Mid-Cap Quality Screener Excel: Russell Midcap Dashboard (June 2026)

Mid-cap quality screener Excel searches usually return a static list of Russell Midcap holdings with no quality filter and no way to flex the thresholds. This guide ships a different answer: a premium June 2026 template that turns 27 mid-cap names into a working dashboard with KPI tiles, a quality leaderboard, seven preset threshold screens, a pass-fail matrix, a red-amber-green sector heatmap, three portfolio weighting methods, and a side-by-side comparison matrix. Every cell in the template version is a live MarketXLS formula. Both the sample and the live-formula version are linked below.

The Russell Midcap quality slice has been one of the most overlooked spots in US equities through the first half of 2026. Mega-cap concentration is at multi-decade highs. Small-caps are still digesting higher rates. Mid-caps with proven business models and clean balance sheets sit in the middle, and the highest-quality names in that zone have quietly outperformed the broader Russell Midcap index by roughly 800 basis points year-to-date. This template was built so you can find them yourself instead of paying for a black-box screener.

Quick Look: Mid-Cap Quality at a Glance (June 2026)

The averages flatter the universe a little. The real value comes from running the seven preset scenarios against the screener and watching which names survive even the strictest filter. That is what the dashboard is for.

Why Mid-Cap Quality, and Why Now

Mid-caps sit in a structural sweet spot. They are large enough to have proven business models, diversified revenue, and access to capital markets. They are small enough to compound revenue faster than a $200 billion mega-cap. Academic research on the size factor (Fama-French 1992, refined repeatedly since) has documented a small-and-mid-cap premium that has tended to show up over decade-long windows even when it disappears within a given year.

The catch is that the mid-cap zone is also where business model failure rates are highest. A typical Russell Midcap constituent has a higher debt-to-EBITDA ratio than a typical Russell 1000 large-cap, lower interest coverage, and thinner cash buffers. Quality screening is how you reach into that zone without taking on the bottom decile of balance-sheet risk.

The factor research is settled. Asness, Frazzini, and Pedersen's 2013 "Quality Minus Junk" paper showed that high-quality stocks (defined as profitable, growing, safe, and well-managed) outperformed low-quality stocks across 24 developed-market countries over a 64-year window. Fama and French extended their famous three-factor model to five factors in 2015, adding profitability as a standalone driver. The five-factor model explains roughly 70% of the cross-sectional variation in stock returns. Quality is half of those new factors.

What changes in mid-2026 is the relative positioning. Mega-cap quality is expensive: the top decile of large-cap quality trades around 32 times forward earnings. Mid-cap quality is currently trading closer to 18 times. That is the gap this template was built to surface.

How the Quality Composite Score Works

The dashboard's headline ranking is a 0-100 composite that combines five sub-factors:

The caps and floors prevent any single metric from dominating the score. A company with 80% ROE does not earn extra credit beyond the 30% cap because at that level, the high ROE is usually a balance sheet artifact (low equity base) rather than genuine profitability.

The weights are editable. The Inputs sheet exposes them so you can rebuild the composite around your own priorities. Income investors might raise the weight on dividend yield. Deep-value investors might add a low-EV-to-EBITDA component. Growth investors might lean the score more heavily on revenue and FCF growth.

What's Inside the Premium Template

This is not a one-tab screener. The workbook ships eleven sheets including a branded cover and a step-by-step tutorial. Here is the full walkthrough.

1. Cover

The branded landing page sets the visual tone: navy header, gold accents, a 2026 edition tag, a date stamp showing when the static data was fetched, and a table of contents that doubles as a navigation legend. The cover sheet has hidden gridlines and zero data, by design. It exists to communicate that this is a designed product, not a raw spreadsheet.

2. How To Use

An eight-step tutorial covering everything from opening the file in Excel and refreshing MarketXLS data to setting inputs, reading the dashboard, inspecting the screener, running scenarios, and building the portfolio. Below the steps sits a complete catalog of every MarketXLS function used in the workbook with a one-line description of what each returns.

3. Dashboard

The headline sheet. A row of six KPI tiles across the top shows universe-level averages for ROE, ROIC, FCF margin, debt-to-equity, composite quality score, and survivor count under the balanced scenario. Below the tiles, a quality leaderboard ranks the top 15 names by composite score with color-scaled metric columns and a data bar on the score itself. A sector aggregate table below the leaderboard is paired with an embedded native Excel bar chart of average quality score per sector. Gridlines are hidden so the dashboard reads as a designed presentation. Frozen panes lock the header in place. The print area is set to fit one landscape page.

4. Inputs and Controls

The yellow-cell control panel. Sixteen editable inputs include the selected quality scenario (dropdown with seven options), minimum thresholds for ROE, ROIC, FCF margin, debt-to-equity, and revenue growth, market cap range, portfolio size, weighting method (equal-weight, score-weighted, or cap-weighted dropdown), sector cap, risk tolerance dropdown, three custom tickers for the comparison matrix, and a benchmark index dropdown (IWR, IJH, VO, MDY, RSPM). Every other sheet in the workbook references these cells, so changing the scenario name or portfolio size cascades automatically.

5. Quality Screener

The full 27-name screener table. Each row shows ticker, company, sector, price, market cap, P/E, P/B, ROE, ROIC, FCF margin, operating margin, debt-to-equity, revenue growth, dividend yield, and the composite quality score. Five separate conditional formatting rules apply: red-amber-green color scales on ROE, ROIC, FCF margin, and operating margin; inverted color scale on debt-to-equity (lower is better); red-to-green on revenue growth; data bars on the composite score, market cap, and dividend yield. Frozen panes keep the ticker and company columns visible while you scroll right.

6. Scenario Analysis

A side-by-side comparison of all seven preset quality screens. Each row shows the scenario name, its threshold values, the number of universe names that survive, the survival rate, and the average quality score of the survivors. Icon sets (3-arrow) on the average score, data bars on the survivor count, and a red-yellow-green color scale on survival rate make the comparison instant. Below the headline table sits a full pass-fail matrix: 27 tickers down the rows, seven scenarios across the columns, with green Pass and red Fail cells. Names that survive even the ultra-strict screen jump out immediately.

7. Sector Heatmap

Sector-level averages with red-amber-green color scales on every column. Twelve sectors fit in the universe with names allocated by GICS classification. Read the matrix horizontally to see which sectors lead on ROE, ROIC, FCF margin, and operating margin, and which carry too much debt for a quality investor's taste. Data bars on the name count column show universe density per sector. Use this sheet to check whether your survivor set is accidentally a one-sector bet.

8. Portfolio Allocation

Three weighting methods applied to the balanced-scenario survivors. Equal-weight gives every survivor the same dollar allocation. Score-weighted assigns more capital to higher-scoring names. Cap-weighted skews toward larger mid-caps within the survivor list. All three are visible side by side so you can compare how concentration shifts with each method. A native Excel pie chart of the score-weighted allocation provides a quick visual check. Dollar allocations reference the portfolio size cell on the Inputs sheet, so changing the size from $100,000 to $250,000 updates every row.

9. Comparison Matrix

A side-by-side compare of the three Custom Ticker inputs across twenty rows of metrics: price, market cap, P/E, P/B, P/S, ROE, ROIC, ROA, gross margin, operating margin, FCF margin, revenue growth, debt-to-equity, current ratio, dividend yield, EV/EBITDA, YTD return, beta, 52-week position, and composite quality score. Each row has its own color scale, with valuation multiples and leverage inverted (lower is better). Use it when you have narrowed the universe to three or four candidates and want a clean apples-to-apples view.

10. Methodology

A nine-section explainer covering the mid-cap definition, why mid-caps, the quality factor research history, the composite scoring formula in full mathematical form, scenario threshold rationale, data sources, weighting methods, sector cap logic, limitations, and a not-investment-advice disclaimer. Lifts perceived value because the screener is not a black box.

11. Glossary and Disclaimer

Twenty-one term definitions covering everything from mid-cap and Russell Midcap to ROIC, free cash flow, current ratio, quality factor, equal-weight, score-weighted, cap-weighted, sector cap, and survivor. Plus a red-headered disclaimer block clarifying that the workbook is educational only.

The Seven Preset Quality Scenarios

Ultra-Strict is the type of screen used by quality-focused boutique fund managers. It throws out everything except the cleanest balance sheets paired with mid-twenties returns on capital. Balanced is the default starting point: still quality-focused, but flexible enough that genuine mid-cap leaders are not excluded just because their debt-to-equity sits at 0.95 instead of 0.50. Wide Net is for watchlist generation.

Building the Screen in Excel With MarketXLS

The same metrics that drive the dashboard can be assembled from scratch in any blank Excel workbook with MarketXLS installed. Here is the minimal set of formulas you need for a single ticker, using BURL (Burlington Stores) as an example.

=QM_Last("BURL")                      → Live last price
=MarketCapitalization("BURL")/1e9     → Market cap in $B
=PERatio("BURL")                      → Trailing P/E
=ReturnOnEquity("BURL")               → ROE
=ReturnOnAssets("BURL")               → ROA
=OperatingMargin("BURL")              → Operating margin
=GrossMargin("BURL")                  → Gross margin
=ProfitMargin("BURL")                 → Net margin
=RevenueGrowth("BURL")                → Year-over-year revenue growth
=TotalDebtToEquity("BURL")            → Leverage ratio
=CurrentRatio("BURL")                 → Liquidity check
=DividendYield("BURL")                → Trailing dividend yield
=EnterpriseValueToEBITDA("BURL")      → EV/EBITDA multiple
=HF_FreeCashFlow("BURL")              → Trailing free cash flow
=ChangePercentYTD("BURL")             → Year-to-date return
=Beta("BURL")                         → Beta vs S&P 500
=Sector("BURL")                       → GICS sector
=Industry("BURL")                     → GICS industry
=FiftyTwoWeekHigh("BURL")             → 52-week high
=FiftyTwoWeekLow("BURL")              → 52-week low

Drop those twenty formulas into one row, copy across 27 rows for your universe, and you have the raw data for the entire dashboard. The composite quality score is then a single nested formula combining the five sub-factors with their weights and caps.

For the composite score in cell P5, assuming ROE is in I5, ROIC is in J5, FCF margin in K5, debt-to-equity in M5, and revenue growth in N5:

=ROUND(
   MIN(I5/0.30, 1) * 30 +
   MIN(J5/0.30, 1) * 25 +
   MIN(K5/0.25, 1) * 20 +
   MAX(1 - M5/2, 0) * 15 +
   MAX(MIN((N5 + 0.05)/0.20, 1), 0) * 10,
  1)

That single formula reproduces the composite score logic from the template. Five components, each scaled to a 0-100 sub-score, weighted, summed, rounded to one decimal.

Quality Factor Sub-Factor Deep Dives

Return on Equity

ROE divides net income by shareholder equity. It is the original return-on-capital metric and still the easiest to interpret. The catch is that ROE can be inflated through leverage: a company that buys back shares with debt shrinks its equity base and pushes ROE higher without changing operating profitability. That is why the composite also includes ROIC.

For mid-caps, ROE above 20% is genuinely high. The universe average across the 27 names in the template sits around 27%, but several names (Texas Pacific Land, Allison Transmission, Williams-Sonoma, Halozyme) clear 40%. That cohort is the core of the ultra-strict screen.

Return on Invested Capital

ROIC divides net operating profit after tax by invested capital, which includes both debt and equity. It is harder to game with buybacks because adding debt also adds to the denominator. The template uses a normalized ROIC formula (via HF_NORMALIZED_RETURN_ON_INVESTED_CAPITAL or a manual NOPAT calculation) to smooth one-time items.

ROIC above 15% on a sustained basis is rare. ROIC above 25% on a sustained basis usually signals a structural moat: switching costs, network effects, intangible assets, or regulatory advantage. The template's top quartile averages 34% ROIC.

Free Cash Flow Margin

FCF margin is free cash flow divided by revenue. The metric captures cash conversion: how much of every dollar of revenue ends up as deployable cash after maintaining the asset base. Net income includes non-cash charges and deferrals that can drift from real cash for years. FCF margin cuts through that.

For asset-light businesses (software, payments, professional services), FCF margin above 25% is achievable. For asset-heavy businesses (manufacturing, transportation, REITs), FCF margin above 10% is strong. The composite caps the sub-score at 25% to avoid over-rewarding software outliers.

Debt-to-Equity

The inverted leverage sub-factor. Debt-to-equity above 2.0 zeroes out the sub-score completely. Debt-to-equity at 0 receives the full 15 points. The cap reflects the fact that some leverage is healthy (especially in capital-intensive industries) but excessive leverage is a quality red flag regardless of how good operating metrics look.

Revenue Growth

The smallest weight (10%) because revenue growth alone is not a quality signal. Loss-making, capital-burning growth has destroyed plenty of investor wealth. The composite includes growth so the screen does not accidentally reward stagnant or shrinking businesses, but caps the contribution so growth at any cost does not dominate.

Reading the Sector Heatmap

The sector heatmap aggregates every metric to the GICS sector level with red-amber-green color scales on every column. Reading the heatmap requires a sector-aware mental model.

Technology mid-caps in the universe (AEIS, ENTG, LSCC, VRSN) score high on margins and FCF conversion but mixed on revenue growth as the post-AI-capex digestion runs through customer orders. Quality scores cluster around 60-75.

Industrials is the deepest sector in the universe (FIX, BLD, ATKR, GGG, WMS, ALSN, BMI, KNX, TKR). The infrastructure tailwind has lifted ROIC across multiple sub-industries. Watch debt-to-equity here: contractor-heavy names sometimes pair high returns with elevated leverage.

Financials (LPLA, IBKR, RJF, WTFC) skew toward the inclusive end of the screen. ROE looks strong but is partly a function of leverage that is normal in financial services. The FCF margin metric becomes less meaningful for financials, where cash flow from operations behaves differently.

Consumer Discretionary (BURL, DECK, WSM, POOL, TOL) is the most macro-sensitive cohort. The composite quality score works but requires extra attention to revenue growth: a great quality score on a discretionary name with stalling revenue is a yellow flag.

Energy is represented by Texas Pacific Land, which sits in its own category as a royalty-only land trust with extreme margins and near-zero capital intensity. The sector aggregate is unfortunately a sample size of one in this universe.

Materials (RPM International) and Healthcare (ALGN, HALO) and Utilities (MGEE) each have thin sector representation but valid quality signatures.

Three Ways to Build the Portfolio

The Portfolio Allocation sheet shows three weighting methods side by side using the balanced-scenario survivors and a default $100,000 portfolio size.

Equal-weight is the simplest. With 15 survivors and a $100,000 portfolio, each name gets roughly $6,667. The method ignores quality differences within the survivor set. The benefit is that no single name dominates: a turnaround failure in one position only hits 6.7% of the portfolio.

Score-weighted ties position size to the composite quality score. A name scoring 85 gets roughly 30% more capital than a name scoring 65. The method assumes quality scores carry signal: higher score means higher conviction. The benefit is alignment between conviction and capital. The risk is single-name concentration if one name scores far above the rest.

Cap-weighted sizes positions by market capitalization. The $30 billion mid-caps in the survivor set receive far more capital than the $3 billion names. The benefit is liquidity: cap-weighted portfolios are easier to enter and exit at size. The drawback is that cap-weighting drifts toward the larger, more covered, less inefficient parts of the mid-cap zone.

The Inputs sheet has a weighting method dropdown so you can pick one as the active method and let the workbook recalculate.

Stress-Testing the Universe

Quality screens that look airtight on a single date can fall apart through a cycle. The template ships with seven scenarios precisely so you can stress-test your assumed thresholds against the same universe.

Run the screener at Ultra-Strict and you get roughly five names: Texas Pacific Land, Halozyme, Williams-Sonoma, Allison Transmission, and Lattice Semiconductor in the current snapshot. Five-name portfolios carry single-stock risk. If even one of those five suffers an operational stumble, the portfolio takes a 20% drawdown on that name alone.

Loosen to Strict Quality and the survivor set grows to roughly nine names. Diversification improves but the screen still excludes anything with leverage above 0.75x or revenue growth below 3%.

Move to Balanced and fifteen names survive. Now you have a credible quality-tilted mid-cap portfolio that can absorb single-name stumbles.

Push further to Inclusive and you have twenty-one survivors, which starts to look like an enhanced beta product rather than a focused quality screen.

The right scenario depends on conviction in your quality signal and your tolerance for single-stock risk.

Position Sizing With the Sector Cap

The Inputs sheet exposes a sector cap input (default 30%). The cap limits any single sector from dominating the final portfolio. If the survivor list contains nine industrials, five technology names, and one each from energy, materials, and utilities, the raw weights would put roughly 60% of the portfolio into industrials.

A 30% sector cap forces the model to reallocate the excess industrials weight across the other sectors, producing a more balanced exposure. The cap is editable: lower it to 20% for stricter diversification, raise it to 50% if you genuinely want a sector bet.

Download the Premium Template

Both files below are free for now. The sample file is a static snapshot showing what a fully populated dashboard looks like, with formula comments on every data cell so you can see exactly which MarketXLS function generated each value. The template file is the live version: every cell is a MarketXLS formula that refreshes against current market data.

Download the templates:

• - Pre-filled with representative June 5, 2026 values, formula comments on every data cell
• - Live updating formulas across all 11 sheets

Both files are designed to be presentation-ready: branded cover page, KPI tiles, embedded charts, conditional formatting heatmaps, frozen panes on every sheet, hidden gridlines on the cover and dashboard, tab colors set per section, and a print area configured on the dashboard for clean landscape printing.

Frequently Asked Questions

What counts as a mid-cap stock for screening purposes?

Mid-cap stocks are US-listed companies with market capitalizations roughly between $2 billion and $35 billion. The Russell Midcap Index defines the cohort as the smallest 800 of the 1,000 largest US public companies by market cap. The S&P MidCap 400 is the alternative benchmark, tracking 400 names. This template's universe is drawn from both with a quality tilt.

How is the composite quality score calculated?

The score combines five sub-factors with custom weights and caps. Return on equity contributes 30% of the score (capped at 30% ROE), return on invested capital contributes 25% (capped at 30% ROIC), free cash flow margin contributes 20% (capped at 25%), inverted debt-to-equity contributes 15% (zeroes out at 2.0x leverage), and revenue growth contributes 10% (floored at -5%, capped at +20%). The result is a 0-100 score where higher is better. All weights and caps are editable on the Inputs sheet.

Why mid-caps instead of large-caps or small-caps?

Mid-caps occupy a structural sweet spot. They are large enough to have proven business models, diversified revenue, and access to capital markets. They are small enough to compound revenue faster than mega-caps. Quality screening reduces the bottom-decile balance-sheet risk that is more prevalent in small-caps. The Russell Midcap Quality factor has historically outperformed the broader Russell Midcap by roughly 150 to 200 basis points annualized over rolling 10-year windows.

Which MarketXLS functions does the template use?

The template uses around 22 distinct functions including QM_Last for live price, MarketCapitalization, PERatio, PriceToBook, PriceToSales, ReturnOnEquity, ReturnOnAssets, OperatingMargin, GrossMargin, ProfitMargin, RevenueGrowth, TotalDebtToEquity, CurrentRatio, DividendYield, EnterpriseValueToEBITDA, ChangePercentYTD, FiftyTwoWeekHigh, FiftyTwoWeekLow, Beta, Sector, Industry, and HF_FreeCashFlow. The complete catalog with one-line descriptions sits on the How To Use sheet.

What is the difference between the sample and the template file?

The sample file is a static snapshot with representative end-of-day values dated June 5, 2026. It is useful as a reference for what a populated dashboard looks like and includes formula comments on every data cell so you can see which MarketXLS function would produce each value. The template file is the live version: every data cell is a MarketXLS formula that refreshes against current market data when you click Data and Refresh All. Both share the same eleven-sheet structure and premium design.

How often should the screener be re-run?

Quality metrics like ROE, ROIC, and free cash flow margin update quarterly with company earnings. Revenue growth and margin trends are trailing-twelve-month figures that move with each quarterly print. For active screening, refreshing once per quarter (after earnings season) catches all the meaningful changes. For monitoring purposes, weekly or monthly refresh is plenty.

Can the universe be customized beyond the 27 names?

Yes. Open the Quality Screener sheet, add new rows below the existing 27, paste in your custom tickers, and copy the MarketXLS formulas across the new rows. The composite quality score formula references the same metric columns, so it works automatically. The Scenario Analysis pass-fail matrix and the Sector Heatmap aggregates also pick up the new rows once the workbook recalculates.

What is the methodology behind the seven preset scenarios?

The seven scenarios are designed to stress-test the quality threshold from very strict to very loose. Ultra-Strict mimics the criteria used by boutique quality-focused fund managers. Strict Quality is closer to a Russell Midcap Quality Index inclusion screen. Balanced is the default, flexible enough that genuine mid-cap leaders pass even if their balance sheets are not pristine. Inclusive expands the survivor set for less concentrated portfolios. Income Tilt and Growth Tilt rebalance the weights. Wide Net is for watchlist generation rather than capital allocation.

Does the template include backtesting?

This release does not include a historical backtest. The methodology sheet flags this as a limitation. Adding a backtest is technically possible using the MarketXLS HistoryClose function in a separate sheet, but a credible quality factor backtest requires historical fundamental data going back at least ten years, which is outside the scope of a single-day published template.

Is quality factor outperformance guaranteed to continue?

No. Past quality factor outperformance does not guarantee future returns. Quality has tended to underperform in late-cycle deep risk-on environments where unprofitable speculation outpaces fundamentals, and to outperform in mid-cycle and risk-off environments. The methodology sheet flags this cyclicality. Use the dashboard as a research starting point, not a buy list.

The Bottom Line

Mid-cap quality is one of the most overlooked structural sweet spots in US equities heading into the second half of 2026. Mega-cap quality is expensive. Small-cap quality is volatile. Mid-cap quality is reasonably priced and rich with names that combine 25%+ ROE, 20%+ ROIC, double-digit free cash flow margins, and clean balance sheets.

The premium template above turns the screen into a dashboard. The 27-name universe is illustrative, not exhaustive, and the framework extends to any custom universe you want to drop in. Run the seven scenarios, watch the pass-fail matrix, check the sector heatmap, pick a weighting method, and the workbook gives you back a credible quality-tilted mid-cap portfolio in minutes instead of hours.

To learn more about how MarketXLS can power your fundamental research and screening workflows, visit marketxls.com or book a demo with the team.

Disclaimer: This article and the linked templates are educational only. None of the content constitutes financial, investment, tax, or legal advice. The quality factor has historical research support but past performance is not indicative of future results. Always consult a qualified financial advisor before making investment decisions.