What Is a Good Beta for a Stock? Ranges and How to Read It

There is no single good beta, because beta describes how much a stock moves with the market rather than how good it is. A beta near 1.0 tracks the market, below 1.0 moves less, and above 1.0 amplifies it in both directions. For a core long-term holding, 0.8 to 1.2 is the usual comfort zone; for a defensive sleeve, 0.5 to 0.8; for deliberate risk, above 1.3.

That framing is the useful one, and it is also where most beta advice stops. What it leaves out is the part that trips people up: two reputable sites will show you two different betas for the same ticker on the same afternoon, both of them correct. Below is what beta actually measures, the ranges by job rather than by dogma, why those numbers disagree, and the one finding about high-beta stocks that runs against everything the label implies.

What does beta measure in a stock?

Beta measures the sensitivity of a stock's returns to the returns of a benchmark, almost always the S&P 500 for US equities. The market is defined as 1.0. A stock with a beta of 1.4 has historically moved about 40% more than the index in both directions: when the index fell 10%, this stock tended toward a 14% fall.

The word "tended" is carrying weight there. Beta is a regression slope fitted to past returns, not a rule the stock obeys. It is an average of how the relationship behaved across a specific window, and single days routinely ignore it entirely. Beta also says nothing about company-specific risk. A biotech awaiting trial results can carry a beta of 0.6 and still fall 70% on a Tuesday, because that fall has nothing to do with the market.

Beta comes out of the capital asset pricing model, the framework William Sharpe and others developed in the 1960s, in which the only risk you should expect to be paid for is the risk you cannot diversify away. Everything unique to one company can, in principle, be diversified out of a portfolio, so CAPM says the market compensates you for beta alone. Whether reality agrees is a question with a surprising answer, and it comes later on this page.

How is beta calculated?

Beta is the covariance of the stock's returns with the market's returns, divided by the variance of the market's returns:

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

In practice it is a linear regression of the stock's periodic returns against the benchmark's returns over the same periods, and beta is the slope of that line. In a spreadsheet with the stock's returns in column A and the index's in column B, =SLOPE(A2:A61, B2:B61) gives you the answer directly, and =RSQ(A2:A61, B2:B61) gives you the R-squared that tells you whether the answer means anything.

Four choices go into that calculation, and none of them is fixed by any standard:

• Window length. Two years, three years, five years. Longer windows are more stable and more stale.
• Return frequency. Daily, weekly, or monthly returns. Daily data gives more observations and more noise from thin trading.
• Benchmark index. S&P 500, NYSE Composite, a total market index, or a sector index.
• Adjustment. Whether the raw regression figure is shrunk toward 1.0 before publication.

Why do different sites show different betas for the same stock?

Because they make those four choices differently, and each choice is defensible. This is the single most common source of confusion about beta, and it is not a data quality problem.

Yahoo Finance labels its figure "Beta (5Y Monthly)" precisely so you know the window. Bloomberg defaults to two years of weekly returns and reports an adjusted beta. Those two are measuring different periods at different resolutions, so a stock that changed character in the last eighteen months, which describes a lot of technology and energy names, will show meaningfully different betas depending on which one you open.

The practical rule is simple: never compare a beta from one source with a beta from another. If you are ranking five stocks by beta, pull all five from the same place, or compute all five yourself over the same window with the same benchmark.

What is adjusted beta and should you use it?

Adjusted beta shrinks the raw regression estimate toward the market beta of 1.0, using the formula Marshall Blume published in the Journal of Finance in 1971:

Adjusted beta = 0.67 x raw beta + 0.33 x 1.0

The logic is empirical rather than theoretical. Blume found that betas drift toward 1.0 over successive periods, as companies mature, diversify, and grow into the market rather than away from it. A stock measuring 1.6 today is more likely to measure 1.4 in five years than 1.8. So the adjusted figure, which would be 0.67 x 1.6 + 0.33 = 1.40, is the better forecast of future beta even though it is the worse description of the past.

Use the raw figure when you are describing what happened. Use the adjusted figure when you are feeding beta into something forward-looking, which is why valuation practitioners default to it. Beta is the input that sets the cost of equity in a discounted cash flow model, and since a private company has no traded price to regress, the standard method borrows adjusted betas from comparable public firms. That single number moves a valuation more than almost any other assumption, so if you ever need to put a defensible number on what a private business is worth, the beta you choose deserves more scrutiny than the revenue forecast.

What is a good beta range for a stock?

Read the range against the job the position is doing, not against an abstract ideal.

A beta of 1.5 is not a warning label and 0.6 is not a gold star. The relevant question is what the position is for. A 1.5-beta name sized at 3% of a portfolio is a considered decision; the same name at 30% is a leveraged bet on the market wearing the costume of a stock pick.

What is a good beta for a stock to buy?

For a stock you intend to hold for years, most investors are best served somewhere between 0.8 and 1.2, because that is where you get equity-like returns without turning normal market weakness into a decision point. The failure mode with high beta is rarely the math. It is that a 1.8-beta position turns a routine 15% market pullback into a 27% loss on your screen, and a lot of people sell there, converting a temporary decline into a permanent one.

Beta should also never be the first screen. It tells you nothing about whether the business earns money, what you are paying for it, or whether the balance sheet survives a bad year. Two companies with identical betas of 1.1 can have completely different prospects. Use beta to size and to understand your exposure after you have decided the company is worth owning, not to decide that.

What is a good beta for a stock portfolio?

Portfolio beta is the weighted average of the betas of the holdings, and for most long-term investors a figure near 1.0 is the sensible default, because it means your portfolio behaves like the market you are trying to participate in. Below 0.8 you are giving up meaningful upside for smoothness; above 1.2 you are running leverage in effect if not in name.

Two things surprise people when they first compute it. The first is how high it already is: a portfolio of large-cap US technology names can carry a portfolio beta of 1.3 or more while feeling diversified, because six positions in the same factor is one position wearing six tickers. The second is that portfolio beta hides concentration. A portfolio holding 90% in a 0.9-beta stock and 10% in cash also comes out near 0.8, and no beta figure will tell you about the single-name risk sitting inside it.

It is also worth asking which market your beta should be measured against. If you hold a concentrated basket of semiconductor names, the S&P 500 is not really your benchmark, and beta against it will overstate how much diversification you have. Measuring against a benchmark that matches what you actually own gives a more honest reading, in the same way that alpha collapses the moment you pick the right comparison.

Is a high beta stock good or bad?

It is neither, but the evidence on whether high beta pays you for the extra risk is genuinely uncomfortable for the theory.

CAPM predicts that higher beta earns higher expected return, since beta is the risk you cannot diversify away. Decades of data say otherwise. Andrea Frazzini and Lasse Heje Pedersen documented this in "Betting Against Beta" (Journal of Financial Economics, volume 111, 2014, pages 1 to 25): high beta is associated with low alpha across US equities, twenty international equity markets, Treasury bonds, corporate bonds and futures. Their BAB factor, long leveraged low-beta assets and short high-beta assets, realized a Sharpe ratio of 0.78 between 1926 and March 2012, roughly twice that of the value effect and about 40% higher than momentum over the same period.

Their explanation is about constraints rather than psychology. Investors who want more return but cannot or will not borrow to get it buy high-beta assets instead, bidding those prices up and their future returns down. Whatever the mechanism, the practical read is clear enough: high beta has historically delivered the extra volatility reliably and the extra return unreliably. That does not make high-beta stocks a mistake, but it does mean "I bought it for the higher expected return" is a weaker argument than it sounds.

What is a good beta score for a stock, and when is beta meaningless?

Beta is only meaningful when the market explains a real share of the stock's movement, and R-squared is what tells you that. R-squared is the proportion of the stock's variance explained by the benchmark. A large diversified industrial might come in around 0.7, meaning most of its movement is market movement and its beta is informative. A small biotech might come in at 0.05, meaning the market explains almost nothing, and its beta of 1.3 is a number the regression produced rather than a fact about the stock.

Always read the two together. A beta without an R-squared beside it is an assertion. If you are computing it yourself, add the RSQ formula next to the SLOPE formula and look at both before you use either.

What is a good beta for a mutual fund or ETF?

For a fund, beta is mostly a consistency check against what the fund says it does. A broad US index fund should sit very close to 1.0 against the S&P 500 with an R-squared near 0.99. A low-volatility or dividend fund earning its fee should come in somewhere around 0.6 to 0.85. An aggressive growth fund at 1.2 to 1.4 is behaving as advertised.

The number to be suspicious of is a fund whose beta does not match its story. A "conservative" fund posting a beta of 1.15 is taking more market risk than its name implies, and a fund with a low beta but an R-squared of 0.4 is doing something the benchmark does not capture at all, which may be skill or may just be a different exposure you are paying active fees for. Read beta next to R-squared, expense ratio, and the fund's actual holdings.

How to check beta assumptions against your own holdings

Beta describes history, so the only way to know what a beta level meant for a portfolio like yours is to look at what actually happened to it. Pull the same window, the same frequency and the same benchmark for everything you are comparing, then look at how a higher-beta version of your allocation behaved through the drawdowns rather than through the averages. The 2020 crash, 2022, and 2008 are the stretches that separate a beta you can live with from one you only thought you could.

That is the kind of question the bench at the top of this page is built for. You state a rule or name a ticker in plain English, it restates the rule so you can confirm what will be tested, then runs it across 20+ years of split- and dividend-adjusted daily data with 0.1% charged per trade, and plots the result against buy and hold with the worst drawdown window shaded. Concentrating a portfolio in high-beta names is a strategy like any other, and it can be tested rather than assumed.

If you want to run that on one name, stock backtesting covers the single-ticker case, portfolio backtesting covers a whole allocation, and investment risk analysis explains the risk panel that comes out of every run. Swing traders who screen for high-beta names because they move enough to be worth trading should read AI swing trading tools, since a setup that only works on the most volatile quartile of the market needs its costs and its drawdowns checked before it is trusted. The data behind every figure is described on historical stock data.

The short answer

What is a good beta for a stock? Around 1.0 if you want market-like behavior, 0.5 to 0.8 for a defensive holding, and above 1.3 only when the position is deliberately sized for it. Beta measures sensitivity to the market and nothing else: not quality, not valuation, not total risk. Read it beside R-squared, take it from a single source with a stated window, and remember that high beta has historically delivered the extra volatility far more reliably than the extra return.

The practical next step is to stop treating beta as a verdict and start treating it as one input you can test. State what you would actually hold, run it against the alternative you would otherwise own, and read the drawdown alongside the return. What is a good alpha covers the return side of the same equation, what is a good Sharpe ratio covers return per unit of volatility, and the Sharpe ratio calculator computes it from real history rather than from numbers you paste in.