Small Cap Value Screener Excel: Russell 2000 Valuation Dashboard for May 2026

Small cap value screener Excel - if you have spent the last eighteen months watching the S&P 500 hit fresh highs while the Russell 2000 traded sideways, you already know why this screener exists. Large cap multiples sit near multi-year extremes. Small cap value sits at one of the widest historical discounts to the broad market on record. The question is not whether the gap is interesting. The question is which specific small cap value names are cheap because the market is wrong, and which are cheap because the underlying business is broken. A spreadsheet that mashes PE, P/B, EV/EBITDA, ROE, leverage, and margins into one filtered dashboard is the cleanest way to tell those two groups apart.

The premium template attached to this post is built around that exact workflow. Eighteen Russell 2000-style names, eleven sheets, a KPI tile row up top, two charts, conditional-formatted heatmaps on every metric column, a quality overlay that flags value traps in red, a re-rating scenario engine, and a sector heatmap so you can see where the cheap multiples actually cluster. Live MarketXLS formulas refresh every cell. Drop in your own tickers and the entire workbook reprices.

This guide walks through what is inside, how to use the dashboard, and the small cap value framework the screener is built around.

Why small cap value in May 2026

Three forces have stretched the small cap value setup into rare territory:

The historical Russell 2000 vs S&P 500 PE discount averages around 10 to 15 percent. The current gap sits closer to 27 percent. Small cap value as a factor (the bottom quintile of book-to-price or PE) screens even cheaper than the broad small cap index because the growth-oriented small caps trade closer to large cap multiples. Combine the index discount with the value factor discount and you arrive at a basket where the median PE is in the high single digits to low double digits.

That setup has two possible readings. The bullish read is that small caps are pricing in a recession that may not arrive, and a re-rating cycle is overdue once the Fed begins cutting. The bearish read is that small caps are correctly pricing in a deteriorating economy, weakening consumer demand, refinancing risk for highly levered small caps, and tariff exposure for the import-heavy retail names. Both reads are defensible. The screener does not pick a side. It just gives you the data to evaluate single names within either interpretation.

What is inside the premium template

The template includes two files (both free, no email required to download). The sample file ships with static values pre-filled so you can see exactly what the workbook looks like with data. The template file ships with live MarketXLS formulas so the numbers refresh every time you open it.

Sheet 1: Cover

The cover sheet is the branded entry point. Large title in MarketXLS navy, a gold subtitle with the May 2026 edition tag, version number, data-as-of date, and a full table of contents covering all eleven sheets. Gridlines are hidden and the entire sheet is filled with navy so it looks like a designed product cover rather than a spreadsheet tab.

Sheet 2: How To Use

Step-by-step tutorial covering seven workflow steps: confirm watchlist, set portfolio inputs, read the dashboard, run a re-rating scenario, apply the quality filter, size positions, and compare small versus large cap. Each step references specific cells and explains what the inputs do downstream. A MarketXLS formula reference table at the bottom lists every function used in the workbook.

Sheet 3: Dashboard (the headline sheet)

This is where you spend most of your time. The dashboard sheet opens with a six-tile KPI row across the top: median PE, median P/B, median EV/EBITDA, count of names below the PE threshold, combined market cap in billions, and the discount to the S&P 500 PE. Each tile uses the premium-tile design pattern - merged cells, navy thick borders, big number in 22pt navy bold, label in muted gray uppercase, and a delta line below in green or red.

Below the tiles, two embedded bar charts compare trailing PE and price-to-book across the basket. The charts use the MarketXLS blue color scheme, with the y-axis as ticker labels and the x-axis as the multiple. The visual lets you spot outliers instantly without scanning the table.

The screener table runs across columns A through Q with seventeen metric columns: ticker, company, price, market cap, PE, P/B, P/S, EV/EBITDA, EV/Revenue, PEG, dividend yield, ROE, operating margin, debt-to-equity, beta, one-year return, and percent below the 52-week high. Conditional formatting paints the table:

• Color scales (red-to-green) on PE, P/B, P/S, EV/EBITDA, dividend yield, ROE, operating margin, and debt-to-equity. Green is good (cheap multiples, high quality, low leverage), red is bad (expensive, low quality, high leverage).
• Data bars on market cap, so you can see the size distribution at a glance.
• Icon sets (three-arrow) on one-year return so up versus flat versus down jumps out visually.

Gridlines are hidden on the dashboard sheet. The print area is set so the whole dashboard prints cleanly on one landscape page.

Sheet 4: Inputs

The yellow control panel. Four input cells in gold-bordered yellow: portfolio size in dollars, target small cap value allocation as a percentage, maximum PE threshold for the quality filter, and minimum ROE threshold. Three scenario dropdowns with data validation: re-rating scenario (Bull, Base, Bear), allocation recipe (Equal Weighted, Value Weighted, Quality Weighted, Yield Weighted), and risk profile (Defensive, Balanced, Aggressive).

The eighteen-ticker watchlist sits in rows 10-27, with each ticker cell yellow-highlighted as an input cell. Replace any ticker and the entire workbook reprices.

Sheet 5: Scenario Analysis

The re-rating engine. For each name in the basket, the sheet calculates current TTM EPS, applies a bear-case 25 percent multiple compression, a base-case flat multiple, and a bull-case 30 percent multiple expansion. Bear and bull price targets show what the stock would be worth under each scenario, holding earnings constant. The bull target cells get a green data bar so you can rank the basket by upside potential.

Use this sheet to stress-test what a small cap re-rating cycle would do to your basket. If you assume a 30 percent multiple expansion on the median PE of around 10, that is a re-rating from 10x to 13x - the kind of move that typically accompanies an end-of-cycle small cap rotation.

Sheet 6: Quality Filter

The value-trap detector. The filter stacks four checks: PE below the threshold from the Inputs sheet, ROE above the threshold from the Inputs sheet, debt-to-equity below 200 percent, and operating margin above 3 percent. Names that pass all four get a green PASS badge. Names that fail get a red FAIL badge.

Conditional formatting colors each metric column independently so you can see exactly why a name failed. A stock with PE 8, ROE 4 percent, and debt-to-equity 400 percent will fail twice - the ROE column will glow red and the debt column will glow red, while the PE column will glow green. That triangulation is what separates a cheap-but-broken business from a cheap-but-healthy one.

Sheet 7: Allocation Sizer

Position-sizing engine. Takes your portfolio size from Inputs (default $250,000) and target small cap value allocation (default 20 percent) and distributes the resulting budget across the basket using three recipes:

• Equal-weighted - 1/N to each name, removing single-name concentration risk.
• Value-weighted - inverse PE tilt, so cheaper names receive higher weight.
• Quality-weighted - ROE tilt, so higher-quality names receive higher weight.

The sheet also calculates approximate share counts using live prices, so you can see how many shares to buy in each name. A pie chart visualizes the equal-weighted allocation.

Sheet 8: Small vs Large Comparison

The macro context sheet. A four-row table comparing the small cap value basket median, the Russell 2000 index, the S&P 500, and the small-cap discount for each of trailing PE, price-to-book, price-to-sales, and dividend yield. A second macro context table shows the 10-year Treasury yield, the current Russell 2000 vs S&P 500 PE discount, the historical average discount (15 percent), and how far the current discount sits from the historical average.

A green-yellow-red color scale on the discount column tells you at a glance which metric shows the deepest small cap discount. The dividend yield row is intentionally green even when the spread is wide because, for income-oriented investors, the small cap dividend premium is positive context.

Sheet 9: Sector Heatmap

Aggregates the basket by sector and shows median PE, median P/B, median EV/EBITDA, median dividend yield, and median ROE for each sector. Color scales paint the cheapest sector green and the most expensive red. Data bars on the dividend yield and ROE columns show the magnitude of each sector's yield and quality profile.

The embedded bar chart at the bottom plots median PE by sector, so you can spot which sectors carry the deepest discounts. In May 2026, consumer discretionary (retail in particular) and materials carry some of the deepest small cap value discounts. Industrials and consumer staples small caps trade closer to the broad small cap index.

Sheet 10: Methodology

One-page explainer covering universe definition, valuation metrics, the quality overlay, re-rating scenarios, allocation recipes, the small vs large cap context, data sources, and limitations. This is the sheet you point your spouse, financial advisor, or compliance officer at when they ask how the model works.

Sheet 11: Glossary and Disclaimer

Twenty-three term definitions covering everything from small cap and Russell 2000 to multiple compression and quality-weighted allocation. The educational-only disclaimer sits at the bottom in a clearly demarcated section. Use it as a reference sheet when you encounter unfamiliar metrics.

The small cap value framework

Before getting to the formulas, here is the framework the screener is built around.

Step 1: Define the value universe

Small cap value as a factor screens for stocks in the bottom quintile of one or more valuation multiples - book-to-price, earnings yield, EBITDA-to-EV, or some composite. The eighteen names in this template span consumer discretionary (retail, apparel, footwear, auto parts), consumer staples (household products), industrials (industrial tools), and materials (specialty chemicals). All are US-listed and most sit in the $500M to $10B market cap range, which puts them squarely in small cap or lower mid cap territory.

This is not the only valid universe. Some screens use book-to-price only. Some include real estate investment trusts. Some exclude financials. The point of this template is that the universe is editable. Drop the eighteen defaults and paste in your own list.

Step 2: Layer in valuation multiples

Trailing PE is the first-pass filter. Stocks under 12x trailing earnings get a green color scale. Stocks above 20x get a red color scale. Stocks in between get amber.

Price-to-book is the small cap value classic. The Fama-French value factor used book-to-price as its anchor. P/B below 1 indicates the market values the company below its accounting book value, which historically marked deep value setups. The catch is that book value is less meaningful for asset-light businesses (software, brands, services) where the bulk of value sits in intangibles. For an asset-heavy small cap like a department store, manufacturer, or chemical company, book value still works.

Price-to-sales is the noise cancellation filter. When earnings are negative or distorted by one-time charges, P/S still gives you a value read. Under 1.0 looks cheap. Above 2.0 looks rich.

EV/EBITDA is the capital-structure-neutral multiple. It works better than PE when comparing companies with different debt loads. EV/EBITDA under 6x is the small cap value sweet spot.

PEG is the growth-adjusted multiple. PE 8 sounds cheap. PE 8 with negative earnings growth is a value trap. PEG below 1.0 is the textbook reading that the multiple is cheap relative to growth.

Step 3: Apply the quality overlay

Cheap is necessary but not sufficient. The quality overlay distinguishes value names from value traps:

• ROE above 10 percent - the business actually earns a return on shareholder capital.
• Operating margin above 3 percent - the underlying business is still profitable, not collapsing.
• Debt-to-equity below 200 percent - leverage is not catastrophic.
• Net profit margin positive - earnings are real, not accounting artifacts.

A name that screens cheap on every multiple but fails three of four quality checks is the textbook value trap. KSS in the screener is an example: trailing PE 8.5, P/B 0.45, P/S 0.18 - cheap on every value metric. But ROE 4.5 percent, debt-to-equity 180 percent, and operating margin 2.5 percent put it in the value-trap quadrant. The quality filter sheet flags it FAIL in red.

By contrast, a name like Carter's (CRI) screens cheap (PE 10.5, P/B 2.40, EV/EBITDA 6.5) and clears quality (ROE 22.5 percent, operating margin 8.5 percent, debt-to-equity 125 percent). The quality filter flags it PASS in green. That is the kind of differentiation the dashboard is built to surface.

Step 4: Stress-test with scenarios

The scenario analysis sheet asks one question: what would each stock be worth if its PE multiple re-rated by 25 percent in either direction? For a stock at PE 8 with $2 in TTM EPS, the math is simple:

• Bear case: PE compresses to 6, target price = 6 x $2 = $12
• Base case: PE holds at 8, target price = $16
• Bull case: PE expands to 10.4, target price = $20.80

A 30 percent re-rating in either direction is the typical move during small cap regime shifts. Run the scenarios and ask: am I being paid enough for the downside risk if I am wrong on the re-rating direction?

Step 5: Size positions appropriately

The allocation sizer offers three recipes. Equal-weighted is the default because it removes single-name concentration risk. Value-weighted tilts toward cheaper names. Quality-weighted tilts toward higher-quality names. Each recipe is a defensible starter, not a recommendation.

The sheet also calculates share counts using live prices via QM_Last, so you can see how many shares to buy in each name to reach the target dollar allocation.

MarketXLS implementation - the formulas that power the dashboard

Every metric in the screener is a live MarketXLS function. Here are the building blocks:

=QM_Last("KSS")                  - Live share price
=PERatio("KSS")                  - Trailing PE
=PriceToBook("KSS")              - Price-to-book
=PriceToSales("KSS")             - Price-to-sales
=EnterpriseValueToEbitda("KSS")  - EV/EBITDA
=EnterpriseValueToRevenue("KSS") - EV/Revenue
=PEGRatio("KSS")                 - PEG
=DividendYield("KSS")            - Dividend yield
=ReturnOnEquity("KSS")           - ROE
=OperatingMargin("KSS")          - Operating margin
=NetProfitMargin("KSS")          - Net profit margin
=TotalDebtToEquity("KSS")        - Debt-to-equity
=CURRENT_RATIO("KSS")            - Current ratio
=Beta("KSS")                     - Beta versus market
=MarketCapitalization("KSS")     - Market cap in dollars
=StockReturnOneYear("KSS","Price") - One-year price return
=PercentBelowFiftyTwoWeekHigh("KSS") - Percent below 52-week high
=Sector("KSS")                   - Sector classification
=Industry("KSS")                 - Industry classification
=EarningsPerShare("KSS")         - TTM EPS
=Name("KSS")                     - Company name

The screener tabulates seventeen columns of these formulas across the basket. The quality filter sheet feeds these into a four-check AND() formula that returns PASS or FAIL. The scenario analysis sheet uses PE x EPS to construct price targets. The allocation sizer uses MarketCapitalization, PE, and ROE as the basis for the three weighting recipes.

Reading the dashboard - a worked example

Open the sample file and look at the screener table. Three colors immediately jump out.

Green cluster - GES (Guess?) shows PE 6.8 (green), P/B 1.25 (green), EV/EBITDA 4.5 (green), dividend yield 8.5 percent (green), ROE 21.5 percent (green). The quality filter flags it PASS. The one warning is the one-year return at minus 28 percent, which paints the cell with a red down-arrow icon. Reading: cheap on every value metric, high quality, paying you to wait, but the market is selling.

Mixed cluster - KSS (Kohl's) shows PE 8.5 (green), P/B 0.45 (green), but ROE 4.5 percent (red), operating margin 2.5 percent (red), and debt-to-equity 180 percent (yellow). Quality filter flags FAIL. Reading: cheap on multiples but the underlying business is deteriorating. Classic value trap candidate.

Quality cluster - CRI (Carter's) shows PE 10.5 (green), P/B 2.40 (amber), EV/EBITDA 6.5 (green), ROE 22.5 percent (green), operating margin 8.5 percent (green), dividend yield 5.8 percent (green). Quality filter flags PASS. Reading: not the cheapest name in the basket, but priced reasonably relative to quality. The dividend yield gives you carry while you wait.

The dashboard is not telling you what to buy. It is showing you which names belong in which category so you can do further research on the ones that pass your own criteria.

Russell 2000 vs S&P 500 - reading the spread

The Small vs Large Comparison sheet quantifies what most investors feel intuitively. As of May 2026:

• Russell 2000 trailing PE: ~16.5x vs S&P 500 ~22.5x. 27 percent discount.
• Russell 2000 P/B: ~1.85x vs S&P 500 ~4.55x. 59 percent discount.
• Russell 2000 P/S: ~1.05x vs S&P 500 ~2.90x. 64 percent discount.
• Russell 2000 dividend yield: ~1.85 percent vs S&P 500 ~1.30 percent. 55 basis point premium.

The historical average Russell 2000 vs S&P 500 PE discount is around 15 percent. The current discount sits roughly 12 percentage points wider than that average. That is the macro setup the screener is targeting.

Two caveats. First, the historical small cap discount is partly an apples-to-oranges comparison: the S&P 500 increasingly consists of asset-light technology companies that earn high returns on capital, while the Russell 2000 has more capital-intensive cyclicals. Some of the discount is structural rather than cyclical. Second, the discount has been wide for a while now without a sustained re-rating. The trigger usually requires either a Fed easing cycle, a clear recession-then-recovery move, or a meaningful drop in long-term yields.

Sector breakdown - where the cheap multiples cluster

The Sector Heatmap sheet aggregates the basket by sector. In May 2026, the cheap multiples cluster heavily in consumer discretionary (retail, apparel, footwear, auto parts). That makes sense: the consumer discretionary group has been pressured by elevated rates, weakening discretionary spending, tariff exposure on imported apparel and home goods, and the structural shift to online retail.

Materials and industrials sit in the middle of the value range. The consumer staples small caps in this basket (NWL, household products) trade near the basket median.

Sector concentration matters for two reasons. First, a basket where 70 percent of names sit in one sector is really a sector bet dressed up as a value bet. Second, small cap sectors can re-rate as a group when the macro narrative changes. If consumer spending picks up, the retail-heavy small cap value bucket would re-rate first.

How to customize the template

The template is built to be edited. Here are the most common customizations:

Swap in a different universe. Replace the eighteen tickers on the Inputs sheet. Every other sheet refreshes via formulas. Try a Russell 2000 Value ETF holdings list, a Morningstar Small-Cap Value top 20, or your own custom watchlist.

Tighten the quality filter. Raise the PE threshold to 12 instead of 15 if you want a stricter cheap-PE cut. Raise the ROE threshold to 15 percent instead of 10 percent if you want a higher quality bar. Edit the AND() formula on the Quality Filter sheet directly to add criteria.

Change the scenario assumptions. The default re-rating is 25 percent down (bear) and 30 percent up (bull). Edit cells E5 and the bull formula on the Scenario Analysis sheet to test different scenarios. Try a 40 percent up case to model a deep value re-rating cycle.

Adjust the allocation recipes. The Allocation Sizer ships with equal-weighted, value-weighted, and quality-weighted recipes. Add a dividend-yield-weighted column or a momentum-weighted column by referencing the relevant MarketXLS function and applying the same weighting math.

Add a backtest sheet. The current template is a snapshot model. To add a historical backtest, create a new sheet that pulls QM_GetHistory for each ticker, computes monthly returns, and runs a basket-level equity curve. This is an extension worth building if you trade the strategy systematically.

Download the templates

Both files are free. The sample version comes pre-filled with static values and formula comments on every data cell, so you can see exactly which MarketXLS function powers each number. The template version comes wired with live MarketXLS formulas - open it in Excel with the MarketXLS add-in active and every cell refreshes to current prices and ratios.

Download the templates:

• - Pre-filled with values, formula shown in every cell comment
• - Live-updating formulas, designed for the MarketXLS add-in

If you do not yet have MarketXLS in Excel, head to marketxls.com to see how the add-in works. Book a demo if you want a walkthrough of how teams use MarketXLS for value screening, portfolio construction, and live data in Excel.

FAQ

What is a small cap value screener?

A small cap value screener is a tool that filters a universe of smaller-capitalization stocks (typically market cap between $300 million and $10 billion) for the ones trading at low valuation multiples - low PE, low P/B, low EV/EBITDA, or some combination. The premium template in this post screens an eighteen-name Russell 2000-style universe across seventeen metrics, then layers a quality overlay (ROE, margin, leverage) on top to filter out value traps.

How do I separate small cap value from small cap value traps?

The quality overlay is the standard answer. A small cap value name has a low PE, low P/B, and low EV/EBITDA, but it also has positive operating margin, ROE above 10 percent, and debt-to-equity below 200 percent. A value trap has the low multiples without the quality. The Quality Filter sheet in the template stacks the four checks and flags every name PASS or FAIL.

Why is the Russell 2000 trading at such a wide discount to the S&P 500?

Three reasons. First, the S&P 500 is dominated by asset-light mega-cap technology companies that earn high returns on capital, which deserve higher multiples. Second, small caps are more sensitive to interest rates because they carry more floating-rate debt and weaker balance sheets. The 10-year Treasury at 4.35 percent has compressed small cap multiples. Third, sentiment - the AI narrative has concentrated flows into the largest names at the expense of smaller, slower-growing businesses. The current ~27 percent PE discount is roughly 12 percentage points wider than the historical average of around 15 percent.

What is the difference between PE, P/B, and EV/EBITDA?

PE divides share price by earnings per share. P/B divides market cap by book value of equity. EV/EBITDA divides enterprise value (market cap plus debt minus cash) by earnings before interest, taxes, depreciation, and amortization. PE is the most common but breaks down when earnings are negative or noisy. P/B is the small cap value classic but is less useful for asset-light businesses. EV/EBITDA is capital-structure-neutral, which makes it the cleanest cross-sector comparison metric.

What is a re-rating cycle?

A re-rating cycle is a sustained move in the PE multiple investors are willing to pay for a stock or sector. Small cap value has gone through several re-rating cycles - the post-2000 cycle saw small cap value PEs roughly double over five years. The trigger is usually a Fed easing cycle, a clear macro improvement, or a clear underperformance gap that prompts mean-reversion flows. The scenario analysis sheet stress-tests what a 30 percent re-rating would do to each name.

Should I just buy a Russell 2000 Value ETF instead?

That is a reasonable alternative if you do not want to pick individual names. A Russell 2000 Value ETF gives you broad exposure to the factor with one trade. The trade-off is that the ETF holds the entire universe including the value traps. A screened basket can target just the names that pass both the value and quality filters. The dashboard helps you build that screened basket from a defensible starting point.

How often should I refresh the dashboard?

Daily is overkill. The valuation multiples do not move enough day to day to require daily monitoring. Monthly is the natural cadence - re-run the screen at the start of each month, see which names have moved between categories, and rebalance if your conviction has changed. The MarketXLS formula version refreshes automatically every time you open the file.

Can I use this template for international small caps?

Yes, with caveats. The MarketXLS functions support international tickers. Drop in a non-US small cap ticker (with the appropriate exchange suffix) and the formulas will pull data. The macro context table comparing Russell 2000 to S&P 500 will still be US-based, but the screener and quality filter logic is currency-agnostic and works for any market.

The bottom line

Small cap value is one of the cleanest setups in markets right now. The Russell 2000 trades at one of the widest historical discounts to the S&P 500 on record. Small cap value as a factor screens cheaper still. But cheap-on-paper does not mean buy-on-paper. The difference between value and value trap is the quality overlay - ROE, margin, leverage, and earnings durability - that separates cheap-and-functional businesses from cheap-and-broken ones.

The premium dashboard in this post does that triage in one screen. Six KPI tiles up top, a conditional-formatted screener showing all seventeen metrics, a scenario analysis engine for re-rating stress tests, a sector heatmap to see where the cheap multiples cluster, and a quality filter sheet that flags every name PASS or FAIL on the four core quality checks. The MarketXLS formula version means the entire workbook refreshes every time you open it.

Download both files above. Drop in your own tickers. Tighten or loosen the filters. Use the dashboard as the first-pass screen, then do deeper fundamental work on the names that pass.

For more on MarketXLS for stock screening and Excel-native financial data, visit marketxls.com or book a demo to see how the add-in is used for value screening, portfolio construction, and live data analysis.

This dashboard is an educational template, not investment advice or a recommendation to buy or sell any security. Past performance is not indicative of future results. Small cap value names can stay cheap for long stretches, and value traps remain a real risk even after layering on a quality overlay. Always do your own research or consult a licensed financial advisor before making investment decisions.