Scenario and Sensitivity Analysis

Test the decision under alternative drivers rather than changing formulas manually.

D3 · Financial Modeling

Scenario and Sensitivity Analysis

Test the decision under alternative drivers rather than changing formulas manually.

Architecture Inputs Formula logic Worked example Controls Common errors Q&A

Model architecture

Define base, upside and downside cases.
Centralise scenario switches.
Create one- and two-variable sensitivities.
Present decision thresholds.

A professional model should make the decision logic visible. Inputs belong in a controlled assumption area; calculations should be formula-driven; outputs should state units, dates and scenarios; checks should be obvious and actionable.

Core inputs

Growth
Margin
Working capital
WACC/exit multiple
Financing and timing

Evidence rule: historical figures should reconcile to approved financial statements or management data. Forecast assumptions should show an owner, date, source and rationale.

Formula logic

Relationship	Use
`Scenario output = Selected case assumptions through a controlled switch`	Model formula / relationship
`Break-even driver = Input at which NPV or cash headroom reaches zero`	Model formula / relationship

Use the formulas as design relationships, not as substitutes for the accounting policy, contract definition or transaction facts relevant to the model.

Practical example

A DCF sensitivity table shows value across WACC of 9%-13% and perpetual growth of 3%-6%, highlighting where value becomes unstable.

How to implement

Load the historical base and reconcile it.
Put assumptions in dedicated cells.
Build the schedule from operational drivers.
Link outputs to financial statements and dashboards.
Run base, upside and downside checks.

Control checks

Cases are mutually consistent
Only assumptions change, not formulas
Downside includes correlated shocks
Outputs show units and dates
Probability-weighting is disclosed

Finin2min crux: the model is credible only when a reviewer can trace a conclusion to evidence, assumptions and formula logic without guessing.

Common modeling errors

Changing several cells manually without recording the case
Using unrealistic combinations
Calling a sensitivity a forecast
Hiding downside results
Applying probabilities without evidence

Practical Q&A

Should the model contain all possible detail?

No. It should contain enough detail to answer the decision question and explain material risks. Excess detail can hide the drivers.

Should a formula ever contain a hardcoded number?

Only for constants that are genuinely universal or immaterial. Business assumptions should be linked to visible input cells.

What is the minimum review standard?

Reconcile historical data, test key formulas independently, scan for hardcodes and errors, verify scenario switches, and review outputs under downside assumptions.

Source framework: ICAI Ind AS resources, notified accounting standards, Schedule III presentation principles, transaction documents and approved management data. The linked workbook templates are educational starting points, not valuation opinions.