The person and the choice
Jordan Lee's decision starts before any input is entered
Jordan Lee, a senior software engineer joining a late-stage private company, is the fictional decision-maker in this worked example. Jordan receives RSUs and options with different vesting schedules, must fund any exercise personally, and cannot assume that vested awards can be sold on demand. The goal is to turn that situation into a traceable case without pretending the assumptions describe a future that is certain.
The choice is not simply between the largest and smallest displayed values. It is whether the modeled result gives Jordan Lee enough evidence to act, which number affects current cash, which number remains conditional, and which unanswered term could reverse the interpretation.
The profile is deliberately realistic rather than universal. Readers should borrow the method—document facts, normalize units, run the engine, reconcile outputs, vary one assumption, and record omitted risks—not copy Jordan Lee's inputs into a different situation.
What is known
Build the evidence ledger before building the scenario
Grant type, quantity, strike price, vesting schedule, expiration, settlement terms, company type, current reference value, and known liquidity events belong in the factual ledger; future value and tax rates remain assumptions. Those items anchor the base case. They receive the label “input” only when a document, record, or current policy supports them; otherwise they remain an assumption even if the value feels likely.
Jordan should reconcile the offer letter, board-approved grant notice, plan document, vesting schedule, strike price, expiration and post-termination window, exercise policy, settlement terms, liquidity rights, tax notices, and capitalization disclosures. For the worked example, the visible input table highlights the fields that explain the result while the typed fixture supplies the calculator's complete validated object.
That distinction prevents a common reporting problem: showing six attractive inputs while hidden defaults do most of the calculation. The fixture is tested against the schema, and the displayed lead metric is recalculated by the production engine. In this an equity compensation grant analysis, that boundary is applied to Jordan Lee's stated facts and assumptions.
| Field | Value | Role |
|---|---|---|
| Company type | private | Input |
| Valuation date | 2025-01-15 | Input |
| Projection horizon | 5 years | Input |
| Included grants | 3 | Input |
| Taxes | Modeled | Assumption |
| Dilution | Modeled | Assumption |
Preparing the inputs
What Jordan Lee has to normalize before pressing calculate
Every grant must be converted into dated vesting events and then mapped to availability, exercise, settlement, and sale rules. RSUs, restricted stock, ISOs, and NSOs cannot be compared as if one unit created identical rights or cash flow. The normalization step creates a common clock and compatible units, but it does not erase restrictions or uncertainty. Cash remains cash, time remains time, and conditional value remains labeled.
In this case, the three operational layers are grant rights and dated vesting, exercise, settlement, and tax cash, and dilution, liquidity, and paper value. Each is prepared separately so the engine can connect them in the right order.
A useful preflight check is to ask whether every percentage has a defined base, every annual amount has a recurrence rule, every date belongs to the correct period, and every scenario value has an owner. Ambiguous units are resolved before calculation, not explained away afterward. In this an equity compensation grant analysis, that boundary is applied to Jordan Lee's stated facts and assumptions.
Jordan Lee's result is reproducible because the fixture, engine path, and assumptions remain separate.
Why these assumptions
Choose a base case that can be defended, not one that flatters the outcome
The reference price, liquidity timing, dilution, tax inclusion, vesting date, exercise policy, expiration, and sale percentage can dominate the result, especially when option strike cost is large relative to household liquidity. The base fixture selects explicit values for those variables so the result can be reproduced. It does not claim they are the most likely values for every reader.
For Jordan Lee, the strongest assumption is the one closest to a written term or recent observed pattern. The weakest is a future outcome controlled by a market, employer, client, schedule, or household event. Those two should never carry the same confidence label.
A conservative case should stress one credible downside without changing unrelated facts. A favorable case should do the same in the other direction. This structure shows which variable causes the spread instead of producing two opaque bundles. In this an equity compensation grant analysis, that boundary is applied to Jordan Lee's stated facts and assumptions.
Calculation walkthrough
Follow one case through the actual PayArith engine
The engine creates grant-level event timelines, tracks vested and available units, applies option strike cost, optional dilution and tax assumptions, constrains sales to modeled liquidity, and separates net cash flow from ending paper value. The audit table below is derived from that engine result. The article does not reimplement the formulas, which prevents prose examples from drifting away from the calculator's validation, ordering, and rounding behavior.
Gross shares times a price is only a scenario value. Subtracting strike cost, estimated taxes, and dilution while respecting vesting and liquidity reveals why economic value, cash proceeds, and cash required must remain separate metrics. The formula block names the central relationship, while the step rows reconcile how the fixture reaches its displayed output. Each calculated value is labeled separately from the assumption that feeds it.
To audit the calculation, start with the first row and ask where its basis came from. Then carry the output into the next relevant stage. If a value cannot be traced, it should not be used as the reason for Jordan Lee's choice.
| Step | Basis | Calculated result |
|---|---|---|
| Year 1 | 23,951 vesting; 0 sold | $0 cash; $0 paper |
| Year 2 | 14,678 vesting; 0 sold | $0 cash; $0 paper |
| Year 3 | 13,740 vesting; 55,000 sold | $580,041 cash; $0 paper |
| Year 4 | 2,313 vesting; 0 sold | -$9,556 cash; $25,826 paper |
| Year 5 | 318 vesting; 0 sold | -$1,800 cash; $30,692 paper |
What the outputs mean
Translate every result back into the decision
Available shares measure executable or settleable units; vested shares measure service-earned units; gross scenario value is pre-cost exposure; net cash flow records money entering or leaving; ending paper value records unsold modeled value. For Jordan Lee, those are not interchangeable scorecards. The metric that best describes long-term modeled value may be the wrong metric for a near-term cash constraint.
The headline strip is a navigation aid, not the whole analysis. The audit explains composition, the scenario chart explains conditional range, and the projection explains timing. A decision should cite the specific view that supports it. In this an equity compensation grant analysis, that boundary is applied to Jordan Lee's stated facts and assumptions.
The text alternative under each chart repeats the plotted values in a table. That supports readers who cannot use the visual and also makes the numerical comparison easier to reconcile against the engine audit. In this an equity compensation grant analysis, that boundary is applied to Jordan Lee's stated facts and assumptions.
The next view keeps the fixture constant and exposes the numerical spread. Read it to locate a decision boundary, then use the table to reconcile the plotted values without relying on color or shape.
| Scenario | Calculated value | Reference value |
|---|---|---|
| conservative | 240,000 | 163,200 |
| expected | 751,500 | 527,760 |
| upside | 1,642,500 | 1,162,800 |
The chart does not rank personal outcomes. It shows how the defined engine metrics move; the surrounding article explains whether the spread is liquid, recurring, sensitive, or incomplete.
The counterfactual
Change one condition and explain why the answer moves
A higher price increases option spread but can also increase exercise-related tax exposure; a delayed liquidity event gives more time for vesting but extends the period in which value remains inaccessible. That alternative changes the relevant engine inputs while leaving the rest of the case intact. The resulting difference is therefore attributable to a named condition rather than a collection of favorable edits.
If the ranking changes, Jordan Lee has found a decision boundary. The next task is to verify how plausible that condition is and whether the household can tolerate being wrong, not to average the cases into a false point estimate.
If the ranking does not change, inspect the size and timing of the remaining lead. A numerically stable result can still be impractical when its value is illiquid, delayed, reversible, or dependent on staying in the role. In this an equity compensation grant analysis, that boundary is applied to Jordan Lee's stated facts and assumptions.
What matters now
Separate current cash, recurring economics, and later value
Exercising moves cash out before sale proceeds exist and may create tax obligations; waiting protects liquidity today but leaves the employee exposed to expiration, termination windows, price changes, and future financing constraints. This is the part of the example most likely to affect an immediate action. A household cannot pay a current obligation with a future scenario value, even when both appear in the same long-term comparison.
An early exercise may start a holding period and capture more future appreciation above a lower basis, while waiting can buy information. Neither advantage matters if the company never provides realizable liquidity. The projection makes that sequence visible but does not predict persistence. It repeats the stated growth, schedule, vesting, cost, or availability assumptions across the chosen horizon.
A good decision memo records three numbers: the Year 1 cash consequence, the recurring annual difference after one-time effects, and the cumulative result at a horizon the person may realistically remain. That memo is more informative than one lifetime total. In this an equity compensation grant analysis, that boundary is applied to Jordan Lee's stated facts and assumptions.
A single-year lead can disappear, widen, or reverse. The projection uses the same stated horizon so timing remains visible rather than being compressed into one lifetime total.
| Period | Primary path | Comparison path |
|---|---|---|
| Year 1 | 475,049.7 | 0 |
| Year 2 | 250,476.3 | 0 |
| Year 3 | 620,295.3 | 0 |
| Year 4 | 601,177 | 25,826.4 |
| Year 5 | 599,376.8 | 30,691.8 |
Use the projection to ask when value appears and which assumption repeats. Do not treat the final point as more certain merely because it is farther to the right.
Outside the output
The engine can be right while the decision is still exposed
Company value, dilution, exit timing, tax treatment, continued employment, future exercise policy, and secondary-market access are uncertain. A scenario can test those variables without converting them into expected facts. The worked example isolates those uncertainties rather than hiding them inside a single expected label.
The central risk is cash paid for an illiquid claim. A favorable valuation path can coexist with no sale access, while termination can compress an exercise decision into a short window. That risk is not an arithmetic defect; it is information outside or beyond the model. It belongs beside the result because it affects how much confidence Jordan Lee should place in the numerical lead.
Risk tolerance, family liquidity, belief in the company, access to financial advice, tolerance for administrative complexity, and the emotional cost of a large irreversible check can outweigh a modeled value spread. None of those factors should be converted into invented dollars merely to force one total. A separate qualitative ledger keeps them explicit and allows Jordan Lee to choose a financially lower path for a stated reason.
Decision takeaway
What Jordan Lee can responsibly conclude from this worked case
This fixture proves how a validated set of an equity compensation grant inputs travels through PayArith's production calculation engine. It proves the arithmetic relationship and the displayed reconciliation; it does not prove that future assumptions will occur.
Before acting, Jordan Lee should ask: What are the exact vesting, settlement, exercise, expiration, post-termination, transfer, repurchase, tax-withholding, dilution, and liquidity terms for each grant? The answer should update a named input or document an unsupported risk. Either outcome improves the decision more than adding another generic scenario.
The practical takeaway is to choose from the range that the household can fund and tolerate, using the metric tied to the actual objective. The highest modeled value is relevant only after its timing, availability, fragility, and nonfinancial cost are acceptable. In this an equity compensation grant analysis, that boundary is applied to Jordan Lee's stated facts and assumptions.