Separate finite-stage dividend payments from the terminal dividend so timing and payout assumptions remain visible.
The decision this tutorial makes visible
Dividend Discount Model is useful because it turns a set of explicit assumptions into a reconciled estimate while preserving the diagnostics needed to challenge that estimate.
The precise question is: How can an explicit dividend forecast and a stable terminal payout be converted into equity value per share?
A practitioner needs to know what the diagnostic does and does not justify. A builder needs a contract that can be reproduced from the same point-in-time inputs in Python, TypeScript, a visual, and a browser lab.
Intuition before notation
The shareholder claim is valued directly from cash distributions: discount each forecast dividend, then capitalize the next dividend after the explicit horizon.
The result depends on the declared algorithm scope, input clocks, units, equality and rounding policies, and unsupported-state treatment. Change one of those and the output represents a different decision even when its field name is unchanged.
Scope and nearby methods
Per-share multi-stage dividend discount model with an explicit dividend vector, constant cost of equity, and Gordon terminal value after the final explicit period.
| Variant | Definition | Best use | Main limitation |
|---|---|---|---|
| Canonical repository convention | Per-share multi-stage dividend discount model with an explicit dividend vector, constant cost of equity, and Gordon terminal value after the final explicit period. | Reproducible teaching and parity | One explicit convention, not a universal default |
| Two-stage DDM | Formula-driven high-growth stage before stability | Structured growth transition | Less flexible than explicit dividends |
| Total payout model | Dividends plus net buybacks | Material repurchases | Requires reliable repurchase and issuance forecasts |
What is sourced, selected, synthetic, and derived
| Role | Material claim | Evidence | Boundary |
|---|---|---|---|
| Definition source | Equity value can be expressed as the present value of expected dividends; the Gordon model uses next-period dividend divided by cost of equity less stable growth. | S1: General DDM and Gordon growth model | Applicability depends on payout and growth assumptions; the source does not validate this package's synthetic numbers. |
| Boundary source | A going-concern terminal value can use a stable-growth perpetuity, and its cash flow, discount rate, and growth assumptions must be mutually consistent. | S2: Stable-growth terminal value and consistency constraints | Does not make any chosen terminal growth rate empirically correct. |
| Implementation choice | Per-share multi-stage dividend discount model with an explicit dividend vector, constant cost of equity, and Gordon terminal value after the final explicit period. | Frozen family definition contract, code, fixtures, and parity checks | This is the repository convention, not the only accepted valuation variant. |
| Synthetic teaching input | Every financial amount, rate, date, and scenario state is repository-authored. | datasets/canonical-input.json and datasets/scenario-results.json | No value represents an observed issuer or provider record. |
| Author-derived calculation | The fixture produces present value of explicit dividends, present value of the terminal payout stream, total value per share, and terminal-value share. | Expected output, examples, and independent validation | Arithmetic correctness does not validate forecasts, discount rates, or market performance. |
The authoritative sources support only the exact facts named in the claim ledger. They do not certify the synthetic numbers in this tutorial. The repository fixture is deliberately invented for auditability, and the displayed output is author-derived under the selected implementation choice.
Formula, symbols, and numerical policy
V0 = sum(D_t/(1+k_e)^t) + [D_N(1+g)/(k_e-g)]/(1+k_e)^N
| Symbol | Meaning | Unit | Policy |
|---|---|---|---|
| D_t | Dividend per share in period t | currency/share | Explicit payout input |
| k_e | Cost of equity | decimal/year | Equity discount rate |
| g | Stable dividend growth after N | decimal/year | Must be below k_e |
- Calculate at full floating-point precision and round only for display.
- Periods and rates share one annual cadence unless an explicitly documented conversion is supplied.
- The last explicit dividend is D_N; the terminal numerator is D_(N+1)=D_N(1+g), and terminal_growth must be strictly below cost_of_equity.
- Reconcile all displayed components back to the headline value.
Read the formula in the same order as the algorithm. Validate identity, ordering, units, and supported state first. Apply the selected equality and window rules second. Calculate with unrounded numeric values. Round only at the declared presentation boundary, and preserve null as a diagnostic rather than coercing it to zero.
Build the algorithm
- Confirm per-share dividend timing
- Discount every explicit dividend
- Project D_(N+1)
- Capitalize and discount the terminal stream
- Reconcile explicit and terminal value
Production-minded operational checklist
- Verify point-in-time statement availability and corporate-action/share-count basis.
- Match operating/equity cash flows to WACC/cost of equity.
- Stress discount and perpetual-growth assumptions rather than presenting one point estimate as truth.
- Reconcile independent arithmetic and Python/TypeScript output before publication.
The checklist is intentionally strict: an explicit rejection is safer than a plausible output built from stale, malformed, or unsupported state.
Worked synthetic example
The canonical fixture is synthetic teaching data, not an observed control
event, customer order, or broker execution. Its primary author-derived output,
intrinsic_value_per_share, is present value of explicit dividends, present value of the terminal payout stream, total value per share, and terminal-value share. The complete input and output
are in datasets/canonical-input.json and datasets/expected-output.json.
The canonical synthetic fixture applies V0 = sum(D_t/(1+k_e)^t) + [D_N(1+g)/(k_e-g)]/(1+k_e)^N and produces present value of explicit dividends, present value of the terminal payout stream, total value per share, and terminal-value share. The expected JSON is generated from the frozen contract and checked independently across both language implementations.
Counterfactual checkpoint
Dividend Discount Model material-assumption change. Move the primary discount or growth driver while holding the other frozen inputs constant. The output changes because A valuation is only interpretable when the source of a changed result is visible.
The structured result retains state and diagnostics in addition to the primary number. That makes the calculation independently reviewable and prevents a partial, null, rejected, or venue-bounded outcome from being mistaken for an unqualified value.
Boundary and counterexample workbook
The playground computes every scenario at 61 deterministic parameter states.
The table uses the declared focus step and states whether that focus reproduces
the canonical fixture. The full state ledger and compressed transition
segments are in datasets/scenario-results.json.
| Scenario | Review focus | Purpose | State | Primary output | Diagnostic | Decision segments |
|---|---|---|---|---|---|---|
| Canonical cost-of-equity sweep | Step 30 · canonical fixture | Move cost of equity around the canonical midpoint. | valued | value/share 38.54 | State valued; value/share 38.54; terminal/continuing share 76.8%. | 1 |
| Terminal-growth sweep | Step 30 · comparison focus | Stress stable dividend growth. | valued | value/share 34.06 | State valued; value/share 34.06; terminal/continuing share 73.8%. | 1 |
| Dividend-ramp scale | Step 30 · canonical fixture | Scale every explicit dividend. | valued | value/share 38.54 | State valued; value/share 38.54; terminal/continuing share 76.8%. | 1 |
| Front-loaded payout | Step 30 · comparison focus | Move value toward earlier distributions. | valued | value/share 39.46 | State valued; value/share 39.46; terminal/continuing share 75.0%. | 1 |
| Deferred distributions | Step 30 · comparison focus | Set early dividends near zero while retaining terminal payout. | valued | value/share 34.13 | State valued; value/share 34.13; terminal/continuing share 86.7%. | 1 |
| Zero terminal growth | Step 30 · comparison focus | Hold the post-horizon dividend constant. | valued | value/share 26.01 | State valued; value/share 26.01; terminal/continuing share 65.6%. | 1 |
| Convergence stress | Step 30 · comparison focus | Narrow the cost-growth spread without crossing it. | valued | value/share 80.89 | State valued; value/share 80.89; terminal/continuing share 89.0%. | 1 |
These rows are not backtest observations. They are controlled counterexamples that expose how one driver changes the state, output, or reason code while the rest of the contract stays fixed.
Visualize the boundary
Open this SVG at full size, or use the guided playground to compare the seven topic-specific canonical, boundary, policy, and failure scenarios.
The Mermaid flow answers where the selected calculation sits in the processing sequence. The SVG keeps the formula, output, decision boundary, and invariant visible together. The lab lets the reader step through the same structured states without changing the underlying definition.
Implementation walkthrough
The Python and TypeScript references begin with the same validation contract, reject malformed and unsupported state before calculation, preserve declared ordering and rounding policies, and return structured diagnostics rather than one context-free number.
The main implementation branches are:
- Input, timing, accounting, or claim contract fails — Reject, because A precise number would be misleading.
- Perpetual growth is greater than or equal to the matching discount rate — Reject, because The frozen stable-growth perpetuity is undefined or nonconvergent.
- Inputs are valid but sensitivity is extreme — Return value with diagnostics, because Fragility is information, not an excuse to hide output.
- All checks pass — Return structured valuation and audit trail, because The definition contract is satisfied.
Neither reference silently fetches data, mutates caller-owned inputs outside the declared engine behavior, guesses hidden state, or substitutes a provider default. Shared JSON fixtures make value, null, state, and reason-code drift visible across languages.
Testing and validation
Definition tests compare every canonical field, reject malformed state, and exercise the material boundary. Family validation recomputes every playground state from the reference function. Independent arithmetic is recorded beside the fixture rather than inferred only from implementation output.
The audit must preserve these invariants:
- Persist valuation date, currency, unit scale, claim level, forecast provenance, discount rate, growth rate, and adjustment basis.
- Return each period's opening balance where relevant, discount factor, undiscounted amount, and present value.
- Expose terminal or continuing value and its share of total value.
- Separate invalid input from a valid but economically fragile estimate.
Passing definition and parity checks proves that the implementation matches the selected contract. It does not prove production performance, universal applicability, or a later market outcome.
Failure modes and misuse
- Observed dividends may understate or overstate distribution capacity when payout policy is constrained or discretionary.
- A terminal payout stream is only as credible as the mature payout and growth assumptions behind it.
Debugging order
When a result looks surprising, inspect the state in this order:
- Confirm identifiers, scope, side, and decision clock.
- Confirm units, ordering, and point-in-time inputs.
- Confirm equality, rounding, null, and reset policies.
- Recalculate the invariant and declared scenario focus before changing code.
Evidence and historical boundary
Historical decision: Not useful for the canonical worked example. The learning obstacle is assumption architecture and reconciliation. A named security would add unverifiable author forecasts and could imply false authority unless every input were reconstructed point in time.
The primary sources are Damodaran DDM, Damodaran terminal value. They support the source roles listed in the research ledger, not a redistributable historical observation, a private participant decision, production conformance certification, execution-quality result, profitability claim, or prediction claim.
Summary and next topic
You can now calculate, reconcile, stress, and explain Dividend Discount Model without hiding its claim, timing, or terminal assumptions. The learning flow is: Free-Cash-Flow DCF → Dividend Discount Model → Gordon Growth Model. Carry the result forward only with its scope, clock, state, and evidence label.
Deep visual atlas
Outcome and pipeline
Timing and continuing-value boundary
Assumption sensitivity
Reconciliation bridge
Model boundaries
Use these visuals together: the pipeline establishes claim order, the timeline prevents off-by-one errors, the matrix exposes fragility, the bridge checks arithmetic, and the boundary card prevents the result from being mistaken for a recommendation.
Rendered from the canonical Mermaid sources linked by this article.
Dividend Discount Model calculation flow
This flow identifies the selected calculation stages and the structured output.
Takeaway: The terminal value begins with the next dividend after the explicit forecast—not the last dividend already counted.
ReferencesPrimary sources and evidence notesExpand the source trail, evidence role, and limitations behind the engineering choices.
Expand the source trail, evidence role, and limitations behind the engineering choices.
S1 — Dividend Discount Models
- Organization or authors: Aswath Damodaran
- Source type: Primary or authoritative technical source
- Publication or effective date: n.d.
- Version: n.d.
- URL or DOI: https://pages.stern.nyu.edu/adamodar/New_Home_Page/lectures/ddm.html
- Accessed: 2026-08-04
- Jurisdiction: International / educational framework
- Supports: Equity value can be expressed as the present value of expected dividends; the Gordon model uses next-period dividend divided by cost of equity less stable growth.
- Limitations: Applicability depends on payout and growth assumptions; the source does not validate this package's synthetic numbers.
S2 — The Dark Side of Valuation: Terminal Value
- Organization or authors: Aswath Damodaran
- Source type: Primary or authoritative technical source
- Publication or effective date: n.d.
- Version: n.d.
- URL or DOI: https://pages.stern.nyu.edu/~adamodar/New_Home_Page/littlebook/terminalvalue.htm
- Accessed: 2026-08-04
- Jurisdiction: International / educational framework
- Supports: A going-concern terminal value can use a stable-growth perpetuity, and its cash flow, discount rate, and growth assumptions must be mutually consistent.
- Limitations: Does not make any chosen terminal growth rate empirically correct.
Evidence boundary
The sources establish the exact rule, interface, protocol, or research context named above. They do not verify the repository-authored synthetic fixture, thresholds, empirical usefulness, execution probability, or profitability. Package-selected choices remain labeled as implementation choices wherever they are used.
Full dependency-light reference implementations in both supported languages.
/** Canonical TypeScript parity implementation for D18-F02 Intrinsic Valuation. */
type Input = Record<string, unknown>;
type Output = Record<string, unknown>;
function numberValue(value: unknown, name: string): number {
if (typeof value !== "number" || !Number.isFinite(value)) throw new Error(`${name} must be a finite number`);
return value;
}
function rate(value: unknown, name: string): number {
const result = numberValue(value, name);
if (result <= 0 || result >= 1) throw new Error(`${name} must be strictly between 0 and 1`);
return result;
}
function growth(value: unknown, name: string, discountRate: number): number {
const result = numberValue(value, name);
if (result <= -1) throw new Error(`${name} must be greater than -1`);
if (result >= discountRate) throw new Error(`${name} must be below the matching discount rate`);
return result;
}
function series(value: unknown, name: string): number[] {
if (!Array.isArray(value) || value.length < 1) throw new Error(`${name} must contain at least 1 value`);
return value.map((item, index) => numberValue(item, `${name}[${index}]`));
}
function sameLength(left: unknown[], right: unknown[], leftName: string, rightName: string): void {
if (left.length !== right.length) throw new Error(`${leftName} and ${rightName} must have the same length`);
}
function discount(value: number, discountRate: number, period: number): [number, number] {
const factor = 1 / ((1 + discountRate) ** period);
return [factor, value * factor];
}
function fcffDcf(data: Input): Output {
const fcff = series(data.fcff, "fcff");
const wacc = rate(data.wacc, "wacc");
const terminalGrowth = growth(data.terminal_growth, "terminal_growth", wacc);
const cash = numberValue(data.cash_and_non_operating_assets, "cash_and_non_operating_assets");
const debt = numberValue(data.debt, "debt");
const preferred = numberValue(data.preferred_equity, "preferred_equity");
const minority = numberValue(data.noncontrolling_interest, "noncontrolling_interest");
const shares = numberValue(data.diluted_shares, "diluted_shares");
if (shares <= 0) throw new Error("diluted_shares must be positive");
let pvExplicit = 0;
const schedule = fcff.map((flow, index) => {
const period = index + 1;
const [discountFactor, presentValue] = discount(flow, wacc, period);
pvExplicit += presentValue;
return { period, fcff: flow, discount_factor: discountFactor, present_value: presentValue };
});
const terminalFcff = fcff.at(-1)! * (1 + terminalGrowth);
const terminalValue = terminalFcff / (wacc - terminalGrowth);
const [terminalDiscountFactor, pvTerminal] = discount(terminalValue, wacc, fcff.length);
const enterpriseValue = pvExplicit + pvTerminal;
const netEquityBridge = cash - debt - preferred - minority;
const equityValue = enterpriseValue + netEquityBridge;
return {
state: "valued", method: "fcff-enterprise-dcf", schedule,
pv_explicit_fcff: pvExplicit, terminal_fcff: terminalFcff, terminal_value: terminalValue,
terminal_discount_factor: terminalDiscountFactor, pv_terminal_value: pvTerminal,
terminal_value_share: enterpriseValue ? pvTerminal / enterpriseValue : 0,
enterprise_value: enterpriseValue, net_equity_bridge: netEquityBridge,
equity_value: equityValue, intrinsic_value_per_share: equityValue / shares,
};
}
function ddm(data: Input): Output {
const dividends = series(data.dividends, "dividends");
if (dividends.some((value) => value < 0)) throw new Error("dividends must be nonnegative");
const cost = rate(data.cost_of_equity, "cost_of_equity");
const terminalGrowth = growth(data.terminal_growth, "terminal_growth", cost);
let pvExplicit = 0;
const schedule = dividends.map((dividend, index) => {
const period = index + 1;
const [discountFactor, presentValue] = discount(dividend, cost, period);
pvExplicit += presentValue;
return { period, dividend, discount_factor: discountFactor, present_value: presentValue };
});
const terminalDividend = dividends.at(-1)! * (1 + terminalGrowth);
const terminalValue = terminalDividend / (cost - terminalGrowth);
const [terminalDiscountFactor, pvTerminal] = discount(terminalValue, cost, dividends.length);
const value = pvExplicit + pvTerminal;
return {
state: "valued", method: "multi-stage-dividend-discount", schedule,
pv_explicit_dividends: pvExplicit, terminal_dividend: terminalDividend,
terminal_value: terminalValue, terminal_discount_factor: terminalDiscountFactor,
pv_terminal_value: pvTerminal, terminal_value_share: value ? pvTerminal / value : 0,
intrinsic_value_per_share: value,
};
}
function gordon(data: Input): Output {
const dividendNext = numberValue(data.dividend_next, "dividend_next");
if (dividendNext < 0) throw new Error("dividend_next must be nonnegative");
const cost = rate(data.cost_of_equity, "cost_of_equity");
const perpetualGrowth = growth(data.perpetual_growth, "perpetual_growth", cost);
const spread = cost - perpetualGrowth;
return {
state: "valued", method: "gordon-growth", dividend_timing: "D1-next-period",
discount_growth_spread: spread, capitalization_multiple: 1 / spread,
intrinsic_value_per_share: dividendNext / spread,
};
}
function residualIncome(data: Input): Output {
const openingBook = numberValue(data.opening_book_value_per_share, "opening_book_value_per_share");
if (openingBook <= 0) throw new Error("opening_book_value_per_share must be positive");
const earnings = series(data.earnings_per_share, "earnings_per_share");
const dividends = series(data.dividends_per_share, "dividends_per_share");
const adjustments = series(data.nonowner_equity_adjustments_per_share, "nonowner_equity_adjustments_per_share");
sameLength(earnings, dividends, "earnings_per_share", "dividends_per_share");
sameLength(earnings, adjustments, "earnings_per_share", "nonowner_equity_adjustments_per_share");
const cost = rate(data.cost_of_equity, "cost_of_equity");
const terminalGrowth = growth(data.terminal_residual_income_growth, "terminal_residual_income_growth", cost);
let book = openingBook;
let pvExplicit = 0;
const residualValues: number[] = [];
const schedule = earnings.map((eps, index) => {
const period = index + 1;
const dividend = dividends[index];
const adjustment = adjustments[index];
if (dividend < 0) throw new Error("dividends_per_share must be nonnegative");
const opening = book;
const equityCharge = cost * opening;
const residual = eps - equityCharge;
const [discountFactor, presentValue] = discount(residual, cost, period);
const closing = opening + eps - dividend + adjustment;
if (closing <= 0) throw new Error("clean-surplus book-value roll-forward must remain positive");
pvExplicit += presentValue;
residualValues.push(residual);
book = closing;
return {
period, opening_book_value_per_share: opening, earnings_per_share: eps,
dividends_per_share: dividend, nonowner_equity_adjustment_per_share: adjustment,
equity_charge_per_share: equityCharge, residual_income_per_share: residual,
discount_factor: discountFactor, present_value: presentValue,
closing_book_value_per_share: closing,
};
});
const terminalResidual = residualValues.at(-1)! * (1 + terminalGrowth);
const continuingValue = terminalResidual / (cost - terminalGrowth);
const [terminalDiscountFactor, pvContinuing] = discount(continuingValue, cost, earnings.length);
const value = openingBook + pvExplicit + pvContinuing;
return {
state: "valued", method: "residual-income", schedule,
pv_explicit_residual_income: pvExplicit, terminal_residual_income_per_share: terminalResidual,
continuing_value: continuingValue, terminal_discount_factor: terminalDiscountFactor,
pv_continuing_value: pvContinuing, continuing_value_share: value ? pvContinuing / value : 0,
closing_book_value_per_share: book, intrinsic_value_per_share: value,
};
}
function eva(data: Input): Output {
const openingCapital = numberValue(data.opening_invested_capital, "opening_invested_capital");
if (openingCapital <= 0) throw new Error("opening_invested_capital must be positive");
const nopat = series(data.nopat, "nopat");
const investment = series(data.net_investment, "net_investment");
sameLength(nopat, investment, "nopat", "net_investment");
const wacc = rate(data.wacc, "wacc");
const terminalGrowth = growth(data.terminal_eva_growth, "terminal_eva_growth", wacc);
let capital = openingCapital;
let pvExplicit = 0;
const evaValues: number[] = [];
const schedule = nopat.map((periodNopat, index) => {
const period = index + 1;
const opening = capital;
const capitalCharge = wacc * opening;
const periodEva = periodNopat - capitalCharge;
const roic = periodNopat / opening;
const [discountFactor, presentValue] = discount(periodEva, wacc, period);
const closing = opening + investment[index];
if (closing <= 0) throw new Error("invested-capital roll-forward must remain positive");
pvExplicit += presentValue;
evaValues.push(periodEva);
capital = closing;
return {
period, opening_invested_capital: opening, nopat: periodNopat,
net_investment: investment[index], capital_charge: capitalCharge, roic,
roic_wacc_spread: roic - wacc, eva: periodEva,
discount_factor: discountFactor, present_value: presentValue,
closing_invested_capital: closing,
};
});
const terminalEva = evaValues.at(-1)! * (1 + terminalGrowth);
const continuingValue = terminalEva / (wacc - terminalGrowth);
const [terminalDiscountFactor, pvContinuing] = discount(continuingValue, wacc, nopat.length);
const marketValueAdded = pvExplicit + pvContinuing;
const enterpriseValue = openingCapital + marketValueAdded;
return {
state: "valued", method: "economic-value-added", schedule,
pv_explicit_eva: pvExplicit, terminal_eva: terminalEva, continuing_value: continuingValue,
terminal_discount_factor: terminalDiscountFactor, pv_continuing_value: pvContinuing,
market_value_added: marketValueAdded,
continuing_value_share: enterpriseValue ? pvContinuing / enterpriseValue : 0,
closing_invested_capital: capital, enterprise_value: enterpriseValue,
};
}
const calculators: Record<string, (data: Input) => Output> = {
"D18-F02-A01": fcffDcf,
"D18-F02-A02": ddm,
"D18-F02-A03": gordon,
"D18-F02-A04": residualIncome,
"D18-F02-A05": eva,
};
export function calculate(topicId: string, data: Input): Output {
const calculator = calculators[topicId];
if (!calculator) throw new Error(`unsupported topic_id: ${topicId}`);
if (!data || Array.isArray(data) || typeof data !== "object") throw new Error("data must be an object");
return calculator(data);
}
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