Methodology — How We Calculate Fair Value, Risk Scores, and Moat Ratings
Every number on FairValueLabs is calculated from public data using documented formulas. This page explains exactly how — so you can verify our work or adjust the assumptions to match your own investment thesis.
Methodology owner: Marcus Wei (engineering), with editorial review by David Chen (CFA). Last comprehensive review: 2026-05-04.
What Lineage Are These Models From?
FairValueLabs doesn't invent valuation theory. The models on this site are public-domain academic and practitioner work, implemented carefully and applied consistently across coverage. The point of listing the lineage is so you can read the original sources directly when our explanation isn't enough.
| Concept | Source | Why we use it |
|---|---|---|
| Altman Z-Score (5-factor model) | Altman, Edward I. (1968). "Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy." The Journal of Finance, Vol. 23, No. 4, pp. 589–609. | The original 1968 calibration. Survived 50+ years of follow-up validation work; we use the manufacturing variant and Altman's later modifications for service and emerging-market firms. |
| Margin of safety; Mr. Market; intrinsic value vs. price | Graham, Benjamin (1949, revised 1973). The Intelligent Investor, Chapters 8 and 20. Also Graham & Dodd (1934), Security Analysis. | The conceptual frame for everything on this site. The DCF and three-factor model are how we operationalize "intrinsic value"; the margin-of-safety zones (heavy buy, buy, watch, fair-over, overvalued) are the Graham construct in practice. |
| Owner earnings; circle of competence | Buffett, Warren E. Berkshire Hathaway shareholder letters, particularly 1992 ("intrinsic value") and 1986 ("owner earnings"). | Owner earnings — net income + D&A − maintenance capex − working-capital change — sits underneath our predicted-EPS calculation for capital-light businesses where reported earnings drift from cash earnings. |
| Economic moat (sources, durability, ROIC anchoring) | Dorsey, Pat (2008). The Little Book That Builds Wealth. Plus Morningstar's published moat-trend methodology for cross-checking. | Dorsey's source taxonomy (intangibles, switching costs, network effects, cost advantages, efficient scale) frames our qualitative read; the quantitative scorecard uses ROIC stability as the dominant input, following Damodaran's note that "moats show up in ROIC before they show up anywhere else." |
| DCF mechanics; cost of capital; relative valuation | Damodaran, Aswath. Investment Valuation (3rd ed., 2012); Damodaran's NYU Stern data library for industry betas, ERP, and country risk premia. | The DCF discount rate uses Damodaran's published equity risk premium and his industry-adjusted betas as cross-checks. The fair-PE cap (40×) and the 15% PE discount come from his work on the relationship between growth, ROE, and justified multiples. |
| Capital allocation; reinvestment vs. payout decisions | Thorndike, William (2012). The Outsiders. Plus Buffett 1985, 1987, 2007 letters on capital allocation. | Underpins how we read management decisions in the dividend-safety section and in the "what should the company do with cash" framing on speculation/no-dividend pages. |
How Do We Differ from Morningstar, Simply Wall St, and Other Tools?
The closest comparable products to FairValueLabs are Morningstar (paid, institutional-grade), Simply Wall St (paid retail), and gurufocus.com (mixed). We borrow from all three and depart from each in specific ways.
| Dimension | FairValueLabs | Morningstar | Simply Wall St |
|---|---|---|---|
| Pricing | Free, no signup | $249/yr Premium | $10/mo+ |
| Data source | Direct from SEC EDGAR XBRL | Proprietary + S&P Global | Aggregated third-party |
| Fair value method | Predicted-EPS × fair-PE; ROE-anchored P/B for financials; transparent inputs | Multi-stage DCF with proprietary growth assumptions | 2-stage DCF with industry-default assumptions |
| Moat method | ROIC stability + gross margin trajectory + revenue stability (quantitative weighting) | Analyst-driven moat trend (qualitative) | Threshold-based on ROCE/ROE |
| Sector exemptions | Explicit: Z-Score and moat scorecards skipped for banks, insurers, utilities, REITs with sector-specific notice | Sector-adjusted models | Same model applied across sectors (known weakness) |
| Bias disclosure | Public miss log, named analyst-of-record per page | Analyst byline on premium reports | No analyst attribution |
| Update cadence | Within 24h of new SEC filing | Quarterly + earnings driven | Daily refresh on aggregated data |
The honest summary: Morningstar's analyst depth on individual names is still better than ours for the largest companies. Where we win is breadth at zero cost, transparency of methodology, and the discipline of refusing to print numbers we can't defend (the "fair value: insufficient data" case, which most paid services cover up with confident-looking outputs).
How Do We Calculate the Altman Z-Score?
The Altman Z-Score was developed by Edward Altman at NYU in 1968. It combines five financial ratios into a single score that predicts bankruptcy probability within 2 years. The formula for manufacturing firms:
Z = 1.2(A) + 1.4(B) + 3.3(C) + 0.6(D) + 1.0(E)
| Variable | Formula | What It Measures |
|---|---|---|
| A | Working Capital / Total Assets | Short-term liquidity |
| B | Retained Earnings / Total Assets | Cumulative profitability |
| C | EBIT / Total Assets | Operating efficiency |
| D | Market Cap / Total Liabilities | Solvency buffer |
| E | Revenue / Total Assets | Asset turnover |
How Do We Interpret the Score?
- Z < 1.8 — Distress Zone (red). High probability of financial distress within 2 years.
- Z 1.8 - 3.0 — Gray Zone (yellow). Elevated uncertainty — warrants closer scrutiny.
- Z > 3.0 — Safe Zone (green). Financially healthy by this metric.
We use the original manufacturing formula for industrial companies and Altman's modified Z''-Score for service and financial firms.
Data source: All five inputs are extracted from the company's most recent 10-K annual filing on SEC EDGAR.
How Do We Calculate Intrinsic Value?
We use a three-factor valuation model that blends three approaches to reduce model-specific bias:
| Factor | Weight | How It Works |
|---|---|---|
| Historical PE × Forward EPS | 50% | Median PE ratio over 4+ years multiplied by consensus forward EPS estimate. Anchors valuation to how the market has historically priced this company's earnings. |
| Discounted Cash Flow (DCF) | 30% | Two-stage DCF: 10-year projection using blended growth rate, terminal value at GDP growth rate (~2.5%), discounted at CAPM-derived WACC. |
| EV/FCF Multiple | 20% | Enterprise Value / Free Cash Flow compared against historical and sector norms. Catches companies where debt or cash significantly distorts equity value. |
Growth Rate
70% analyst consensus forward estimates + 30% historical compound annual growth rate (CAGR). This blends forward-looking expectations with backward-looking track record. Growth is capped at 20% to prevent unrealistic projections.
Discount Rate (WACC)
Capital Asset Pricing Model (CAPM): Risk-free rate (10-year Treasury from FRED) + beta × equity risk premium. Higher-beta stocks get a higher discount rate, producing lower intrinsic values.
Net Cash Adjustment
After calculating the weighted average intrinsic value, we add net cash (cash & equivalents minus total debt) from the balance sheet. Companies sitting on large cash piles show higher intrinsic values than pure earnings-based models would suggest.
Three-Level Classification
- Value Investment — Profitable, stable cash flows. All three factors apply at full weight.
- Value-Speculation — Profitable but volatile. PE-based factor weight reduced.
- Pure Speculation — Unprofitable. Traditional valuation unreliable; page warns prominently.
Key Assumptions & Limitations
- Growth rate capped at 20% (conservative bias for high-growth companies)
- Companies with negative FCF get reduced or zero DCF component
- The model tends to undervalue fast-growing tech companies (known bias — will recalibrate as coverage expands)
- Margin of Safety = (Intrinsic Value - Market Price) / Intrinsic Value. Positive = potentially undervalued.
- Every ticker page shows a sensitivity table so you can adjust growth and discount rate assumptions
Data source: Historical financials from 10-K/10-Q filings on SEC EDGAR. Forward estimates from analyst consensus. Current price from Yahoo Finance.
How Do We Rate Competitive Moats?
Our moat rating combines quantitative signals with structured qualitative assessment:
| Factor | Weight | How We Measure |
|---|---|---|
| ROIC Stability | 40% | Standard deviation of Return on Invested Capital over 10 years. Lower variance = wider moat. |
| Gross Margin Trend | 30% | 10-year gross margin trajectory. Expanding margins suggest pricing power. |
| Switching Cost Assessment | 30% | Qualitative: customer lock-in, ecosystem effects, regulatory barriers. |
Star Rating Scale
- 5 stars — Wide moat. Dominant competitive position with high barriers to entry.
- 4 stars — Solid moat. Strong advantages but with some competitive pressure.
- 3 stars — Narrow moat. Some competitive advantages but vulnerable to disruption.
- 2 stars — Weak moat. Commoditized business with limited pricing power.
- 1 star — No moat. Highly competitive, no sustainable advantage evident.
How Do We Grade Dividend Safety?
Our dividend safety grade (A through F) is based on three factors:
| Factor | Safe Signal | Danger Signal |
|---|---|---|
| Payout Ratio | < 60% of earnings | > 100% (paying more than earned) |
| FCF Coverage | FCF > 1.5x dividend | Negative FCF for 2+ quarters |
| Growth Streak | 5+ years consecutive increases | Recent cut or freeze |
Grade Definitions
- A — Very Safe. Low payout ratio, strong FCF coverage, long growth streak.
- B — Safe. Healthy payout with adequate cash flow support.
- C — Borderline. Elevated payout ratio or inconsistent FCF.
- D — Unsafe. Payout exceeds earnings or FCF is negative.
- F — Cut Likely. Multiple danger signals — dividend cut appears imminent.
Data source: Dividend per share, earnings, and free cash flow from SEC EDGAR and Yahoo Finance.
Where Does the Data Come From?
Every metric on FairValueLabs flows through an automated data pipeline. Here is how it works end-to-end:
| Stage | Source / Tool | What Happens |
|---|---|---|
| 1. Ingest | SEC EDGAR API | Python ETL queries the EDGAR full-text search and XBRL APIs for the latest 10-K and 10-Q filings. New filings are detected and processed within 24 hours of publication. |
| 2. Enrich | Yahoo Finance, FRED | Real-time stock prices, analyst consensus estimates, beta, and the 10-year Treasury yield are merged with filing data to produce a complete financial profile. |
| 3. Compute | Python (NumPy) | All models run: Altman Z-Score, three-factor fair value, moat rating, dividend safety grade. Automated checks flag anomalies — stock splits, missing line items, extreme values, and sector exemptions (financials, utilities, REITs). |
| 4. Classify | Rule engine | Each ticker is categorized as Value Investment, Value-Speculation, or Pure Speculation based on profitability, cash-flow stability, and earnings history. Classification determines which valuation methods apply. |
| 5. Publish | 11ty SSG → Cloudflare Pages | JSON output is consumed by an Eleventy static site generator. Every page is pre-rendered HTML — no client-side data fetching, no API keys exposed, sub-second load times globally via Cloudflare's CDN. |
How Do We Ensure Data Quality?
- Stock split detection — price and EPS history are adjusted automatically when a split is detected, preventing false valuation swings.
- Sector exemptions — banks, insurance companies, utilities, and REITs are automatically flagged. Models that produce misleading results for these sectors (e.g., Altman Z-Score) display an exemption notice instead of a spurious number.
- Extreme value filtering — PE ratios above 200, negative book values, and other outliers trigger warnings rather than being silently passed through to valuation models.
- Quarterly refresh cycle — the pipeline runs daily for price updates and within 24 hours of any new SEC filing for fundamental data.
If a data point looks wrong, it probably is — and we would rather show "insufficient data" than a misleading number. Transparency over false precision.
What Are the Limitations?
- All models use historical data — they cannot predict future management decisions, black swan events, or macroeconomic shifts.
- DCF is highly sensitive to growth rate assumptions. Always check the sensitivity table.
- The Altman Z-Score was designed for manufacturing firms. We use modified versions for other sectors, but accuracy varies.
- Moat assessment includes subjective elements. Our rating is a starting point, not a final verdict.
- Quarterly data may lag by 1-2 months after the filing deadline.
This is not financial advice. All data is sourced from SEC EDGAR public filings. Always consult a qualified financial advisor before making investment decisions.
Further Reading
If our explanation isn't enough, the original sources do better:
- Altman, E. I. (1968). "Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy." Journal of Finance 23(4): 589–609. — The Z-Score paper.
- Graham, B. (1973). The Intelligent Investor, revised edition. — Chapters 8 (Mr. Market) and 20 (margin of safety) are the load-bearing chapters for everything on this site.
- Buffett, W. E. (1992). Berkshire Hathaway shareholder letter, "Intrinsic Value" section.
- Dorsey, P. (2008). The Little Book That Builds Wealth. — The most accessible book on moat sources.
- Damodaran, A. (2012). Investment Valuation, 3rd ed. — Encyclopedic; the chapters on relative valuation and growth are particularly useful for our predicted-EPS × fair-PE method.
- Thorndike, W. (2012). The Outsiders. — Eight CEO case studies on capital allocation; useful background for our dividend-safety reasoning.