Beta in Stocks: Why Exxon's 0.17 Beta Didn't Mean Low Risk

Beta is one of the most quoted numbers on any stock quote page, and one of the most misunderstood. It tells you how much a stock has tended to move when the overall market moves. A beta of 1.0 means the stock has historically moved in step with the market. A beta of 2.0 means it has tended to move about twice as far, in the same direction. A beta of 0.3 means it has barely reacted to the market at all.

That sounds simple, and the basic idea is. The trouble starts when investors treat beta as a complete measure of risk. It is not. In the five years of data we calculated for this guide, Exxon Mobil had the lowest beta of seven assets we tested, at 0.17, yet its shares were more volatile than Coca-Cola's, Johnson & Johnson's and the S&P 500's. Tesla, a stock most people would call far riskier than Nvidia, had a lower beta than Nvidia. And Bitcoin, often described as uncorrelated with stocks, had a beta of 1.68.

This guide explains what beta in stocks actually measures, how to calculate it by hand, what our own five-year numbers show, how to use beta to build and hedge a portfolio, and where it quietly misleads. All historical figures below are our own calculations from monthly adjusted closing prices published by Yahoo Finance for November 2021 through October 2026. Worked portfolio examples are hypothetical and labeled as such.

What beta in stocks measures

Beta measures the sensitivity of an asset's returns to the returns of a benchmark, usually a broad stock index such as the S&P 500. The U.S. Securities and Exchange Commission's investor education site, Investor.gov, lists beta in its glossary as a measure of a security's volatility relative to the market. More precisely, beta is the slope of the line you get when you plot a stock's returns against the market's returns over the same periods.

Here is how to read the most common values:

Beta	What it says about past behavior	Typical example
Above 1.0	The stock tended to amplify market moves	Many technology and consumer discretionary stocks
About 1.0	The stock tended to move in line with the market	A broad index fund tracking the benchmark
Between 0 and 1.0	The stock tended to move in the same direction but less	Many utilities, staples and health care companies
Near 0	Market moves explained little of the stock's moves	Some commodity producers over certain periods, cash
Below 0	The stock tended to move against the market	Rare for stocks; some hedging instruments

Two words in that table matter more than any number: "tended to." Beta is a historical statistic. It describes a relationship over a chosen window, and that relationship can change.

The beta formula

The textbook formula is:

Beta = Covariance(stock returns, market returns) ÷ Variance(market returns)

Covariance measures how two series move together. Variance measures how much the market moves on its own. Dividing one by the other tells you how many units the stock has moved, on average, for each unit of market movement.

An equivalent way of writing it is often more intuitive:

Beta = Correlation × (Stock volatility ÷ Market volatility)

This version shows that beta is built from two separate ingredients. Correlation captures how consistently the stock moves with the market. The volatility ratio captures how large the stock's moves are compared with the market's. A stock can have a high beta because it moves a lot, because it moves consistently with the market, or both. A stock can have a low beta because it is calm, or because it is wild but marches to its own drum. That second case is the trap we will come back to.

How to calculate beta by hand: a four-month example

Real beta estimates use dozens of observations, but a tiny hypothetical example shows exactly what the formula does. Suppose over four months the market and a stock returned the following:

Month	Market return	Stock return
1	+2%	+3%
2	−1%	−2%
3	+3%	+5%
4	−2%	−3%

Step 1: find the averages. The market's average monthly return is (2 − 1 + 3 − 2) ÷ 4 = 0.5%. The stock's average is (3 − 2 + 5 − 3) ÷ 4 = 0.75%.

Step 2: find each month's deviation from the average. For the market the deviations are +1.5, −1.5, +2.5 and −2.5. For the stock they are +2.25, −2.75, +4.25 and −3.75.

Step 3: multiply the paired deviations and add them up. 1.5 × 2.25 = 3.375; −1.5 × −2.75 = 4.125; 2.5 × 4.25 = 10.625; −2.5 × −3.75 = 9.375. The total is 27.5.

Step 4: square the market deviations and add them up. 2.25 + 2.25 + 6.25 + 6.25 = 17.

Step 5: divide. 27.5 ÷ 17 = 1.62. (Covariance and variance would each be divided by the same n − 1, so that divisor cancels.)

A beta of about 1.62 says that in this sample, when the market rose or fell by 1 percentage point more than usual, the stock tended to move about 1.62 points more than usual in the same direction. Four observations are far too few to rely on; we use them only to make the arithmetic visible.

In a spreadsheet you can skip the steps. Put market returns in one column and stock returns in another, then use =SLOPE(stock_range, market_range). The result is the same number, because beta is literally the slope of a regression line of stock returns on market returns.

What five years of real data show

To move beyond textbook examples, we calculated beta for six well-known U.S. stocks and Bitcoin against the SPDR S&P 500 ETF Trust (SPY), a common proxy for the S&P 500. We used 60 monthly returns from November 2021 through October 2026, based on Yahoo Finance adjusted closing prices, which account for dividends and stock splits. Volatility is the standard deviation of monthly returns, annualized by multiplying by the square root of 12.

Asset	5-year beta vs SPY	Correlation	R-squared	Annualized volatility
Nvidia (NVDA)	2.21	0.68	0.47	49.9%
Tesla (TSLA)	1.72	0.46	0.21	58.4%
Bitcoin (BTC-USD)	1.68	0.49	0.24	53.7%
Apple (AAPL)	1.07	0.67	0.45	24.7%
Coca-Cola (KO)	0.28	0.26	0.07	16.4%
Johnson & Johnson (JNJ)	0.22	0.19	0.04	17.7%
Exxon Mobil (XOM)	0.17	0.09	0.01	27.4%
S&P 500 (SPY)	1.00	1.00	1.00	15.5%

Your broker or a financial website may show different betas for the same stocks. That is normal. Providers use different windows (one, two, three or five years), different frequencies (daily, weekly or monthly) and different benchmarks. We return to why that matters below.

Lesson 1: Apple is the closest thing to "the market" in this list

Apple's beta of 1.07 is close to 1.0, and its correlation of 0.67 is among the highest in the table. Over this window, Apple behaved roughly like a slightly more energetic version of the S&P 500. That is not surprising: Apple is one of the largest companies in the index, so it is partly measuring itself.

Lesson 2: Nvidia's high beta came from both size of moves and consistency

Nvidia had the highest beta, 2.21, combining very high volatility (49.9%) with a correlation of 0.68, the highest of any asset here. When the market had a bad month, Nvidia usually had a much worse one. In April 2022, SPY fell 8.78% and Nvidia fell 32.03%. In July 2022, SPY rose 9.21% and Nvidia rose 19.82%.

Lesson 3: Tesla is more volatile than Nvidia but has a lower beta

Tesla's annualized volatility of 58.4% was the highest in the table, yet its beta of 1.72 was well below Nvidia's. The reason is correlation. Tesla's correlation with the market was only 0.46, so a large share of its moves had nothing to do with the market. Its R-squared of 0.21 says that, in a simple regression, market moves accounted for only about a fifth of the variation in Tesla's monthly returns. The rest was stock-specific: deliveries, product news, its chief executive, and everything else that makes Tesla Tesla.

This is the single most important idea in this guide. Beta measures only the part of risk that is tied to the market. It says nothing about the rest.

Lesson 4: Exxon has the lowest beta and is not the safest stock

Exxon Mobil's beta of 0.17 might suggest a sleepy, defensive stock. Its 27.4% volatility says otherwise: it moved more than Apple, Coca-Cola, Johnson & Johnson and SPY. Its beta is low because its correlation with the market was just 0.09. Oil and gas prices, not the S&P 500, drove most of its returns over this period.

The monthly data show this clearly. In June 2022, SPY fell 8.25% and Exxon fell 10.79%. But in April 2022, SPY fell 8.78% while Exxon rose 3.22%. In April 2026, SPY rose 10.51% and Exxon fell 9.04%. A low beta did not mean small moves. It meant moves that were unreliably linked to the market's.

Low beta also did not mean low return. Exxon's total return, including dividends, was about 246% over the window, compared with about 76% for SPY. That was a sector story, not a beta story, and investors should not expect the two to line up in future periods.

Lesson 5: Coca-Cola and Johnson & Johnson are genuinely defensive, by this measure

Coca-Cola (beta 0.28, volatility 16.4%) and Johnson & Johnson (beta 0.22, volatility 17.7%) combine low beta with low volatility. That is the classic defensive profile. In April 2022, when SPY fell 8.78%, Coca-Cola rose 4.21% and Johnson & Johnson rose 1.82%.

Defensive does not mean immune. In September 2022, the worst month for SPY in our sample at −9.24%, Coca-Cola still fell 8.56%. One month can break a pattern that holds on average across 60.

Lesson 6: Bitcoin behaved like a high-beta tech stock

Bitcoin is often marketed as a diversifier. Over these five years its beta to SPY was 1.68, close to Tesla's. Its correlation of 0.49 means it was far from perfectly linked to stocks, but when the link showed up, it was strong. In June 2022, SPY fell 8.25% and Bitcoin fell 37.77%. One technical caveat: Bitcoin trades every day, so its calendar-month closes do not always fall on the same day as SPY's last trading day of the month. That timing mismatch adds some noise to the estimate.

If you want more background on how crypto and index markets interact, our crypto signals hub and index signals hub cover how traders follow both.

The worst and best months for the market, stock by stock

Averages hide the moments investors actually feel. Here is how each asset moved in the three worst and two best months for SPY in our window.

Month	SPY	NVDA	TSLA	BTC	AAPL	KO	JNJ	XOM
Sep 2022	−9.24%	−19.55%	−3.76%	−3.08%	−12.10%	−8.56%	+1.25%	−8.66%
Apr 2022	−8.78%	−32.03%	−19.19%	−17.18%	−9.71%	+4.21%	+1.82%	+3.22%
Jun 2022	−8.25%	−18.80%	−11.19%	−37.77%	−8.14%	−0.02%	−1.13%	−10.79%
Jul 2022	+9.21%	+19.82%	+32.38%	+17.95%	+18.86%	+2.00%	−1.68%	+13.18%
Apr 2026	+10.51%	+14.43%	+2.66%	+11.83%	+6.92%	+3.56%	−5.97%	−9.04%

Read across any row and you can see beta at work and beta failing. Nvidia amplified the market in every one of these months. Tesla fell only 3.76% in September 2022, far less than its beta would predict, two months after rising 32.38% in July 2022. Johnson & Johnson moved against the market in four of the five months. Beta is an average relationship; individual months scatter widely around it.

How investors use beta

1. Estimating portfolio sensitivity

A portfolio's beta is the weighted average of its holdings' betas. That makes it easy to estimate how much market exposure you have.

Hypothetical example: a $50,000 portfolio holds 30% in Apple, 20% in Nvidia, 30% in Coca-Cola and 20% in Johnson & Johnson. Using the five-year betas above:

• Apple: 0.30 × 1.073 = 0.322
• Nvidia: 0.20 × 2.205 = 0.441
• Coca-Cola: 0.30 × 0.279 = 0.084
• Johnson & Johnson: 0.20 × 0.218 = 0.044

Portfolio beta ≈ 0.89. Based on past relationships, if the market fell 10%, the market-linked part of this portfolio would be expected to fall about 8.9%, or roughly $4,450. The actual result could be quite different, because each stock also carries its own specific risk, and that does not average away fully in a four-stock portfolio.

Notice how much one holding drives the result. Nvidia is only 20% of the money but contributes about half of the portfolio's beta (0.441 out of 0.890).

2. Hedging market exposure

Beta is the basis of the simplest equity hedge. To neutralize the market exposure of a portfolio, you short an amount of an index product equal to the portfolio value multiplied by its beta.

Hypothetical example: a $100,000 portfolio with a beta of 1.3 has the market exposure of about $130,000 of the index. To hedge it, an investor could short roughly $130,000 of an S&P 500 ETF or the equivalent in index futures. If the market then fell 5%, the portfolio would be expected to lose about $6,500 from its market exposure while the short would gain about $6,500. What remains is the stock-specific part, which the hedge does not touch, plus costs such as borrowing fees, financing and any tracking difference between the hedge instrument and the index.

The same logic underlies protective strategies built with options. If you want a floor on a single holding rather than a beta-weighted hedge, our guide to the protective put walks through the cost of that insurance.

3. Estimating expected return with CAPM

The Capital Asset Pricing Model (CAPM) uses beta to estimate the return investors should demand from a stock:

Expected return = Risk-free rate + Beta × (Expected market return − Risk-free rate)

Hypothetical example: with a 4% risk-free rate, an assumed 9% expected market return and a beta of 1.2, the CAPM expected return is 4% + 1.2 × (9% − 4%) = 4% + 6% = 10%. A stock with a beta of 0.5 under the same assumptions would have an expected return of 4% + 2.5% = 6.5%.

CAPM is widely taught and used in corporate finance to estimate the cost of equity. It is also a simplification. Decades of academic research have found that the relationship between beta and realized returns is much flatter than the model predicts; low-beta stocks have often done better, and high-beta stocks worse, than CAPM implies. Treat CAPM outputs as a starting assumption, never a forecast. The inputs here are illustrative, not current market estimates.

4. Matching holdings to your risk tolerance

An investor who loses sleep in sell-offs might tilt toward lower-beta holdings. An investor with a long horizon and a high tolerance for swings might accept higher beta. Beta is a reasonable shorthand for that choice, as long as you also look at volatility, concentration and what actually drives each business.

For investors who prefer to own the whole market, a broad index fund has a beta of about 1.0 by design. Our comparison of tracking error explains how closely such funds actually follow their benchmark.

Why beta changes depending on where you look

If you check the beta of the same stock on three websites, you may get three different numbers. Common reasons include:

• Window length. A one-year beta reacts quickly to recent events; a five-year beta is steadier but may include a business the company no longer resembles.
• Return frequency. Daily, weekly and monthly returns can produce different betas, especially for smaller or less frequently traded stocks, where prices may lag the market.
• Benchmark. Beta against the S&P 500, the Nasdaq-100, a global index or a sector index answers different questions.
• Adjustments. Some providers publish "adjusted" beta that pulls raw estimates toward 1.0, based on the observation that extreme betas tend to drift toward the average over time.
• Price data. Using adjusted closes, which include dividends, rather than raw prices slightly changes the result, especially for high-dividend stocks.

None of these choices is wrong. But when you compare betas, compare numbers calculated the same way. Mixing a one-year daily beta for one stock with a five-year monthly beta for another is comparing different measurements.

The limits of beta

Beta ignores stock-specific risk

Total risk can be split into two parts: systematic risk, which comes from the market as a whole, and idiosyncratic risk, which is specific to the company. Beta captures only the first. That is why Tesla and Exxon, with R-squared values of 0.21 and 0.01, have betas that say little about how much their prices actually move. A single fraud, failed drug trial or lost contract can wipe out a low-beta stock.

In a well-diversified portfolio, stock-specific risks tend to offset each other, which is why finance theory focuses on beta. Investor.gov's explanation of diversification makes the general case for spreading risk. In a concentrated portfolio, stock-specific risk can matter more than beta.

Beta assumes a straight-line relationship

Beta is the slope of a straight line. Real stocks often behave differently in falling markets than in rising ones. Some have a higher "downside beta" than "upside beta": they fall harder in sell-offs than they rise in rallies. A single beta averages those together.

Beta looks backward

Companies change. A utility that buys a growth business, a tech company that matures into a cash machine, or an oil producer whose correlation with stocks shifts with the energy cycle will all see betas move. Interest rates, inflation regimes and market leadership also change which stocks move with the index.

A low R-squared makes beta unreliable

Always look at R-squared, or at least correlation, alongside beta. With an R-squared near 0.45, as for Apple and Nvidia here, beta is a meaningful description. With an R-squared near 0.01, as for Exxon, the "beta" is close to noise; a slightly different window could produce a very different number.

Beta is not the same as volatility

This is worth repeating because the two are confused so often. Volatility measures how much a price moves. Beta measures how much of that movement is tied to the market, scaled by relative size. Exxon was nearly twice as volatile as SPY in our data with a beta under 0.2. For a different angle on risk that depends on timing rather than market sensitivity, see our guide to sequence of returns risk.

Beta compared with other risk measures

Measure	What it captures	What it misses
Beta	Sensitivity to the market	Stock-specific risk; non-linear behavior
Standard deviation (volatility)	Total size of price swings	Whether swings are linked to the market
Correlation	How consistently two assets move together	The size of the moves
R-squared	Share of variation explained by the market	Direction and size of sensitivity
Maximum drawdown	Worst peak-to-trough loss in a period	How often or why it happened
Duration (bonds)	Price sensitivity to interest rates	Credit and liquidity risk

Beta is to stocks roughly what duration is to bonds: a single sensitivity number to one main driver. Our guide to modified duration shows the bond version of the same idea.

How to use beta sensibly: a practical checklist

1. Check the method. Know the window, frequency and benchmark behind the number you are reading.
2. Look at R-squared or correlation. If the market explains little of a stock's movement, its beta is a weak guide.
3. Look at volatility too. A low-beta stock can still swing hard.
4. Use portfolio beta, not just stock beta. What matters for market exposure is the weighted total.
5. Watch concentration. One high-beta holding can dominate the portfolio's sensitivity, as Nvidia did in our example.
6. Stress-test with real months. Ask what your holdings did in the market's worst months, not just what their average beta says.
7. Re-check periodically. Betas drift as businesses and markets change.
8. Do not confuse sensitivity with quality. A low-beta stock is not automatically a good investment, and a high-beta stock is not automatically a bad one.

Beta for traders versus long-term investors

Short-term traders often use beta to size positions and to understand how much of a trade is really a bet on the market. If you buy a stock with a beta of 2, a large part of your profit or loss may come from the index rather than from your view on the company. Some traders pair a long position in a stock with a beta-weighted short in an index to isolate the company-specific part of the trade.

Long-term investors tend to use beta more broadly: to understand how a portfolio might behave in a sell-off, and to decide whether its swings fit their goals and time horizon. For them, beta is less about precise hedging and more about avoiding surprises. Combining market exposure with time and patience is a theme we explore in our article on time in the market versus timing the market.

In both cases, the same rule applies. Beta is a description of the past relationship between a stock and the market. It is useful, it is easy to calculate, and it is incomplete.

How we calculated the numbers

We downloaded monthly adjusted closing prices from Yahoo Finance for SPY, Apple, Nvidia, Tesla, Coca-Cola, Johnson & Johnson, Exxon Mobil and Bitcoin (BTC-USD), from October 2021 through October 2026. We computed 60 simple monthly returns for each, from November 2021 to October 2026. Beta is the sample covariance of each asset's returns with SPY's returns divided by the sample variance of SPY's returns. Correlation is the Pearson correlation; R-squared is its square. Annualized volatility is the sample standard deviation of monthly returns multiplied by √12. Total returns compare the October 2026 adjusted close with the October 2021 adjusted close. Calculations and source data are preserved in our editorial records so they can be reproduced.

Historical figures can be revised by data providers, for example after corporate actions. Past betas and returns do not predict future results.

The bottom line

Beta in stocks tells you how strongly a stock has moved with the market. It is a simple slope: covariance divided by variance, or correlation multiplied by relative volatility. A beta above 1 means the stock has tended to amplify market moves; below 1 means it has tended to dampen them.

Our five-year numbers show why you should never read beta alone. Nvidia's 2.21 beta reflected large, market-linked moves. Tesla's 1.72 hid even larger moves that were mostly its own. Exxon's 0.17 came from a stock that moved more than the S&P 500 but for different reasons. Bitcoin's 1.68 challenged the idea that it diversifies a stock portfolio. Check beta with correlation, volatility and the method behind it, and use it to understand your exposure rather than to predict your returns.

This article is for educational purposes only and is not investment advice. Investing involves risk, including the loss of principal.