Methodology deep-dive
The math behind every concluded value
Not a black box. This is the exact analytical engine the platform runs from cap table to per-share fair value — the WACC build, the Black-Scholes option pricing, the allocation waterfalls, and the marketability discount, every formula and every input exposed and persisted as auditable data.
Built on the AICPA Practice AidFull CAPM & Black-Scholes implementationsReproducible to the cent
End-to-end flow
From financials to per-share fair value
Every box below is a persisted, queryable, audit-loggable entity. Every arrow is a deterministic calculation whose inputs are fixed at finalization.
- 1
Cap table + financial statements
A finalized, point-in-time cap table and the income statement & balance sheet feed everything downstream.
- 2
Valuation methods
DCF, GPC, GTM, OPM Backsolve, and Post-Money each run independently, producing a fully-auditable indicated equity value.
- 3
Hybrid weighting
Each method carries a percentage weight; the platform sums them into a single Weighted Indicated Equity Value.
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Allocation engines
OPM, CVM, and CSE run independently against the weighted equity value across the full preference stack.
- 5
Hybrid allocation
The engines are percentage-weighted and merged into one per-security allocation table, with the weights persisted as JSON.
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DLOM layer
Chaffe, Finnerty, Longstaff, or restricted-stock discount — applied company-wide or per share class via Merton-elasticity volatility.
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Per-class fair value per share
The canonical number that goes on the front of the 409A, ASC 820, ASC 718, or Indicated Value report.
Income approach · DCF
A precise, configurable DCF engine
Every input an analyst would tune in Excel is exposed as a structured field, every intermediate calculation is persisted, and a 2-D sensitivity grid around the concluded value is generated automatically.
Discount-rate (WACC) build
Constructed from first principles using the full CAPM framework — curve-sourced risk-free rate, equity risk premium, raw and Bloomberg-style adjusted beta with Hamada unlevering / re-levering, Duff & Phelps / Kroll size premium, company-specific and country-risk premia, and an after-tax cost of debt — with selectable capital-structure basis, MROUND rounding, and analyst overrides on every selected row.
WACC = (Re-Levered β × ERP + Risk-Free Rate
+ Size Premium + Company-Specific Premium
+ Country Risk) × Weight_Equity
+ After-Tax Cost of Debt × Weight_DebtFree cash flow projections
A configurable stub period, five explicit projection years plus a terminal year, in either Detail (revenue-driven build of COGS, OpEx, CapEx, D&A and working capital) or Simple (analyst-supplied EBIT/EBITDA) mode — with an optional mid-year convention and full TCJA two-bucket NOL carryforward (pre-2018 unlimited, post-2017 capped at 80%).
FCF = NOPAT + D&A − CapEx − ΔWorking Capital
NOPAT = EBIT − Max(0, Adjusted EBIT × Blended Tax Rate)Terminal value
Two supported methods — Gordon Growth perpetuity (default 3% LTGR, configurable) or an exit multiple sourced from the GPC/GTM comparable set.
Gordon: TV = Terminal FCF / (WACC − g)
Exit: TV = Terminal Metric × Exit MultipleEquity bridge
Total business enterprise value is bridged to common equity using the standard cash, debt, minority-interest and preferred adjustments.
TBEV = PV(Explicit FCF) + PV(Terminal Value)
Equity = TBEV + Cash + Excess Inv.
− Debt − Minority Int. − PreferredAutomatic 2-D sensitivity grid
For every saved DCF, the platform produces a matrix of discount rate (± 2 steps around the concluded WACC) against the terminal driver (LTGR or exit multiple, ± 2 steps). The center cell reconciles exactly to the persisted concluded value — a defensible “range of value” exhibit without a manual rebuild.
Market approach · GPC & GTM
Comparables grounded in live Capital IQ data
Guideline Public Company and Guideline Transactions analysis on a shared statistical framework — not screenshots, not stale extracts. Every multiple is computed per comparable and every selection is recorded.
Multiples per comparable
Revenue and EBITDA multiples across LFY, LTM, NFY, NFY+1 and NFY+2, with a selectable MVIC or BEV denominator. Deal comparables (GTM) use the identical structure with optional recency time-weighting for stale transactions.
Statistical aggregation
Mean, median, harmonic mean (the statistically correct aggregator for ratios), and 25th/75th percentiles — with NM exclusion for negative or distorted multiples. The implicit control premium emerges naturally from the GTM-vs-GPC spread.
Transaction-calibrated · OPM Backsolve
Black-Scholes inversion across the preference stack
Given a recent priced round, the platform inverts Black-Scholes across the full preference stack to solve for the total equity value that exactly reconciles the observed transaction price — calibrated, not assumed.
Breakpoint construction
The full equity-value waterfall is derived automatically from seniority, liquidation multipliers, participation caps, conversion thresholds, and option/warrant strikes.
Per-tranche pricing
Each breakpoint range is valued as an incremental call option on enterprise value; per-class allocations are summed across tranches.
Equity-value solve
A bisection solver (with bound expansion) finds the equity value at which the anchor security's per-share value equals the observed round price.
C(S, K, T, σ, r) = S × N(d₁) − K × e^(−rT) × N(d₂)
d₁ = [ln(S/K) + (r + σ²/2) × T] / (σ × √T)
d₂ = d₁ − σ × √TDeterministic preferred-warrant exercise
For cap structures where preferred-stock warrants are economically certain to exercise, Sandwich mode treats them as exercise events rather than option-style claims, builds a single combined liquidation-preference row, and shifts the breakpoint geometry accordingly — producing materially more defensible per-class allocations than a naive treatment would.
Equity allocation · OPM / CVM / CSE
Three engines, hybrid-weighted to one table
Once the Weighted Indicated Equity Value is set, it is allocated across every share class. Analysts assign percentage weights to the engines and the platform merges them into a single per-security allocation table, with the weights persisted as JSON.
Option Pricing Model
Each security class is valued as a portfolio of call options on enterprise value, struck at each breakpoint in the preference / participation / conversion waterfall. Outputs include per-class value, per-share value, ownership %, and the full breakpoint table.
Current Value Method
Deterministic liquidation waterfall — no optionality. Dividends paid first, then senior and junior preferences in strict seniority order, with participation caps enforced and the residual flowing to common. The right method for near-term or distressed exits.
Common Stock Equivalent
Every security is treated as if converted to common; the equity value allocates pro-rata across the fully-diluted common-equivalent share count (treasury-stock-method for in-the-money options and warrants). A clean treatment for mature structures and a cross-method sanity check.
Marketability discount · DLOM
Multiple models, applied company-wide or per class
Each methodology is implemented per its canonical formulation, applied on top of the merged allocation result to reach the final per-share fair value.
Protective Put
An at-the-money European put representing the cost of being unable to sell over the holding period. Typical range 15–35%.
Put = K·e^(−rT)·N(−d₂) − S·e^(−qT)·N(−d₁)Average-Strike Put
An Asian-style average-strike put — more conservative than Chaffe, reflecting the ability to average into a sale. Typical range 10–25%.
Put = S·e^(−qT)·[N(x₁) − N(−x₁)]Geometric Asian Put
A geometric-averaging adjustment to the Black-Scholes put, capturing path dependence. Typical range 20–30%.
σ* = σ/√3 , b* = (r−q)/2 − σ²/12Empirical Studies
A static empirical benchmark (median ≈ 27%) from the published restricted-stock literature — used when volatility-based inputs are unreliable.
DLOM ≈ 27% (empirical median)Per-class volatility via Merton elasticity
For share-class-level DLOM, the platform derives a class-specific volatility from the OPM breakpoint outputs — higher for junior common (and therefore a higher discount), lower for senior preferred. The result is a per-class DLOM that is theoretically defensible rather than a single hand-picked discount applied across a complex preference stack.
σ_class = σ_equity × Σ[ wᵢ × (N(d₁ᵢ) − N(d₁ᵢ₊₁)) ] × S / V_classNo hidden Excel cell
Every input is structured data the platform can re-derive
Each method is stored with its weight, equity value, and a strongly-typed link to the underlying engine; the merged allocation persists the exact method weights as JSON alongside pre- and post-DLOM totals. Every change is captured in a field-level audit trail — so even after finalization, any auditor can drill into exactly how a number was produced.
Frequently asked questions
How does the platform build the discount rate (WACC)?
The platform constructs WACC from first principles using the full CAPM framework: a curve-sourced risk-free rate, equity risk premium, raw and Bloomberg-style adjusted beta with Hamada unlevering/re-levering against the comparable set, a Duff & Phelps / Kroll size premium, company-specific and country-risk premia, and an after-tax cost of debt. Capital structure can be point-in-time or trailing-quarter average, with selectable MROUND rounding (0.5%, 1%, 2.5%) and analyst overrides on every published-style selected row.
What is OPM Backsolve and what is Sandwich mode?
OPM Backsolve inverts the Black-Scholes option-pricing framework across the full preference stack to solve for the total equity value that exactly reconciles a recent priced round (e.g. a Series C at $X/share). Sandwich mode handles cap structures where preferred-stock warrants must be treated as deterministic exercise events rather than option-style claims — it builds a single combined liquidation-preference row and shifts the breakpoint geometry, producing materially more defensible per-class allocations for complex preferred-warrant structures.
How is DLOM computed per share class?
For share-class-level DLOM the platform derives a class-specific volatility using the Merton elasticity framework from the OPM breakpoint outputs. Junior common gets a higher volatility — and therefore a higher discount — while senior preferred gets a lower one. This produces a theoretically defensible per-class DLOM rather than a single hand-picked discount applied across the whole preference stack.
Is any of this hidden in a spreadsheet?
No. Every percentage, every selected multiple, every comparable, every breakpoint, every Black-Scholes input, and every DLOM selection is stored as structured data and captured in a field-level audit trail. There is no hidden Excel tab anywhere in the workflow — any reviewer can re-derive the concluded value to the cent.
Which reporting standards does this methodology serve?
The same engine produces 409A (fair market value), ASC 820 (fair value measurement), and ASC 718 (stock-based compensation) reports in the US, and an Indicated Value report for India and the rest of the world. The methods, allocation, and DLOM are identical across standards — only the reporting output differs.
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