How Cost of Equity Reveals Which Tech Giants Beat Expectations

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Think of the cost of equity as the “minimum tip” investors expect for taking on risk. Just like you’d expect a bigger tip for delivering pizza in a thunderstorm versus on a sunny day, investors demand higher returns for riskier stocks.

The formula is beautifully simple:

Cost of Equity = Risk-Free Rate + Beta × (Market Return — Risk-Free Rate)

But before we dive deeper, let’s decode these three magical ingredients…

🏛️ Risk-Free Rate: The “Safe Haven” Baseline

This is what you’d earn by parking your money in the safest possible investment — typically 10-year U.S. Treasury bonds. Think of it as your “lazy money” return… around 4% currently. It’s like the guaranteed minimum wage of investing — not exciting, but rock solid.

Why 10-year Treasuries? Because they’re backed by the U.S. government (they print money), making default virtually impossible.

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Market Return: The “Average Joe” Performance

This represents what the overall stock market delivers annually — we use the S&P 500 as our benchmark. It’s like the class average on a test. Some stocks score higher, others lower.

🎢 Beta: The Drama Queen Coefficient

And here’s where it gets spicy… Beta measures your stock movement compared to the overall market. It’s pure mathematical poetry:

• Beta = 1.0: Moves exactly with the market (the reliable friend)

• Beta > 1.0: More dramatic than the market (the emotional roller coaster)

• Beta < 1.0: Calmer than the market (the zen master)

Now here’s where it gets interesting… 👀

Real Examples from Our Data:

• NVDA (β=2.13): Swings more than twice as wild as the market — when the market moves 1%, NVIDIA typically moves 2.13%

• GOOGL (β=1.005): Almost perfectly mirrors the market — steady and predictable

• TSLA (β=2.40): The ultimate drama queen — nearly 2.5x more volatile than the S&P 500

This is why NVIDIA and Tesla were expected to deliver such massive returns (209% and 243% respectively over 5 years) — with great volatility comes great return expectations!

The Shocking Reality Behind the Numbers

Here’s where our analysis gets mind-bending. Let’s decode what those results mean:

Analyzing 8 tech stocks vs S&P 500 over 5 years...

Stock Performance Analysis:
Ticker  Beta Actual 5Y Return Annualized Return Market Return (5Y) Alpha (5Y) Annual Cost of Equity 5Y Expected Return (CAPM)  Outperformance
  NVDA 2.131          1520.8%             74.6%              92.4%    1428.4%                 25.3%                    209.0%            True
  TSLA 2.398           211.4%             25.5%              92.4%     119.0%                 28.0%                    243.3%            True
  META 1.284           208.7%             25.3%              92.4%     116.2%                 16.8%                    117.7%            True
 GOOGL 1.005           160.4%             21.1%              92.4%      68.0%                 14.0%                     93.0%            True
  MSFT 1.033           157.2%             20.8%              92.4%      64.7%                 14.3%                     95.3%            True
  NFLX 1.596           132.5%             18.4%              92.4%      40.1%                 20.0%                    148.4%            True
  AAPL 1.199            96.0%             14.4%              92.4%       3.6%                 16.0%                    109.9%            True
  AMZN 1.337            50.5%              8.5%              92.4%     -42.0%                 17.4%                    122.7%           False

High-Beta Outperformers:
- NFLX (β=1.60, α=40.1%, Actual: 132.5% vs Expected: 148.4%)
- TSLA (β=2.40, α=119.0%, Actual: 211.4% vs Expected: 243.3%)
- NVDA (β=2.13, α=1428.4%, Actual: 1520.8% vs Expected: 209.0%)

Underperformers:
- AMZN (β=1.34, α=-42.0%, Actual: 50.5% vs Expected: 122.7%)

The Superstar: NVIDIA

• Actual Return: 1,520.8% (yes, you read that right)

• Expected Return: 209.0%

• The Twist: NVIDIA still outperformed expectations by 1,428.4%!

NVIDIA crushed what the model predicted. That’s like expecting to find $20 on the street and instead discovering buried treasure.

🏆 The Consistent Champions

Microsoft and Google showcase something beautiful — steady, reliable outperformance:

• Both have moderate betas (around 1.0–1.3)

• Both delivered solid returns above expectations

• Both prove you don’t need extreme volatility to win

😬 The Plot Twist: Amazon’s “Failure”

Amazon, with a respectable 50.5% five-year return, is actually… underperforming?

With a beta of 1.34, we expected 122.7% market return for 5 years, making Amazon’s alpha a disappointing -42.0%. It’s like getting a B+ when everyone expected an A+.

How is Alpha Calculated?

Alpha = Actual Return — Expected Return (based on CAPM)

🔍 What This Means for Your Portfolio

This analysis reveals three crucial insights:

1. High Risk ≠ High Reward (Always)

Just because a stock is volatile doesn’t guarantee it’ll make you rich. Tesla’s beta of 2.40 suggested massive potential returns of 243.3%, but it “only” delivered 211.4%. Still incredible, but technically underperforming expectations.

2. The Sweet Spot Stocks

META, Google, and Microsoft found the perfect balance — delivering strong returns without excessive drama. These are the stocks that let you sleep at night while your portfolio grows.

3. Context Is Everything

A 50% return sounds amazing… until you realize the risk profile suggested it should have been 122%. This is why understanding the cost of equity is like having investment superpowers.

💡 The Code Behind the Magic: Step-by-Step Breakdown

Our Python analysis is like a financial detective that automatically uncovers the truth. Here’s how it works its magic:

Step 1: Setting Up the Financial Laboratory

RISK_FREE_RATE = 0.04  # 10-year Treasury yield (~4%)
MARKET_RETURN_ANNUAL = 0.14  # S&P 500 annualized return (5Y ≈ 14%)
YEARS = 5  # Analysis period

First, we establish our baseline assumptions — the “rules of the game” that every stock will be measured against.

Step 2: The Data Harvester

def get_stock_data(ticker, years=YEARS):
    data = yf.download(ticker, period=f"{years}y")
    data['daily_return'] = data['close'].pct_change()
    data['cumulative_return'] = (1 + data['daily_return']).cumprod() - 1

This function is our time machine — it grabs 5 years of daily stock prices and calculates how much money you would have made (or lost) each day. The cumulative_return tracks your total gains from day one.

Step 3: The Beta Detective

def get_beta(ticker):
    stock = yf.Ticker(ticker)
    beta = stock.info.get('beta', None)

Here’s where we discover each stock’s “drama coefficient.” Yahoo Finance has already calculated how volatile each stock is compared to the market — we just need to ask nicely!

Step 4: The Expectation Calculator

def calculate_cost_of_equity(beta, risk_free_rate=0.04, market_return=0.14):
    return risk_free_rate + beta * (market_return - risk_free_rate)

This is the CAPM formula in action! It’s saying: “Given this stock’s volatility (beta), here’s what investors should reasonably expect as a return.”

Step 5: The Performance Judge

def calculate_expected_5yr_return(beta):
    annual_coe = calculate_cost_of_equity(beta)
    return (1 + annual_coe) ** YEARS - 1

We compound the annual expected return over 5 years. This is like asking: “If this stock performed exactly as expected based on its risk, what would your total return be?”

Step 6: The Reality Check

alpha = stock_total - benchmark_total
'Outperformance': alpha > 0

Finally, the moment of truth! Alpha reveals whether the stock beat expectations (positive alpha) or disappointed (negative alpha). It’s the difference between what happened and what “should have” happened.

The Beautiful Result

When we run this on our tech giants, we get shocking revelations:

• NVDA: Expected 209%, delivered 1,520% — alpha of +1,428%! 🚀

• AMZN: Expected 122%, delivered only 50% — alpha of -42% 😬

The beauty? It strips away the noise and shows you which stocks truly earned their returns versus those that got lucky — or unlucky.

The Bottom Line for Investors

Cost of equity analysis is like having a financial lie detector. It tells you:

• Which “boring” stocks are hidden gems

• Which “explosive” stocks might be all flash, no substance

• Whether you’re being adequately compensated for the risks you’re taking

The next time someone brags about their stock’s massive returns, you can casually ask: “But what was the alpha?” 😎