Beta Calculation in Excel: SLOPE, Unlever & Adjusted (2026)

July 10, 2026 · VeloraAI Team
Formulas Financial Modeling Data Analysis

Pull a beta off Yahoo Finance and drop it into a DCF, and you've just made three silent assumptions — that the historical window is right, that raw regression beta predicts future risk, and that the target company has the same leverage as the reference sample. Beta calculation in Excel is not a one-formula exercise. It is a chain of decisions — return frequency, index choice, regression method, adjustment, unlever/relever — that together determine whether your cost of equity is defensible or a rounding error away from garbage.

This guide walks through the full stack: raw regression beta from price history using SLOPE and LINEST, the covariance/variance identity, Blume-adjusted beta the way Bloomberg computes it, industry beta via unlever/relever, and rolling betas that expose regime shifts. Every formula below runs in Excel 365 and Excel 2021, and every convention lines up with what an M&A team or an equity research desk would actually accept in a live model.

What Is Beta and Why Does It Matter in Excel?

Beta measures a security's sensitivity to broad market moves. A beta of 1.0 means the stock moves in line with the market; 1.5 means it swings 50% more; 0.6 means it dampens market moves by 40%. In practical terms, beta is the single biggest driver of the cost of equity in the CAPM used inside a WACC build, so getting it right materially changes equity value.

Statistically, beta is the slope of a regression line between a stock's excess returns and the market's excess returns over a common window. In Excel, that means a SLOPE, LINEST, or COVARIANCE.S/VAR.S call over paired return series — and a lot of judgment about what goes into those series.

The Three Levers That Change Beta

Before touching a formula, three inputs need to be locked:

  1. Return frequency — Daily, weekly, or monthly. Weekly is the industry standard for equity research; daily overweights microstructure noise; monthly loses precision on short histories.
  2. Estimation window — 2 years of weekly returns (~104 observations) is Bloomberg's default. 5 years of monthly returns is the academic standard.
  3. Market proxy — S&P 500 for US large-caps, MSCI ACWI for global businesses, Russell 2000 for US small-caps. Never mix currencies or geographies without an FX-neutral index.

⚠️ Warning: The single most common beta error is using a global index for a US-only business, or vice versa. If your target's revenue is 90% domestic, use the domestic index — otherwise systematic risk gets blended with FX exposure and country risk you didn't intend to price.

How Do You Calculate Beta in Excel Using SLOPE?

To calculate beta in Excel with SLOPE, download aligned closing prices for the stock and market index, convert both series to periodic returns using =(P1/P0)-1, then run =SLOPE(stock_returns, market_returns). The result is your raw regression beta over the chosen window. This is the exact identity that anchors CAPM.

Step-by-Step: Raw Regression Beta

Assume you have weekly closing prices in a worksheet:

  • Column A: Week-ending dates
  • Column B: Stock closing price
  • Column C: S&P 500 closing price

Step 1 — Compute weekly returns. In cell D3 (first return, second row of prices):

=B3/B2-1

In E3:

=C3/C2-1

Fill both down. You now have paired periodic returns.

Step 2 — Run the SLOPE regression. In any output cell:

=SLOPE(D3:D106, E3:E106)

For 104 weekly observations (~2 years), this returns your raw beta. Note the argument order: known_y's first, known_x's second — the stock is the dependent variable, the market is the independent variable. Reversing them gives you the wrong number.

Step 3 — Cross-check with the covariance identity. Beta can be derived directly:

=COVARIANCE.S(D3:D106, E3:E106) / VAR.S(E3:E106)

This must equal the SLOPE output to at least 6 decimals. If it doesn't, you have a range-alignment bug (usually a header row bleeding into one array).

💡 Pro Tip: Wrap your beta calculation in LET for readability: =LET(s, D3:D106, m, E3:E106, SLOPE(s, m)). When the ranges grow to 500+ rows, one named argument is far easier to audit than four range references scattered across the formula. See our LET function guide for financial formulas for the pattern library.

Using LINEST for Regression Diagnostics

SLOPE gives you the coefficient. LINEST gives you the coefficient plus the standard error, R², and F-statistic — the diagnostics you need to defend the beta in an investment committee.

=LINEST(D3:D106, E3:E106, TRUE, TRUE)

Entered as a dynamic array in Excel 365, this spills a 5-row × 2-column matrix. Reading the outputs:

LINEST Cell Value Interpretation
Top-left Slope (beta) Same as SLOPE()
Top-right Intercept (alpha) Excess return unexplained by beta
Row 2 Standard errors SE of beta and SE of alpha
Row 3 (left) R² Fit quality — 0.30 to 0.50 is typical for single stocks
Row 4 (left) F-statistic Overall significance
Row 5 (left) Sum of squares regression For decomposing explained variance

Reading the diagnostics: an R² below 0.15 means the market explains almost none of your stock's variance — the regression beta is statistically fragile and the market may be the wrong proxy. A standard error above 0.30 means your 95% confidence interval on beta is ±0.60 wide — wide enough that "1.2" and "0.6" are indistinguishable.

How Do You Unlever and Relever Beta in Excel?

Raw beta reflects the target's current capital structure. To use peer betas as a proxy for a private company or a new business line, you must unlever each peer's beta to strip out financial risk, average the pure business-risk betas, then relever at the target's own capital structure. The Hamada equation is the standard formulation.

The Hamada Unlever/Relever Formula

The unlevering step:

βunlevered = βlevered / (1 + (1 - Tax) × D/E)

The relevering step:

βrelevered = βunlevered × (1 + (1 - Tax) × D/E_target)

Excel Implementation With a Comp Set

Assume you have five comparables in a worksheet:

  • Column A: Ticker
  • Column B: Levered beta (from your regression)
  • Column C: Debt/Equity ratio
  • Column D: Marginal tax rate

Unlever each peer — in E2:

=B2/(1 + (1 - D2) * C2)

Fill down. You now have five pure business-risk betas.

Average the unlevered betas — in G1:

=AVERAGE(E2:E6)

Consider MEDIAN if you have an outlier — a comp with unusual capital structure can distort the mean.

Relever at the target's D/E — assuming target D/E is in H1 and target tax rate in H2, in H3:

=G1 * (1 + (1 - H2) * H1)

That is your peer-based beta for the target.

One-Shot Dynamic Array Version

For a cleaner, auditable build, collapse the whole flow into one dynamic array formula:

=AVERAGE(B2:B6/(1+(1-D2:D6)*C2:C6)) * (1+(1-H2)*H1)

This spills nothing — it returns a single scalar — but it computes the unlevered beta vector inline, averages it, and relevers in one step. Wrap in LET for auditability:

=LET(
   levered, B2:B6,
   tax, D2:D6,
   de_peer, C2:C6,
   tax_t, H2,
   de_t, H1,
   unlevered, levered/(1+(1-tax)*de_peer),
   AVERAGE(unlevered)*(1+(1-tax_t)*de_t)
)

Example: Five peers with an average unlevered beta of 0.85, a target D/E of 0.50, and a 25% target tax rate: relevered beta = 0.85 × (1 + 0.75 × 0.50) = 1.169. That's the input for CAPM.

graph TD
    A[Peer Levered Beta] --> B[Divide by 1 + 1-Tax × D/E]
    B --> C[Peer Unlevered Beta]
    C --> D[Average Across Peers]
    D --> E[Target Business Risk Beta]
    E --> F[Multiply by 1 + 1-Tax × D/E Target]
    F --> G[Target Relevered Beta]
    G --> H[Input to CAPM Cost of Equity]

What Is Adjusted Beta and How Do You Compute It in Excel?

Adjusted beta corrects raw regression beta for the empirical tendency of betas to revert toward 1.0 over time. Marshall Blume documented this in 1975: high-beta stocks tend to decline toward 1.0, low-beta stocks tend to rise toward 1.0. Ignoring this bias systematically overstates forecast beta on both tails. Bloomberg reports adjusted beta by default.

The Blume Adjustment (Bloomberg Convention)

Bloomberg's adjusted beta uses fixed weights that approximate Blume's regression coefficients:

Adjusted Beta = 0.67 × Raw Beta + 0.33 × 1.0

In Excel, if the raw beta lives in B10:

=0.67*B10 + 0.33

That's the Bloomberg formula in a single cell. A raw beta of 1.50 becomes 1.335. A raw beta of 0.60 becomes 0.732. Both drift toward 1.0.

The Vasicek Adjustment (Bayesian)

Vasicek (1973) refines Blume by weighting the adjustment by the precision of the individual regression relative to the cross-sectional variance of all betas. Where the individual beta is precisely estimated (low SE), Vasicek trusts it. Where it is noisy, Vasicek shrinks it harder toward the market average.

βVasicek = w × βraw + (1 - w) × βavg

where:

w = σ²(all betas) / [σ²(all betas) + SE²(individual beta)]

In Excel, if the raw beta is in B10, its LINEST standard error is in B11, the market-wide beta average is in B12, and the cross-sectional variance of betas is in B13:

=(B13 / (B13 + B11^2)) * B10 + (1 - B13/(B13 + B11^2)) * B12

When to use which: Blume for speed and Bloomberg parity. Vasicek when you have several regression outputs across a portfolio or peer set and want the noisier estimates shrunk more aggressively than the clean ones.

Method Formula Data Required Best For
Raw regression SLOPE(y, x) Return pairs only Historical descriptive
Blume-adjusted 0.67×β + 0.33 Raw beta Bloomberg-parity valuation
Vasicek-adjusted Precision-weighted shrinkage Raw beta + SE + cross-section Portfolio-level forecasting
Peer-based (unlevered) Hamada relever Peer betas + capital structure Private companies
Industry beta Median of unlevered peers Peer regression outputs New business lines, IPOs

💡 Pro Tip: For a valuation you plan to defend against a Bloomberg terminal readout, publish both raw and Blume-adjusted beta side by side. That makes the reconciliation trivial when the investment committee opens Bloomberg mid-meeting.

How Do You Build a Rolling Beta in Excel?

A single-point beta hides regime shifts. A rolling beta — a moving-window regression that steps forward one period at a time — surfaces exactly when the stock's market sensitivity changed. This is essential for cyclicals, post-M&A companies, and any name that repositioned strategically inside your estimation window.

Rolling Beta Setup

Assume you have 260 weekly return pairs (5 years). Use a 52-week rolling window.

In row 55 (first row with 52 lookbacks available), in cell F55:

=SLOPE(OFFSET(D55, -51, 0, 52, 1), OFFSET(E55, -51, 0, 52, 1))

Or with modern spilling — much cleaner:

=SLOPE(INDEX(D:D, ROW()-51):INDEX(D:D, ROW()), INDEX(E:E, ROW()-51):INDEX(E:E, ROW()))

Fill down. Column F now holds a 52-week rolling beta by week.

Plotting and Interpreting

Chart column F against column A (date). What you're looking for:

  • Level shifts — a step change in beta usually signals a strategic move (acquisition, divestiture, product mix change) or a regime break in the underlying business
  • Trend drift — a slow rise or fall in beta reflects gradual capital-structure or business-model change
  • Volatility clusters — periods where beta whipsaws often coincide with earnings misses, guidance revisions, or macro shocks

ℹ️ Note: Weekly rolling betas over 52-week windows are the analyst standard. Daily rolling betas over 252 days produce very similar averages but 3-5× more noise per point.

Which Return Frequency Should You Use for Beta?

Weekly Wednesday-to-Wednesday returns are the industry default because they balance sample size against microstructure noise. Daily returns pack in more observations but pick up bid-ask bounce and asynchronous trading effects that inflate variance and drag beta toward zero for illiquid names. Monthly returns are clean but require 5+ years to get statistically meaningful.

Frequency Comparison

Frequency Typical Window Observations Trade-Off
Daily 1 year ~252 High sample, high noise, downward bias for illiquid stocks
Weekly 2 years ~104 Bloomberg default; balanced
Monthly 5 years 60 Low noise but requires stable business over 5 years
Quarterly 10 years 40 Almost never used — too coarse

The illiquidity trap: for small-caps and thinly traded names, daily beta systematically understates true beta because the stock doesn't reprice every day. Fix this by using weekly returns or applying a Scholes-Williams / Dimson correction — but the simpler fix is weekly-frequency estimation.

Full CAPM Cost of Equity Build in Excel

Once beta is nailed down, plugging it into CAPM is trivial. The formula:

Cost of Equity = Rf + β × (Rm - Rf)

With inputs in a clean block:

  • B1: Risk-free rate (10Y Treasury yield)
  • B2: Equity risk premium
  • B3: Relevered beta (from the peer set)

Cost of equity in B4:

=B1 + B3*B2

For an interactive audit, wrap the CAPM plus WACC blend in a LET block and stress-test beta ±0.20 with a two-variable data table — the same pattern covered in our sensitivity analysis guide. When beta is the top-two driver, showing that sensitivity is the difference between a defensible valuation and a hand-wave.

Common Beta Calculation Mistakes to Avoid

The formulas are easy; the judgment isn't. In practice, most beta errors trace back to one of four patterns:

  1. Wrong index — Using the S&P 500 for a European company. Use MSCI Europe or a domestic index instead.
  2. Frequency mismatch — Regressing weekly stock returns against monthly index returns. Both series must be sampled at the same frequency and aligned to the same period end.
  3. Return calculation error — Using arithmetic returns for some series and log returns for others. Pick one convention and use it for both.
  4. No adjustment for private/target company leverage — Copying a peer's raw beta into CAPM for a target with different leverage. Always unlever, average, relever.

⚠️ Warning: Never regress raw price levels against index levels. Beta is a returns-based concept. Regressing levels gives you a nonsense number that will pass basic sniff tests but produce cost of equity errors of 200-400 bps.

Frequently Asked Questions

What is a good beta value for a stock?

There is no universally "good" beta — the right value depends on the business risk of the underlying company. Utilities and consumer staples typically show betas of 0.4-0.8. Broad market index funds cluster at 1.0. Cyclicals, small-caps, and high-growth tech typically show betas of 1.2-2.0. What matters is whether beta is defensible for the specific business, not whether it's above or below a benchmark.

Can I calculate beta in Excel without regression?

Yes — use the covariance/variance identity: =COVARIANCE.S(stock, market) / VAR.S(market). This produces the exact same value as SLOPE. Both are algebraically equivalent representations of the OLS regression slope, so they always agree to full precision unless your ranges are misaligned.

Should I use adjusted or raw beta in DCF?

For forecasting future cost of equity — which is what a DCF requires — use adjusted (Blume) beta. Raw beta describes historical sensitivity; adjusted beta corrects for the empirical mean-reversion tendency and better predicts the next 3-5 years. Bloomberg and most sell-side research default to adjusted beta for exactly this reason.

Why does my Excel beta differ from Yahoo Finance's beta?

Yahoo Finance uses 5 years of monthly returns against the S&P 500. If you use 2 years of weekly returns, you'll get a different — usually higher — beta. Bloomberg publishes 2-year weekly adjusted beta. Reuters publishes 5-year weekly raw beta. The discrepancies are almost always down to window, frequency, or adjustment differences, not calculation errors.

How many observations do I need for beta to be statistically valid?

At least 30 observations, ideally 60+. Below 30, standard errors balloon and the confidence interval around beta becomes wider than the number itself is worth. For a 2-year weekly regression (~104 observations), typical standard errors are 0.10-0.20 — a tight enough interval to trust the point estimate for valuation.

The Bottom Line

A defensible beta is not one formula — it's a chain of decisions: frequency, window, index, regression method, adjustment, and unlever/relever. Excel handles every step with SLOPE, LINEST, COVARIANCE.S, and a handful of dynamic-array patterns. What separates a valuation the investment committee approves from one they send back is showing the audit trail: the raw beta, the adjustment, the peer set, the relevering, and the sensitivity.

VeloraAI's Excel add-in accelerates the plumbing — pulling paired price histories, running the regression stack, and building the peer unlever/relever grid — so the analyst can spend time on the judgment calls instead of range-hunting through workbooks. The next time a DCF opens, the beta should be the number nobody argues about, because every input is one click from its source.