📐 Platform Methodology

The research that runs
billion-dollar funds. Free.

Quantitative hedge funds and institutional asset managers pay millions to build models on the same 14 academic papers that power The Compound Family. We apply them directly, openly, and transparently — so every retail investor has access to the same analytical rigor. No subscription required to see the scores.

⚠ Educational purpose only. All scores, analyses and AI interpretations are provided for informational and educational purposes only. Nothing on this page or on thecompoundfamily.com constitutes investment advice or a recommendation to buy or sell any security. All investment decisions are solely your responsibility. Full disclaimer →
6
Scoring factors
40+
Financial metrics
14
Academic papers
10Y+
Historical data
4
Free tools
Stock Analysis Terminal

Six factors. One overall score.

Every stock receives a score from 0 to 100 based on six independent factors. Each factor is weighted differently depending on the company's sector — because what matters for a Utility is not the same as what matters for a Software company. Seven sector profiles in total.

📈 Business Quality ~27%

Is this a good business? We measure revenue growth, free cash flow generation, and profit margins over 5–10 years. A great business earns more cash than it spends — consistently.

Revenue Growth 5Y FCF Margin TTM Net Margin FCF Trend
💰 Capital Allocation ~18%

How does management use the money? We reward buybacks, dividends, and smart reinvestment. We penalise excessive share dilution — the silent tax on shareholders.

Buybacks TTM Dividends TTM CapEx Intensity Shareholder Return
🏷️ Valuation ~18%

Is it cheap or expensive relative to its own history? We compare current multiples against 5-year averages using five complementary metrics — not just P/E.

P/FCF vs 5Y Avg EV/EBITDA Forward P/E P/S · P/B · PEG
🔬 Earnings Quality ~18%

Can we trust the numbers? We apply the Piotroski F-Score (9-point financial health checklist) and measure accruals, ROIC, ROE, and cash-to-earnings conversion.

Piotroski F-Score Accruals Ratio ROIC · ROE Earnings Quality %
⚠️ Risk Assessment ~9%

What could go wrong? We measure leverage, interest coverage, short-term debt risk, and SBC dilution — using the Altman Z-Score framework to flag distress signals.

Net Debt/EBITDA Interest Coverage Short-term Debt Risk SBC/FCF
🔴 Altman Z-Score — Bankruptcy Prediction
Z > 2.99
Safe Zone
1.81 – 2.99
Grey Zone
Z < 1.81
Distress Risk
Altman (1968) demonstrated 72–80% accuracy in predicting bankruptcy 1 year ahead, 69% accuracy 2 years ahead. Used globally in credit analysis and institutional risk management.
🚀 Momentum ~10%

Is the market moving with or against this stock? We apply the academic 12M-1M price momentum method — excluding the last month to remove short-term noise — combined with EPS surprise trend over the last 4 quarters.

12M-1M Return 6M-1M Return EPS Surprise Trend

* Weights vary by sector. Utilities: Risk increases to 20%, Business to 20%. Healthcare: Earnings Quality increases to 25%. Energy: Valuation increases to 25%. Consumer Discretionary: Momentum increases to 13%. 7 sector profiles total.

DCF Valuation Suite

Beyond scoring — calculate intrinsic value.

A score tells you whether a company is financially strong. A valuation tells you whether the price is right. The DCF Valuation Suite applies the same academic methodology used by professional analysts — with one critical improvement: instead of a single estimate, we give you a probability distribution.

🧮 WACC Calculator
Calculates the Weighted Average Cost of Capital using the CAPM framework, relevered beta, country risk premium, and size premium table. Auto-populated from live market data when you enter a ticker.
→ Modigliani & Miller (1958) · Damodaran
📊 3-Scenario DCF
Bull, Base, and Bear scenarios with auto-suggested growth rates from historical CAGR. Supports both Gordon Growth (perpetuity) and EV/EBITDA Exit Multiple terminal value methods.
→ Graham & Dodd (1934) · Damodaran
🎲 Monte Carlo Simulation
Runs thousands of simulations varying growth rates and WACC within realistic ranges. Returns a probability distribution of intrinsic value — not just one number. Shows the 10th, 50th, and 90th percentile outcomes.
→ Metropolis & Ulam (1949)
🔄 Reverse DCF
Works backwards from the current stock price. Instead of asking "what is it worth?", it asks: "what growth rate does today's price already assume?" If the implied growth is unrealistic, the stock may be overvalued.
→ Mauboussin (2001)
📋 Sensitivity Analysis
Full sensitivity table showing how Fair Value changes across combinations of growth rates and WACC assumptions. Understand the range of outcomes visually.
→ Standard practitioner methodology
📰 Earnings Update Mode
Enter actual quarterly results vs analyst estimates to instantly recalculate fair value after earnings. Built for active investors who track companies through each reporting cycle.
→ Damodaran earnings revision methodology

Most DCF tools give you one number. One number is always wrong — the future is uncertain. Our Monte Carlo simulation gives you a range and a probability, so you know not just what the stock might be worth, but how confident you should be in that estimate. Open DCF Suite →

Academic foundation

14 papers. Decades of research. All applied here.

Every factor, every formula, every scoring decision is grounded in peer-reviewed academic research. Unlike platforms that hide behind proprietary black boxes, we cite the original paper for everything we do. You always know exactly why a stock scored the way it did.

Piotroski (2000)
Journal of Accounting Research
Earnings Quality
9-point financial health scoring system based on profitability, leverage, and operating efficiency signals. Demonstrated that high F-Score stocks outperformed low F-Score stocks by 23% annually in the original study. One of the most cited papers in quantitative equity investing.
→ Used in: Earnings Quality factor (F-Score, 9 signals)
Altman (1968)
Journal of Finance
Risk Assessment
The Altman Z-Score — a 5-variable linear discriminant model predicting bankruptcy probability from financial ratios. Demonstrated 72–80% accuracy in predicting bankruptcy 1 year ahead and 69% accuracy 2 years ahead. Z > 2.99 = safe zone; 1.81–2.99 = grey zone; < 1.81 = distress risk. Used globally in credit analysis and institutional risk management for over 50 years.
→ Used in: Risk Assessment factor (bankruptcy prediction)
Jegadeesh & Titman (1993)
Journal of Finance
Momentum
Foundational momentum research demonstrating that stocks with strong 6–12 month price performance continue to outperform over the next 3–12 months. Excluding the most recent month reduces mean-reversion noise — a refinement that improves momentum signal quality.
→ Used in: Momentum factor (12M-1M, 6M-1M price returns)
Sloan (1996)
Accounting Review
Earnings Quality
Demonstrates that earnings with high accrual components predict lower future returns. Cash-backed earnings are more reliable than accounting-based earnings. The accruals ratio is now a standard measure of earnings quality in institutional analysis.
→ Used in: Earnings Quality factor (Accruals Ratio)
Fama & French (1992, 2015)
Journal of Finance
Sector Weights
Multi-factor asset pricing models establishing the role of size, value, profitability, and investment factors in explaining stock returns. Basis for our sector-adjusted weighting system — different factors matter differently across industries.
→ Used in: All 7 sector-adjusted weight profiles
Damodaran
NYU Stern — ongoing
Valuation · DCF
Aswath Damodaran's publicly available WACC methodology, FCF yield framework, CapEx intensity analysis, country risk premium tables, and Revenue Exit Multiple approach. The global standard reference for practitioner valuation — used by investment banks, PE funds, and asset managers worldwide.
→ Used in: DCF Suite (WACC, scenarios), Valuation factor, Capital Allocation
Modigliani & Miller (1958)
American Economic Review
DCF · WACC
Foundational capital structure theory — the theoretical basis for WACC calculation. The cost of capital depends on the blend of debt and equity financing and their respective risk profiles. Nobel Prize in Economics (Miller, 1990).
→ Used in: DCF Suite (WACC calculation)
Graham & Dodd (1934)
Security Analysis
Valuation · DCF
The original framework for fundamental analysis and value investing. Discounted cash flow valuation, margin of safety, and earnings normalisation all originate from this work. The foundation of modern security analysis — still directly applicable 90 years later.
→ Used in: DCF Suite, Valuation factor, Business Quality
Metropolis & Ulam (1949)
Journal of the American Statistical Association
Monte Carlo
Original Monte Carlo simulation methodology — developed for nuclear physics, now applied across finance, engineering, and risk management. Used in our DCF model to generate probability distributions for intrinsic value across thousands of scenarios instead of a single point estimate.
→ Used in: DCF Suite (Monte Carlo simulation)
Mauboussin (2001)
Credit Suisse First Boston
Reverse DCF
Reverse DCF methodology — inferring the growth rate the current market price implies, rather than projecting a growth rate to estimate value. Useful for understanding what the market expects and whether those expectations are realistic.
→ Used in: DCF Suite (Reverse DCF)
Data sources

Official filings. Not estimates.

Our primary data source is SEC EDGAR — the official US Securities and Exchange Commission database of company filings. Every number you see is sourced from official 10-K and 10-Q reports, not third-party estimates or analyst projections. This is the same data that institutional analysts work from.

🏛️
SEC EDGAR — Primary source
Official 10-K annual reports and 10-Q quarterly reports filed directly with the US Securities and Exchange Commission. XBRL-structured financial data going back 10+ years per company. Completely free, public, and government-maintained. This is the ground truth — the same filings auditors and institutional investors use.
How we compare

More depth. Open methodology. Less cost.

Most platforms show you data or hide their scoring in a proprietary black box. We score every metric using published academic methods, explain every formula on this page, and give you professional-grade tools for free. No account required to start.

Feature The Compound Family Typical Screeners
Free–$10/mo		 Premium Platforms
$20–35/mo
Academic scoring model
14 peer-reviewed papers		 ✓ Fully transparent ✗ Raw data only ~ Proprietary / hidden
Altman Z-Score bankruptcy prediction
72–80% accuracy 1 year ahead		 ✓ Full Z-Score ✗ ~ Limited
Sector-adjusted weights
7 sector profiles		 ✓ 7 profiles ✗ ~ Limited
10+ years historical data
From official SEC filings		 ✓ SEC EDGAR ~ Varies ✓
Full DCF + Monte Carlo
Thousands of scenarios		 ✓ Free ✗ ~ Basic / paid
Reverse DCF
What growth is priced in?		 ✓ Free ✗ ✗
Momentum factor
12M-1M academic method		 ✓ Academic ✗ ~ Limited
No account required
Full scores visible instantly		 ✓ Always ~ Often limited ✗ Login required
AI interpretation
Plain-English explanation		 ✓ 1/day free · unlimited Premium ✗ ~ Some platforms
Methodology fully disclosed
Every formula explained		 ✓ This page ✗ ✗ Black box

Unlike platforms that hide their scoring logic, every formula we use is documented here — referenced to the original academic paper. You always know exactly why a stock scored the way it did. See full pricing comparison →

Important notice

A model is a tool. Not a verdict.

⚠ Model limitations & continuous improvement. The Compound Family scoring model is continuously developed and improved. Like any quantitative model, scores may be affected by data quality issues, sector-specific accounting differences, extraordinary items, companies in transition, or factors not captured by the underlying metrics. The model does not account for qualitative factors such as management quality, competitive moat, geopolitical risk, or pending litigation.

Scores should never be the sole basis for any investment decision. Always conduct your own comprehensive research, review primary sources including official company filings, and consider consulting a qualified financial advisor before making any investment. Past model performance does not guarantee future accuracy. Full disclaimer →
Common questions

Frequently asked questions

The Piotroski F-Score is a 9-point financial health checklist developed by Joseph Piotroski in 2000 and published in the Journal of Accounting Research. It evaluates a company across three categories: profitability (4 signals), leverage and liquidity (3 signals), and operating efficiency (2 signals). A score of 8–9 indicates a financially strong company; 0–2 signals potential distress. The original study showed high F-Score stocks outperformed low F-Score stocks by 23% annually. We apply this directly in the Earnings Quality scoring factor.
Altman (1968) demonstrated 72–80% accuracy in predicting bankruptcy 1 year ahead, and 69% accuracy 2 years ahead in the original study. The model uses 5 financial ratios (working capital, retained earnings, EBIT, market cap, and sales — all scaled by total assets) combined into a single Z-Score. A Z-Score above 2.99 indicates a safe zone; between 1.81 and 2.99 is the grey zone; below 1.81 signals high distress risk. The model has been used in credit analysis and institutional risk management for over 50 years.
We use the academic 12M-1M momentum method from Jegadeesh and Titman (1993): the 12-month price return excluding the most recent month. Removing the last month reduces short-term reversal noise that is well-documented in academic literature. We also calculate 6M-1M return and combine both with an EPS surprise trend measured over the last 4 quarters.
DCF stands for Discounted Cash Flow — a method of valuing a company based on its projected future free cash flows, discounted back to today using the WACC. A standard DCF gives a single estimate — which is always wrong because the future is uncertain. Our Monte Carlo model runs thousands of simulations, varying growth rates and WACC assumptions within realistic ranges, to produce a full probability distribution of intrinsic value. This tells you not just what a stock might be worth, but how confident you should be in that estimate — and what the downside scenario looks like.
Reverse DCF works backwards from the current stock price. Instead of asking "what is this company worth?", it asks: "what growth rate does today's price already assume?" Based on Mauboussin (2001), this is one of the most powerful valuation tools for growth stocks — it tells you what the market is pricing in, and whether those expectations are realistic given the company's track record. If the implied growth rate is higher than the company has ever achieved, the stock may be overvalued.
Different sectors operate under fundamentally different business models. A utility company is valued on stability, regulated income, and dividend yield — so balance sheet risk matters more (weight 20%). A software company is valued on growth and margin expansion — so business quality matters more (weight 27%). Applying the same fixed weights to all sectors produces systematically misleading scores. We apply 7 sector profiles so each stock is judged by criteria that actually matter for its industry.
Yes — the core platform is completely free with no account required. All 6 scoring factors, all metric cards with tooltips, all historical charts, Piotroski F-Score detail, Altman Z-Score, the full DCF Suite (Monte Carlo, Reverse DCF, Sensitivity, Earnings Update Mode), and Save Model are all free. Free users also get 1 AI analysis per day after signing in with Google. Premium ($19/month or $149/year) unlocks Fair Value shown directly on the stock page and unlimited AI analysis. See the full pricing page →
No. All scores, analyses, and AI interpretations are provided for informational and educational purposes only. Nothing on thecompoundfamily.com constitutes investment advice or a recommendation to buy or sell any security. The platform is a research and education tool — it helps you understand a company's financial profile. All investment decisions are solely your responsibility. See our full disclaimer.