EV/EBITDA Screener Excel May 2026: Valuation Multiple Dashboard for Quality Stocks

EV/EBITDA screener Excel - if you came here looking for a way to compare companies on enterprise value to EBITDA, sort the results by sector fair-value band, and turn the answer into a position-sized watchlist without leaving Excel, this guide and the dashboard template that ships with it are built for you. Q1 2026 earnings season is in the books, the multiple compression of the late-2025 sell-off has partly unwound, and the gap between sector medians is wider than it has been since 2023. May is when long-only investors do the slow work of separating cheap-and-quality from cheap-and-broken, and EV/EBITDA is the multiple they reach for first because it cuts through capital structure and one-time accounting noise that contaminate PE.

This is the long-form companion to a free professional-grade Excel dashboard. The dashboard has ten sheets, KPI tiles, embedded charts, conditional-formatted heatmaps, scenario re-rating, sector fair-value bands, an allocation sizer, methodology notes, and a glossary. The post walks through what EV/EBITDA actually measures, why it deserves a permanent slot in your screen stack, how to build the screener in MarketXLS-powered Excel, what the May 2026 tape is showing, and how to use the template's scenario engine to size your conviction.

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Download the dashboard at the bottom of this post. Both files are free. The sample carries a static snapshot dated 2026-05-07; the template version is wired to live MarketXLS formulas and refreshes every time you open it.

EV/EBITDA at a Glance: The Multiple That Travels Across Sectors

Before we open Excel, here is the headline data table the dashboard surfaces on its Cover and Dashboard sheets. These are illustrative reference values from May 7, 2026 - your live template will update them.

The point of the table is not the numbers themselves - they move every week. The point is the spread. Energy can trade at one third the multiple of software for the same dollar of EBITDA, and that gap is normal because the businesses convert EBITDA into free cash flow at very different rates. A good EV/EBITDA screener does not just sort the universe by multiple; it sorts the universe by multiple relative to its sector band and flags the names that are cheap inside their own peer group.

Why EV/EBITDA Beats PE for a Cross-Sector Screen

PE is the most quoted multiple in finance and also the most misleading when you compare across capital structures. A levered REIT and a cash-rich software company can both trade at 25x earnings; what they cost a buyer to actually own is wildly different.

EV/EBITDA fixes three problems with PE in one move:

1. Capital structure neutrality. Enterprise value adds debt and subtracts cash. Two companies with identical operating businesses and different debt loads will look similar on EV/EBITDA, while PE will artificially flatter the levered one (more interest expense pulls down EPS but the share count is unchanged).
2. Tax and amortization noise. EBITDA strips out interest, tax, depreciation, and amortization. That removes accounting noise (large goodwill amortization from past acquisitions) and tax-domicile differences (an Irish-domiciled tech company versus a US-domiciled industrial).
3. Comparability with private deals. Private equity firms underwrite in EV/EBITDA terms. When you screen public companies on the same multiple, you can plausibly ask "what would a strategic or financial buyer pay for this?" - a question PE simply cannot answer.

The trade-off: EBITDA understates true cost for capital-intensive businesses (utilities, telecoms, real estate, heavy industrials) because depreciation is a real economic expense for them. The screener compensates by reporting EV/EBITDA, EV/Revenue, and PE side by side and by showing each name against its sector band. Use the multiple that suits the business you are looking at.

What Q1 2026 Did to the Multiple Map

Three things showed up in the post-Q1 tape that this dashboard is designed to surface.

First, the AI capex names re-rated again. NVDA's EV/EBITDA sits near 39x in our snapshot - a 64% EBITDA margin and triple-digit revenue growth justifies a lot of multiple, but the dispersion versus the broader semiconductor group is at multi-year extremes. The screener flags it instantly: it is the most expensive name in the watchlist, and the dashboard's KPI tile labels it explicitly so you do not have to scroll.

Second, the energy majors are cheap on every measure. XOM and CVX both trade in the 5-6x EV/EBITDA range in our reference snapshot, half the SPX median. The Energy sector fair-value band is 4-8x, which means the names sit in the top half of their own historical range despite the headline cheapness. EV/EBITDA on its own would say "buy"; the sector band reminds you that energy multiples mean-revert downward in commodity downcycles.

Third, post-trough industrials look interesting again. BA's screener row shows a punitive ~74x EV/EBITDA that the conditional formatting paints bright red - but the row also shows the EBITDA base is collapsed (single-digit margin), so the multiple is a denominator artifact rather than a richness signal. The Forward PE column gives you the recovery view. Use the screener as a starting point; never act on a single multiple in isolation.

The dashboard's Dashboard sheet shows the median EV/EBITDA across the watchlist as a KPI tile, the cheapest and most expensive names by name, and the count of names trading below the SPX median. Those six tiles answer the only questions a portfolio manager wants on first open: where is the watchlist priced, what are the extremes, how many names look cheap relative to the broad market, and how much of the watchlist passes a quality filter.

The Strategy: Cheap Multiple Plus Quality Filter

The educational hypothesis behind the template is that low EV/EBITDA, by itself, is a weak signal. Stocks that trade at 5x EBITDA cluster around two very different populations: high-quality businesses going through a cyclical or sentiment trough, and structurally impaired businesses where the multiple is correctly forecasting future earnings collapse. The first group is the classic value opportunity. The second is the value trap.

The way the dashboard separates the two is by layering quality factors on top of the multiple:

• EBITDA margin above 25 percent - the business converts a high share of revenue into operating cash earnings
• Total debt-to-equity below 2.0 - the balance sheet does not amplify cyclical earnings risk
• Forward PE within a normal range - consensus expects earnings to recover or hold

The dashboard's "Quality Tilt" KPI reports the share of the watchlist passing both the margin and leverage filters. A higher number means the universe leans toward genuine value rather than potential traps. You can tighten or loosen the thresholds on the Inputs sheet.

This is an educational hypothesis. Past relationships between low multiples and forward returns do not guarantee future ones, and the screener is not a stock recommendation. It is a starting filter that turns 28 names into the five or ten worth deeper work.

EV/EBITDA Screener Excel: How To Build It With MarketXLS

The dashboard ships ready to use, but the recipe is worth knowing. Here are the verified MarketXLS formulas it relies on, all of which are documented in the MarketXLS function reference.

Core valuation formulas

=EnterpriseValueToEBITDA("AAPL")    ' Returns EV/EBITDA multiple
=EnterpriseValueToRevenue("AAPL")   ' Returns EV/Revenue
=EnterpriseValue("AAPL")            ' Returns enterprise value in dollars
=EBITDA("AAPL")                     ' Returns trailing EBITDA in dollars
=EBITDA_Margins("AAPL")             ' Returns EBITDA margin as decimal
=PERatio("AAPL")                    ' Returns trailing PE
=ForwardPE("AAPL")                  ' Returns forward PE
=MarketCapitalization("AAPL")       ' Returns market cap in dollars
=Revenue("AAPL")                    ' Returns trailing revenue

Quality and risk overlays

=TotalDebtToEquity("AAPL")          ' Returns debt/equity
=ReturnOnEquity("AAPL")             ' Returns ROE
=OperatingMargin("AAPL")            ' Returns operating margin
=Beta("AAPL")                       ' Returns 5y beta
=RevenueGrowth("AAPL")              ' Returns revenue growth
=DividendYield("AAPL")              ' Returns trailing yield

Reference and classification

=Sector("AAPL")                     ' Returns GICS sector
=Industry("AAPL")                   ' Returns GICS industry
=QM_Last("AAPL")                    ' Returns live last price

The screener is a vertical layout: one row per ticker, columns for each metric. Column H carries EV/EBITDA, column L carries EBITDA margin, column N carries debt-to-equity. Conditional formatting on column H paints multiples red above 30x and green below 5x, with a yellow midpoint. The conditional formatting recolors automatically when the formulas refresh.

The "median multiple below SPX" KPI uses a COUNTIFS against the SPX reference value:

=COUNTIFS(H11:H38,"<14.5")

The "cheapest name" KPI uses a combined INDEX-MATCH-MIN to label which ticker has the lowest multiple:

=INDEX(A11:A38,MATCH(MIN(H11:H38),H11:H38,0))&" ("&TEXT(MIN(H11:H38),"0.0")&"x)"

The "quality tilt" KPI reports the share of names that pass both filters:

=ROUND(COUNTIFS(L11:L38,">0.25",N11:N38,"<2")*100/28,0)

These are real, working formulas that ship inside the template. Open the dashboard, change a ticker on the Inputs sheet, and every KPI updates.

What's Inside the Template: All Ten Sheets Walked Through

The template ships ten sheets, each with a tab color, frozen panes, and a footer that lists the MarketXLS functions used on the sheet. Open it once and the structure should feel like a designed product, not a grid of cells.

1. Cover

Branded navy cover page with the dashboard title, the May 2026 edition tag, the data-as-of date, and a table of contents. Hidden gridlines, MarketXLS branding in gold against the navy background. Nothing operational happens here - it is the front door.

2. How To Use

A six-step tutorial that walks a first-time user from "open the file" to "size a position." Each step references the specific cells you need to change and the sheets that update in response. There is also a MarketXLS Functions Used box at the bottom listing every formula touched on this tour.

3. Inputs

The yellow tab. This is where every downstream calculation gets its parameters. Four yellow input cells take portfolio size, the EV/EBITDA ceiling that defines "cheap", the maximum debt-to-equity you are willing to tolerate, and the minimum EBITDA margin. Three dropdown menus pick a Valuation Stance (Conservative, Balanced, Aggressive), a Sector Tilt (Quality, Cyclical, Defensive, Yield, Growth), and a Risk Filter (Lenient, Standard, Strict). Below the input block sits the editable watchlist - 28 tickers in column B, gold-bordered yellow cells. Replace any ticker and the rest of the workbook re-prices.

4. Dashboard

The headline sheet. The KPI tile row across the top reports six headline numbers in big bold type: median EV/EBITDA, cheapest name, most expensive name, count below the watchlist median, count below the SPX median, and quality tilt percentage. Below the tiles sits the screener: 15 columns wide, 28 rows deep, conditional formatting on every quantitative column. Two embedded bar charts compare EV/EBITDA by ticker and EBITDA margin by ticker. A print area is set on the dashboard so it lays out cleanly in landscape orientation. Hidden gridlines, frozen panes at row 11, freeze pane at column A.

5. Scenario Analysis

Picks the cheapest five names from the watchlist and walks each through five re-rating scenarios: minus 25 percent multiple compression, minus 10 percent, base case (current multiple), plus 10 percent, plus 25 percent expansion. Implied price for each scenario is computed as (target multiple x EBITDA - net debt) / shares outstanding. EBITDA is held constant - this is a multiple-only sensitivity. A clustered bar chart lets you compare the upside and downside ranges side by side.

6. Valuation Playbook

Sector-by-sector cheap, fair, and expensive bands. Each row shows a sector, the cheap-below threshold, the fair range, the expensive-above threshold, and the watchlist's current median for that sector with a green/yellow/red fill that flags which side of the band the sector is on. The bands are starting heuristics drawn from long-run market history - they are editable in cells B5:B15 and D5:D15. A 38-character note column explains why each sector deserves the band it gets.

7. Allocation Sizer

Takes the portfolio size from the Inputs sheet and computes a weight for every name in the watchlist using a transparent factor recipe: 0.6 x normalized cheap-multiple score plus 0.4 x normalized margin score minus a debt penalty above 2.0 D/E. Weights normalize to 100 percent. The dollar allocation column multiplies each weight by the portfolio size, formatted as currency. A pie chart on the right shows the allocation mix at a glance. Conditional formatting paints the EV/EBITDA column green-to-red and the EBITDA margin column red-to-green.

8. Sector Comparison

Aggregates the 28-name watchlist into sector medians for EV/EBITDA, EV/Revenue, PE, and EBITDA margin. Sectors are sorted cheap-first, so the top of the table shows you where the value pockets are. Three color scales recolor the sector medians: green is cheap, red is rich for valuation columns, and the EBITDA margin column inverts the gradient (green is high). A horizontal bar chart on the right shows median EV/EBITDA by sector.

9. Methodology

A one-page explainer covering nine topics: why EV/EBITDA, the universe definition, multiple calculation, sector fair-value bands, the quality tilt score, scenario analysis math, the allocation score, limitations of the model, and data sources. Read this once and you will understand every number on every other sheet.

10. Glossary & Disclaimer

Fourteen term definitions covering EV/EBITDA, enterprise value, EBITDA, EV/Revenue, PE, forward PE, net debt, multiple expansion and compression, re-rating, value trap, quality tilt, and total debt-to-equity. Followed by the standard educational disclaimer.

Reading the May 2026 Snapshot: Three Takeaways

Open the sample file and a few patterns jump out. Treat the patterns as illustrative; rerun them in the live template with current data before drawing conclusions.

The Energy sector trades at the bottom of its band. Median EV/EBITDA across XOM and CVX in the reference snapshot is in the 5-6x range. Energy's fair band is 4-8x. That puts the sector mid-band, not bottom of band. After Q1 2026's continued downward revisions to crude price expectations, the cheapness is partly justified - but the dispersion versus refiners and integrated majors is wide enough that the screener calls out which name has the better quality profile (Chevron's higher dividend yield versus Exxon's lower debt).

Information Technology bifurcates. ORCL screens as cheaper than the IT median; NVDA screens as the most expensive name in the watchlist. The dashboard's KPI tile labels both explicitly. AAPL and MSFT sit in the upper-mid range of the IT band. The interesting structural insight: the IT sector median is around 23x in the snapshot, comfortably inside the historical 16-24x band, which means the headline "tech is expensive" narrative is misleading. The expensive part is concentrated in three or four AI-leveraged names; everything else is in band.

Healthcare looks underpriced relative to its band. UNH, JNJ, and PFE all trade between 11x and 14x in the snapshot, against a healthcare fair band of 10-16x. The sector median sits below the SPX median. PFE in particular shows the value-trap risk: 11x EV/EBITDA but a forward PE that is dramatically lower than its trailing PE because consensus expects an EPS recovery from depressed COVID-era comps. The screener flags it; you decide whether the recovery is real.

The Sector Comparison sheet shows the sector medians side by side. The Valuation Playbook sheet shows where each sector median sits inside its own band. The two together are the dashboard's most-used analytical surface.

EV/EBITDA Screener Excel: How To Apply It Day-to-Day

The dashboard is most useful as a weekly screen, not a daily one. EV/EBITDA does not change meaningfully from one trading day to the next; what changes is the relative position of names inside their sector bands as the broader tape moves. A workflow that works:

Monday open, weekly review: Open the template. Refresh. Read the six KPI tiles on the Dashboard. If the median EV/EBITDA has moved more than 0.5x since last week, scroll the screener to find which names drove the shift.

Mid-week earnings filter: Companies that report earnings get re-rated immediately. If a watchlist name reported between your previous check and now, look at the screener row - the EBITDA value will reflect the new trailing twelve months, and the multiple may have shifted materially. The Forward PE column tells you whether consensus revised next-year EPS up or down.

Monthly sector rotation: Open the Sector Comparison and Valuation Playbook sheets together. If a sector median has crossed below its cheap threshold, it is a candidate for an overweight; if a sector median has crossed above its expensive threshold, it is a candidate for a trim. Use the conditional formatting fills as your visual anchor.

Quarterly position sizing: Open the Allocation Sizer. If your portfolio size on the Inputs sheet has changed materially - inflows, outflows, drawdown - the dollar allocation column re-prices everything. Compare the allocation mix in the pie chart to your actual portfolio and rebalance toward the model.

The point of the dashboard is not to give you the answer. It is to compress the analytical work of looking at 28 names across 12 metrics down to a few minutes per week.

Common Pitfalls With EV/EBITDA Screens

Three things to watch for when you apply this template - or any EV/EBITDA screener - to a real watchlist.

Negative or near-zero EBITDA. Companies in trough years can post tiny EBITDA, which generates astronomical EV/EBITDA multiples that distort sector medians and color scales. The dashboard handles this by using IFERROR-wrapped MEDIAN formulas in the Sector Comparison sheet, but on the screener itself, very high multiples will appear in bright red. Treat any name with EV/EBITDA above 50x as a denominator problem, not a richness signal, until you investigate.

Stock-based compensation. Some companies (particularly software) report EBITDA that adds back stock-based compensation. When SBC is large - 10-20 percent of revenue - the reported EBITDA overstates true cash earnings. EV/EBITDA looks lower than it should. The dashboard reports EV/Revenue alongside EV/EBITDA so you can sanity-check; a software name with low EV/EBITDA but high EV/Revenue is a likely SBC-heavy story.

Capital intensity ignored. EBITDA does not subtract maintenance capex. Utilities, telecoms, and heavy industrials need to spend a lot every year just to keep the lights on. Their reported EBITDA is therefore a poor proxy for distributable cash. The dashboard partly compensates by widening sector bands for these groups (utilities at 10-13x, real estate at 16-22x). For deeper work, use EV/EBIT or EV/Free Cash Flow on the same names.

The Methodology sheet inside the template lists these limitations explicitly so anyone using your dashboard knows what the model can and cannot tell them.

How This Template Compares to a Bloomberg Function

A common question when we publish dashboards like this: why not just use the BBG terminal? Three honest answers.

First, the terminal is built for traders who want one screen showing one company. This dashboard is built for investors who want one screen showing 28 companies and one ranking. They are different jobs.

Second, BBG's EV/EBITDA pull is fast and correct, but the comparison view requires you to build a custom screen and re-build it every time you add a name. The dashboard's screener is permanent and editable.

Third, the dashboard's Excel-native format means you own the data, the formulas, and the layout. You can extend it - add an EV/EBIT column, add a sector neutralization step, swap the watchlist for a custom universe, integrate it with your portfolio file - in ways the terminal does not allow. If you want a Bloomberg alternative for EV/EBITDA work, this is one practical example of what that looks like.

Frequently Asked Questions

What is a good EV/EBITDA ratio?

EV/EBITDA "good" depends entirely on the sector. Energy companies typically trade at 4-8x; software at 16-24x; real estate at 16-22x. The number 14.5x is the long-run S&P 500 median. A name trading below its sector's typical band is screening cheap; a name above is screening rich. The dashboard's Valuation Playbook sheet gives you the bands; the screener tells you where each name sits inside its band.

Why use EV/EBITDA instead of PE?

EV/EBITDA is capital-structure neutral (it includes debt and excludes cash via enterprise value), it strips out interest, taxes, depreciation, and amortization, and it is comparable to the multiples private equity buyers use for whole-company purchases. PE is distorted by leverage, tax domicile, and accounting choices. For cross-sector comparisons, EV/EBITDA is the cleaner multiple. For same-sector comparisons of stable, low-debt businesses, PE works fine.

Can I use this EV/EBITDA screener Excel template on my own watchlist?

Yes. Open the Inputs sheet and replace any ticker in column B. The Dashboard, Allocation Sizer, Sector Comparison, and Scenario Analysis sheets all refresh from the watchlist. There is no hard cap on watchlist size; the screener formulas work on any vertical range. If you grow the list above 28 names, extend the Dashboard table by inserting rows above row 38 - the conditional formatting and chart references will track the new range as long as you insert rather than overwrite.

How often should I refresh the EV/EBITDA screener?

EV/EBITDA does not move materially day to day. A weekly refresh is enough for most long-only investors. The biggest re-pricing events are earnings releases (which change the EBITDA denominator) and material price moves of more than 10 percent (which change enterprise value). If you are tracking a watchlist that has earnings in a given week, refresh after the prints; otherwise weekly is fine.

Does EV/EBITDA work for banks and insurers?

Not well. Financials use balance-sheet-driven valuation - PE, price-to-book, and price-to-tangible-book are the standard multiples. The dashboard includes JPM and BRKB for completeness, but the Sector Comparison sheet's Financials row should be read with caution. For deeper financial-sector work, use a price-to-book screener instead.

What is the difference between trailing and forward EV/EBITDA?

Trailing EV/EBITDA uses the EBITDA from the past four reported quarters. Forward EV/EBITDA uses consensus estimated EBITDA for the next twelve months. The dashboard reports trailing EV/EBITDA on the screener and trailing PE plus Forward PE side by side so you can compare the two. A wide gap between trailing and forward (forward much lower than trailing) usually signals the consensus expects an earnings recovery; a narrow gap signals stable expectations.

Download the Templates

Download the EV/EBITDA screener Excel templates:

• - Pre-filled with a May 7, 2026 reference snapshot. Every data cell carries a comment showing the MarketXLS formula that would produce it. Use this version to study the layout and the formula recipe.
• - Live MarketXLS formulas across all 10 sheets. Open it with MarketXLS installed and the screener refreshes with current EV/EBITDA, EBITDA margin, debt-to-equity, beta, and sector data.

Both files include the same ten-sheet structure: Cover, How To Use, Inputs, Dashboard, Scenario Analysis, Valuation Playbook, Allocation Sizer, Sector Comparison, Methodology, and Glossary & Disclaimer. Both have hidden gridlines on the Cover and Dashboard, frozen panes everywhere, color-coded tabs, and the standard MarketXLS footer on every sheet.

The Bottom Line

EV/EBITDA is the multiple that travels best across sectors and capital structures, and a dedicated screener Excel template earns its keep when you need to rank 20 to 50 names by enterprise multiple, sector band, and quality factor in a single afternoon rather than a single week. The dashboard ships with a starter watchlist of 28 large caps, a full visual layer (KPI tiles, embedded charts, conditional formatting), and a transparent quality tilt that filters out the obvious value traps. Edit the watchlist, tune the bands, change the scenario weights - it is your file.

If you want to push deeper into MarketXLS-powered valuation work, the free cash flow yield screener, the forward earnings yield dashboard, and the equity risk premium dashboard are companion templates in the same premium series. Each ships with the same ten-sheet structure and the same design rules.

For a deeper look at how MarketXLS exposes 1,100+ functions for fundamental analysis, valuation, options pricing, technicals, and live streaming data inside Excel, visit marketxls.com or book a personalized demo with the team.