ToolsValue-ScreenBuffett

Building a Buffet-Style Watchlist: Stocks That Pass Buffett's Quality-and-Price Filters

UUnknown

2026-02-16

9 min read

Download a Buffett-style watchlist and scoring rubric to screen stocks on ROIC, earnings predictability, leverage and valuation — with AAPL, AMZN, GOOG examples.

Build a Buffet-Style Watchlist: A Practical, Downloadable Screen for Quality and Price

Hook: If you’re overwhelmed by data, missing timely trade signals, or unsure which stocks combine durable business quality with reasonable price — you’re not alone. Markets in late 2025 and early 2026 have rewarded companies with predictable cash flow and high returns on capital while punishing high-leverage, low-profitability names. This article gives you a ready-to-use, Buffett-inspired watchlist and a transparent scoring rubric you can download and plug into your stock screener today.

What you’ll get

A downloadable CSV watchlist pre-filled with example tickers (AAPL, AMZN, GOOG) and illustrative metrics
A downloadable CSV scoring rubric that quantifies Buffett’s quality-and-price filters
Step-by-step rules and screen settings to reproduce the watchlist in common screeners
Case studies showing how AAPL, AMZN and GOOG would score under the rubric (illustrative, Jan 2026)

Why a Buffett-style screen still matters in 2026

Late 2025 introduced a market regime where growth alone no longer guaranteed outperformance. After multiple rate hikes in the earlier half of the decade and a partial easing in late 2025, investors have rotated into companies that show durable returns and predictable earnings. In 2026, two trends make a Buffett-style filter even more relevant:

Capital-intensity scrutiny: AI and cloud investments grew capex budgets, but investors now reward firms that convert that spending into sustainable ROIC.
Earnings predictability premium: With macro uncertainty still elevated, stocks with lower earnings volatility and visible margins command higher valuations.

"It's far better to buy a wonderful company at a fair price than a fair company at a wonderful price." — Warren Buffett

Core filters: What Buffett would screen for (and why)

We translate Buffett’s qualitative rules into quantitative, screenable metrics. Each metric maps to a business quality: economic moat, capital efficiency, stability, leverage, and valuation.

1) Return on Invested Capital (ROIC) — moat & capital efficiency

Why: High ROIC indicates a firm reinvesting capital at attractive rates. Buffett prizes businesses that earn much more than their cost of capital.

Metric: ROIC (Trailing 5-year median)
Threshold: Prefer >= 15%; watchlist threshold >= 12% for broader coverage
Notes: Use 5-year median to reduce cycle noise. For capital-light businesses (digital platforms), consider adjusted ROIC excluding excessive cash.

2) Earnings predictability — lower volatility of core earnings

Why: Predictable earnings make forecasting intrinsic value easier and reduce downside risk during market shocks.

Metric: Coefficient of Variation (CV) of yearly EPS over 5 years (standard deviation / mean)
Threshold: CV <= 0.25 is excellent; keep CV <= 0.40 as pass for cyclical sectors
Action: If EPS is occasionally negative, substitute with adjusted operating cash flow variability.

3) Low leverage — sustainable through downturns

Why: High debt increases bankruptcy risk and reduces optionality. Buffett favors businesses that can weather recessions without dilutive financing.

Metrics: Debt / EBITDA and Interest Coverage Ratio (EBIT / Interest)
Thresholds: Debt/EBITDA <= 3 is preferred; Interest Coverage >= 5

4) Reasonable price — a margin of safety

Why: Price decides return. A great business bought at an unreasonable price can still deliver disappointing returns.

Metrics: EV / EBIT (preferred) and P/FCF
Thresholds: EV/EBIT <= 20 and/or P/FCF below 10-year sector median. Also compare price to discounted normalized free cash flow.
Approach: Use normalized (cycle-adjusted) earnings or a conservative DCF for the “fair value” baseline.

The scoring rubric — translate ideas into a 0–100 score

Below is the rubric we provide as a downloadable CSV. We weight metrics to reflect Buffett’s preference for quality first, price second.

Weights and scoring (default)

ROIC (35%) — 0–35 points: linear from 0 at 0% ROIC to 35 at >= 25% ROIC
Earnings predictability (20%) — 0–20 points: CV of EPS mapped to score (CV 0 => 20; CV 0.4 => 0)
Leverage (15%) — 0–15 points: best score for Debt/EBITDA <= 1.5 and Interest Coverage >= 8; scaled down for worse ratios
Valuation (20%) — 0–20 points: EV/EBIT vs. sector norm; full points if EV/EBIT <= 12, 0 if > 30
Momentum / Liquidity adjustment (10%) — 0–10 points: optional; positive for stable positive FCF momentum and high average daily volume

Score interpretation: 80+ = Watchlist “high priority”; 60–79 = Watchlist “monitor”; <60 = pass but not priority.

Download: Watchlist CSV and Scoring Rubric

Use these two files to import into Excel, Google Sheets, TradingView or your custom screener.

Case studies: How AAPL, AMZN and GOOG fare

Below are concise, transparent illustrations of the rubric applied to three widely followed names. These are illustrative snapshots for Jan 2026, not trade recommendations. Use them to see the rubric in action.

AAPL — a classic Buffett candidate (illustrative)

ROIC: very high (ex. ~30% range) → strong score
EPS CV: low → stable earnings score
Leverage: modest debt load relative to earnings → strong score
Valuation: premium EV/EBIT and P/FCF due to large cash flows → valuation reduces overall score slightly
Aggregate: high overall score (example: 88) — qualifies as a high-priority watchlist name

AMZN — improving quality, valuation depends on profit conversion (illustrative)

ROIC: improving but still lower than the best moat businesses (example ~12–15%)
EPS CV: moderate → some score penalty for variability historically
Leverage: reasonable
Valuation: high EV/EBIT and P/FCF if you use GAAP operating profit; better if normalized FCF from cloud & subscription revenue is used
Aggregate: middling score (example: 62) → monitor list, becomes priority if valuation compresses or ROIC demonstrates sustained improvement

GOOG (Alphabet) — platform economics and strong margins (illustrative)

ROIC: solid (platform economics) → good score
EPS CV: low → predictability score high
Leverage: minimal debt → strong score
Valuation: premium but often justified by cash flow growth in AI/cloud era; results in a good-but-not-stretching total score (example: 78)

How to build the screen in your stock screener (step-by-step)

These filter examples work in most modern screeners (TradingView, Screener.co, Finbox, Yahoo, Bloomberg). Translate field names as necessary.

Set universe: US-listed, market cap > $10B (reduces microcap noise)
ROIC: 5-year median ROIC >= 12% (strict: >=15%)
EPS stability: CV(EPS, 5y) <= 0.40 (strict: <=0.25)
Debt: Debt/EBITDA <= 3 AND Interest Coverage >= 4 (strict: <=1.5 and >=8)
Valuation: EV/EBIT <= 20 OR P/FCF <= sector 10-year median; optionally require current price <= 1.1*conservative intrinsic value
Optional liquidity filter: ADTV > $5M

Practical tips for implementation and workflow

Import the CSV into Google Sheets, then use the rubric formulas to compute a live score by pulling current financials via your data provider or APIs (Alpha Vantage, Polygon, IEX, or your premium data feed).
Set alerts for when score crosses 80 or when price drops >= 15% relative to your fair-value estimate.
Rebalance frequency: review watchlist weekly, re-score monthly. For larger portfolios, rebalance exposures quarterly after earnings and macro updates.
Backtest: run the screen over a 10-year period using historical fundamentals to measure compound returns vs. S&P 500. Pay attention to drawdowns and sector concentration; consider alternative yield environments like those discussed in private credit vs public bonds strategies when calibrating discount rates.

Advanced adjustments for 2026 and beyond

As markets evolve, so should the screen:

AI-investment adjustment: For companies with material AI-driven capex, prefer forward-looking ROIC (projected 3-year) because recent capex may temporarily depress trailing ROIC. See practical infra discussions in Edge AI reliability write-ups for handling noisy capital cycles.
Regulatory overlay: Add a political/regulatory risk flag for firms facing active antitrust or platform regulation cases; treat such flags as a negative in final weighting — for related compliance context see recent compliance coverage.
Macroeconomic sensitivity: When yields move, EV/EBIT thresholds should be tightened if long-term real rates rise; loosen when rates compress.

Common pitfalls and how to avoid them

Relying on a single metric: ROIC is powerful, but combine with predictability and leverage to reduce false positives.
Ignoring accounting distortions: Watch for large non-recurring items, stock-based compensation swings, and tax rate anomalies — adjust EPS and EBIT as needed.
Chasing low yield from past performance: A high historical ROIC can revert; check recent investments and management commentary on capital allocation.

How to verify — a quick checklist before adding to your portfolio

Confirm ROIC drivers: Are they sustainable (moat) or cyclical?
Validate earnings predictability manually across business segments
Stress-test leverage at higher interest rates and lower revenue scenarios
Recompute fair value under conservative growth assumptions; demand a margin of safety

Wrap-up & action steps

Buffett’s approach was never a mechanical checklist — it was an emphasis on buying high-quality businesses at sensible prices. This downloadable watchlist and scoring rubric convert that philosophy into a reproducible process you can use with modern screeners and data feeds in 2026.

Actionable next steps:

Download the watchlist and rubric CSVs above and import them into your preferred tool.
Run the screen over your universe and tag names that score >= 80 as high-priority candidates.
Perform the quick verification checklist for each high-priority candidate before allocating capital.

Final note: Use the rubric as a disciplined starting point — not a black box. Recalibrate weights based on your time horizon, risk tolerance, and the sectors you trade. In 2026, combining classic Buffett filters with modern data (AI-driven segment trends, cloud economics, and stress-tested leverage) will produce a watchlist that is both robust and actionable.

Call to action

Download the CSVs, import them into your screener, and run the filter today. Subscribe to our newsletter for monthly updates to the rubric and an evolving example universe as 2026 unfolds — we’ll publish audited, re-scored watchlists after each quarter. Click the CSV links above to get started and join a community of traders turning Buffett’s principles into modern, data-driven workflows.

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