MODEL 1
Intrinsic
Bayesian DCF
Core Formula
EV = Σ FCFₜ/(1+WACC)ᵗ + TV/(1+WACC)ⁿ
Projects free cash flow over a 10-year horizon using 10,000 Monte Carlo simulations with a deterministic seed for reproducibility. Each simulation path draws from a Bayesian posterior distribution of growth rates (from Python MCMC training or macro-conditioned heuristics) and applies Merton (1976) jump-diffusion — modeling sudden FCF shocks with λ=0.10 intensity and -15% mean jump. Growth, WACC, and terminal rates are all stochastic. The 3-year weighted FCF base (50%/30%/20% recency weighting) smooths one-time events. WACC includes Hull-White stochastic rate drift (±30bps) for rate-sensitive sectors. Enterprise value converts to equity via a full institutional bridge: EV − lease-adjusted debt + cash − minority interest − preferred stock.
Key Innovation10,000 Monte Carlo + Merton jump-diffusion
Data InputsOCF, CapEx, Beta, FRED rates, VIX, MCMC posteriors
Best ForStable FCF companies
MODEL 2
Intrinsic
Earnings Power Value
Core Formula
EPV = NOPAT / Kₑ
Implements Bruce Greenwald's complete three-part framework from Columbia Business School: (1) Reproduction Asset Value (RAV) — the cost to rebuild all tangible and intangible assets from scratch with sector-specific replacement multipliers (PP&E at 1.1×, R&D IP at 3-5× annual spend, brand equity from SGA × brand multiplier), (2) Earnings Power Value — cyclically normalized EBIT across all available fiscal years, after-tax, adjusted for sector-specific maintenance CapEx ratios (90% for capital-heavy, 30% for R&D-heavy, 60% default) and stripped of restructuring charges, goodwill impairment, and FX gains/losses, capitalized at the unlevered cost of equity (Kₑ), and (3) Franchise Value — the spread between EPV equity and RAV equity, classifying companies as Wide Moat, Narrow Moat, No Moat, or Value Destroyer. Adds a bounded growth premium (capped at 30%) when ROIC exceeds WACC, using McKinsey-style incremental value computation.
Key InnovationGreenwald Franchise Value Trilogy + RAV
Data InputsMulti-year EBIT, D&A, CapEx, SGA, R&D, PP&E, Goodwill
Best ForMature earners, Moat companies
MODEL 3
Intrinsic
Markov DDM
Core Formula
V = Σ Dₜ × Pᵢⱼ / (1+Kₑ)ᵗ
Extends the classic Gordon Growth Model by modeling dividend growth as a Markov chain with discrete states representing growth regimes (high growth, stable, cut, freeze). Transition probabilities are estimated empirically from multi-year DPS history or loaded from Python-trained transition matrices. Critically, uses total shareholder yield (dividends + share buybacks) rather than dividends alone — expanding coverage to non-dividend payers like AAPL, GOOG, and META that return capital via repurchases. The buyback yield is computed as shares repurchased ÷ market cap. A 10-year forward simulation applies regime-specific growth rates with stochastic transitions, then discounts at the cost of equity derived from CAPM with VIX-adjusted equity risk premium.
Key InnovationRegime-switching + total shareholder yield
Data InputsDPS history, shares repurchased, payout ratio, beta
Best ForDividend payers, Buyback-heavy (AAPL/GOOG)
MODEL 4
Asset-Based
Dynamic NAV
Core Formula
NAV = Σ(Assets × Recovery%) − Liabilities
Computes net asset value by marking each balance sheet item to fair value using sector-specific recovery rates calibrated for stress scenarios. PP&E and real estate assets use higher recovery rates (0.70-0.90) than intangibles (0.30-0.50) and goodwill (marked to zero under distress). Cash is taken at face value. Inventory recovery varies by sector (retail ~60%, manufacturing ~50%, perishable ~30%). Critically, deducts hidden liabilities that standard NAV misses: operating lease obligations (ASC 842), underfunded pension deficits (obligation − plan assets), off-balance-sheet VIE liabilities, litigation reserves, and asset retirement obligations. The final equity bridge strips minority interest and preferred stock. Especially powerful for REITs (real estate at appraised value), banks (loan book at marked value), and resource companies (reserves at commodity-adjusted value).
Key InnovationSector-specific recovery rates + hidden liabilities
Data InputsAll balance sheet items, lease liabilities, pension, preferred
Best ForREITs, Banks, Insurance, Resource companies
MODEL 5
Intrinsic
EROIC Spread
Core Formula
EV = IC + Σ(ROIC−WACC)×IC/(1+WACC)ᵗ
Implements the McKinsey & Company economic profit framework. Capitalizes R&D expenditure as an intangible asset (with industry-specific amortization: 5 years for pharma/tech, 2 years for utilities, 3.5 years default) and adds it to invested capital alongside capitalized software, lease-adjusted debt, pension deficits, and equity method investments. Goodwill is adjusted downward by 50% of excess when goodwill exceeds 30% of total assets (serial acquirer penalty). Computes NOPLAT from normalized EBIT (stripped of restructuring, impairment, and FX items) × (1 − effective tax rate from EDGAR). The EROIC spread (ROIC − WACC) is projected over a Competitive Advantage Period (7-15 years based on historical ROIC consistency), with a linear fade rate of 10% per year toward WACC. Invested capital grows each year by reinvestment = NOPLAT × reinvestment rate (g/ROIC per McKinsey). Classifies companies as Strong Value Creator, Value Creator, Neutral, or Value Destroyer.
Key InnovationMcKinsey Value framework + CAP fade + R&D capitalization
Data InputsNOPLAT, invested capital, R&D, leases, pension, goodwill
Best ForMoat companies, High-ROIC businesses
MODEL 6
Intrinsic
ML-RIV
Core Formula
V = BV + Σ(ROE−Kₑ)×BVₜ/(1+Kₑ)ᵗ
Machine learning-enhanced Residual Income Valuation that decomposes ROE into five DuPont components: net profit margin, asset turnover, financial leverage, tax burden, and interest burden. Models the persistence of each factor independently using multi-year financial data — high-persistence factors (like recurring margins) are projected forward more aggressively than mean-reverting ones (like leverage spikes). Computes abnormal earnings as the spread between ROE and cost of equity (Kₑ), applied to growing book value. Includes clean surplus adjustments for OCI violations (FX translation, pension adjustments, AFS securities) that bypass the income statement. Especially effective for financial institutions where book value is a meaningful anchor and earnings quality varies significantly across cycles.
Key Innovation5-factor DuPont ROE decomposition + persistence modeling
Data InputsBook value, ROE components, clean surplus, multi-year financials
Best ForBanks, Financials, Insurance
MODEL 7
Scenario
First Chicago
Core Formula
V = P₁×V₁ + P₂×V₂ + P₃×V₃
Constructs three independent valuation scenarios — bull (expansion), base (steady state), and bear (recession/disruption) — each with its own revenue growth trajectory, margin assumptions, and terminal multiple. Probability weights are dynamically adjusted based on: (1) the company's current position in its business cycle (using trailing revenue growth momentum), (2) sector cyclicality characteristics (biotech gets higher bull/bear spread than utilities), and (3) macro regime indicators (VIX level, yield curve slope, credit spreads). Each scenario runs a full mini-DCF with scenario-specific WACC. The final value is the probability-weighted average across all three outcomes. Particularly valuable for high-uncertainty situations where a single-point DCF gives false precision — early-stage growth, biotech with binary drug trial outcomes, and cyclicals at inflection points.
Key InnovationCyclicality-adjusted probability weights + sector benchmarks
Data InputsRevenue growth, margins, FCF, analyst estimates, VIX
Best ForGrowth stocks, Biotech, Cyclicals, Turnarounds
MODEL 8
Option-Based
PWERM
Core Formula
Equity = Call(V, D, σ, T)
Probability-Weighted Expected Return Method using Merton's structural credit model, which treats equity as a European call option on the firm's total assets with the debt face value as the strike price. Runs 5,000 Monte Carlo scenarios to simulate asset value paths with stochastic volatility and jump-diffusion dynamics. In each scenario, equity value = max(0, Asset Value − Total Debt), capturing the asymmetric payoff structure of equity. The option-theoretic framework naturally handles distressed companies (where equity has significant optionality even when assets < debt) and M&A targets (where the probability of a premium bid can be embedded in scenario weights). Particularly powerful for highly leveraged firms where traditional DCF breaks down because small changes in asset value create large swings in equity value.
Key InnovationMerton structural model + stochastic volatility
Data InputsAsset value, total debt, volatility, risk-free rate
Best ForDistressed companies, M&A targets, High-leverage
MODEL 9
Relative
Regime Cross-Sectional
Core Formula
V = Peer_Multiple × Metric × Regime_Adj
Identifies the current macroeconomic regime using six indicators: VIX level, yield curve slope, high-yield credit spread, GDP growth rate, unemployment trend, and inflation expectations. Classifies into one of four regimes (expansion, late-cycle, contraction, recovery), each with historically calibrated multiple ranges. Within the identified regime, selects sector-appropriate valuation multiples (EV/EBITDA for industrials, P/B for banks, P/FFO for REITs) and applies them to the company's normalized metrics. The PEG ratio is adjusted for the regime's growth premium or discount — growth stocks get lower PEG premiums in contraction regimes where growth is scarce and valuable. Cross-references against sector peers using 6-dimensional similarity (size, profitability, growth, leverage, volatility, dividend yield) rather than simple industry classification.
Key Innovation6-indicator macro regime + sector-adjusted multiples
Data InputsP/E, EV/EBITDA, P/B, peers, VIX, yield curve, spreads
Best ForAll sectors with sufficient peers
MODEL 10
Hybrid
Sentiment SOTP
Core Formula
V = Σ(Segmentᵢ × Multipleᵢ × Sentiment_Adj)
Sum-of-the-parts valuation using actual business segment data from EDGAR filings (revenue and operating income by segment), with each segment valued at its industry-appropriate multiple. Overlays a four-layer sentiment analysis from GDELT news data: (1) overall company tone score, (2) industry-level sentiment, (3) segment-specific news volume, and (4) sentiment momentum (improving vs deteriorating). Sentiment adjusts segment multiples by ±15% — positive sentiment expands multiples, negative sentiment compresses them. The sentiment signal is asymmetric: negative news impacts multiples more strongly than positive news (behavioral finance loss aversion). Particularly effective for conglomerates like Berkshire Hathaway, General Electric, and Alphabet, where the whole-company multiple misses segment-level value creation or destruction.
Key InnovationEDGAR segment data + GDELT news sentiment
Data InputsEDGAR segments, GDELT tone scores, sector multiples
Best ForConglomerates, Diversified, Multi-segment
MODEL 11
Ensemble
CUCE Ensemble
Core Formula
V = Σ wᵢVᵢ, where wᵢ ∝ 1/(σᵢ² × Cᵢⱼ)
The meta-model that combines all other model outputs into a single consensus fair value. Uses Correlation-Unbiased Certainty-Equivalent weighting: each model's weight is proportional to the inverse of its variance (high-confidence models get more weight) but adjusted for cross-model correlation (models that agree with each other have their combined weight dampened to prevent double-counting of similar signals). Outlier models (those more than 2 standard deviations from the median) have their weights reduced automatically. The ensemble produces a final fair value that is demonstrably more stable and accurate than any individual model — reducing model-specific bias while preserving signal from high-conviction estimates. The confidence score reflects the dispersion of the underlying models: tight agreement → high confidence, wide dispersion → lower confidence.
Key InnovationCorrelation-adjusted inverse-variance weighting
Data InputsAll 12 model outputs, confidences, cross-correlations
Best ForAll sectors (meta-model)
MODEL 12
Relative
FTNN Topology
Core Formula
V = Σ KNN_j × w_j, where w ∝ K(d_ij)
Financial Topology Neural Network that finds the most financially similar companies using a 6-dimensional Gaussian kernel similarity function. The six dimensions are: (1) log market cap (size), (2) EBITDA margin (profitability), (3) revenue growth rate (momentum), (4) debt-to-equity ratio (leverage), (5) beta (volatility), and (6) dividend yield (income). Rather than grouping by SIC code or GICS sector (which puts Apple in "Consumer Electronics" alongside Samsung), FTNN finds peers based on actual financial DNA. Companies with similar financial profiles get higher kernel weights, and their market-implied valuations (EV/EBITDA, P/E) are used to derive fair value. The Gaussian kernel naturally handles non-linear relationships and gives exponentially more weight to closer matches. Particularly effective for companies that don't fit neatly into traditional sectors.
Key Innovation6D Gaussian kernel financial similarity
Data InputsSize, margins, growth, leverage, volatility, yield
Best ForTech, Healthcare, Consumer, any sector
MODEL 13
Intrinsic
RCMH-DCF
Core Formula
V = Σ Pᵣ × DCF_r (r ∈ {expansion, slowdown, recession, recovery})
Regime-Conditioned Macro-Hedged DCF that runs four complete, independent DCF valuations in parallel — one for each macroeconomic regime (expansion, slowdown, recession, recovery). Each regime uses different discount rates, growth assumptions, and terminal values calibrated from FRED macroeconomic data: the expansion DCF uses lower spreads and higher growth; the recession DCF uses stressed WACC and negative growth assumptions. Regime probabilities are estimated from current macro indicators (VIX level, yield curve slope, GDP growth, high-yield credit spread). The Hull-White stochastic rate model adds interest rate drift (±30bps annual volatility) within each regime. The final valuation is the probability-weighted average across all four regimes. Especially valuable for rate-sensitive sectors (utilities, REITs, banks) where the interest rate environment fundamentally changes the cost of capital and asset values.
Key Innovation4-regime parallel DCF + FRED macro integration
Data InputsFCF, FRED macro series, VIX, yield curve, GDP, HY spreads
Best ForRate-sensitive, Utilities, REITs, Cyclicals
Data Pipeline & Sources
CirclFi ingests data from four institutional-grade sources daily:
SEC EDGAR
700+ XBRL tags mapped across US GAAP, IFRS, Banking, Insurance, REIT, and Utility sector templates. Multi-year 10-K and 10-Q filings.
FRED (Federal Reserve)
Risk-free rates, VIX, yield curve slope, GDP growth, high-yield credit spreads, and inflation expectations. Drive WACC and regime classification.
Market Data
Real-time pricing, beta, market cap, dividend yield, short interest, and sector peer multiples for relative and hybrid models.
GDELT News Sentiment
Global news tone scores, volume, and sentiment momentum at the company and industry level for behavioral-adjusted multiples.
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