 0.055       # equity risk premium (long-run estimate)

# -- Fetch 3Y daily returns -------------------------------------------------
df = xfl.prices(tickers + [benchmark], period="3y", fields=["return_daily"])
returns = df.pivot_table(index="date", columns="ticker", values="return_daily").dropna()

# -- OLS regression for beta estimation ------------------------------------
market = returns[benchmark]
results = []

for ticker in tickers:
    slope, intercept, r_value, p_value, std_err = stats.linregress(
        market, returns[ticker]
    )
    cost_of_equity = rf + slope * erp
    results.append({
        "ticker": ticker,
        "beta": slope,
        "beta_se": std_err,
        "r_squared": r_value ** 2,
        "cost_of_equity": cost_of_equity,
    })

rdf = pd.DataFrame(results).sort_values("cost_of_equity", ascending=False)

# -- Print results ---------------------------------------------------------
print("=== CAPM Cost of Equity Estimation ===")
print(f"Risk-free rate (Rf): {rf:.1%}  |  Equity risk premium (ERP): {erp:.1%}")
print(f"Formula: Ke = Rf + Beta x ERP = {rf:.1%} + Beta x {erp:.1%}")
print()
print(f"{'Ticker':6s}  {'Beta':>6s}  {'Beta SE':>8s}  {'R²':>8s}  {'Cost of Equity':>15s}")
print("-" * 46)

for _, r in rdf.iterrows():
    print(
        f"{r['ticker']:6s}  {r['beta']:>6.2f}  {r['beta_se']:>8.3f}  "
        f"{r['r_squared']:>8.3f}  {r['cost_of_equity']:>14.1%}"
    )

median_ke = rdf["cost_of_equity"].median()
min_ke = rdf["cost_of_equity"].min()
max_ke = rdf["cost_of_equity"].max()
print(f"\nMedian cost of equity: {median_ke:.1%}")
print(f"Range: {min_ke:.1%} to {max_ke:.1%}")

Output:

=== CAPM Cost of Equity Estimation ===
Risk-free rate (Rf): 4.3%  |  Equity risk premium (ERP): 5.5%
Formula: Ke = Rf + Beta x ERP = 4.3% + Beta x 5.5%

Ticker    Beta   Beta SE      R²   Cost of Equity
----------------------------------------------
TSLA      2.22     0.113   0.339           16.5%
NVDA      2.08     0.090   0.416           15.8%
AMZN      1.40     0.054   0.468           12.0%
AAPL      1.13     0.046   0.439           10.5%
MSFT      0.98     0.044   0.399            9.7%
JPM       0.90     0.044   0.363            9.3%
XOM       0.29     0.054   0.037            5.9%
UNH       0.28     0.088   0.013            5.8%
PG        0.15     0.041   0.018            5.1%
JNJ       0.05     0.041   0.002            4.6%

Median cost of equity: 9.5%
Range: 4.6% to 16.5%

What this tells us

The CAPM produces a 12-percentage-point spread in cost of equity, from 4.6% (JNJ) to 16.5% (TSLA). This spread directly impacts valuation: a DCF model using 16.5% discounts TSLA’s future cash flows far more aggressively than one using 4.6% for JNJ. The standard errors are informative — JNJ’s beta standard error (0.041) is nearly as large as its point estimate (0.05), indicating the beta is not reliably distinguishable from zero. Its R-squared of 0.002 confirms JNJ’s returns are almost entirely idiosyncratic — the market explains essentially none of its variation. For such stocks, the CAPM framework provides limited predictive value because the market risk factor is irrelevant.

So what?

When building a DCF model, the cost of equity is the most consequential assumption after the growth rate. Using a single “market average” discount rate for all stocks introduces systematic bias — overvaluing high-beta stocks and undervaluing low-beta ones. Estimate beta from at least 3 years of data, report the standard error, and use the Merrill Lynch adjusted beta (shrunk toward 1.0) for stocks with low R-squared to reduce estimation noise. For defensive stocks like JNJ and PG where market beta is near zero, consider supplementing CAPM with a build-up method that adds company-specific risk premiums.