Sensitivity Disclosure explained: this guide covers what it means, who it applies to, the step-by-step process, documents required, fees, due dates and penalties in India — so you can stay compliant with confidence and avoid costly mistakes.
A sensitivity analysis performed as a compliance exercise can be worse than none at all — it creates an impression of robustness the underlying work does not support.
The disclosure framework
| Assumption | Base case | Tested range | Value impact |
|---|---|---|---|
| WACC | 12.0% | ± 1.0% | ₹X crore to ₹Y crore |
| Revenue growth | 8.0% | ± 2.0% | ₹X crore to ₹Y crore |
| EBITDA margin | 15.0% | ± 2.0% | ₹X crore to ₹Y crore |
| Terminal growth rate | 5.0% | ± 0.5% | ₹X crore to ₹Y crore |
Four columns, and the fourth is the one that carries the information. Where the conclusion is particularly sensitive to a specific assumption, this should be explicitly highlighted rather than left for the reader to spot in the table.
The seven failures
| Failure | Defensibility risk |
|---|---|
| Testing immaterial assumptions while ignoring key value drivers | Creates a misleading impression of robustness |
| Using unrealistically narrow testing ranges | Understates valuation uncertainty |
| Using extreme or unrealistic testing ranges | Produces results with limited practical relevance |
| Presenting changes in assumptions without showing value impact | Prevents stakeholders from assessing materiality |
| Failing to explain the basis of test ranges selected | Weakens the credibility of the analysis |
| Omitting sensitivity analysis where the valuation is highly assumption-dependent | Creates vulnerability during review or challenge |
| Disclosing only favourable sensitivities | Creates an impression of bias or selective reporting |
Five of the seven are technical shortcomings. Two are different in character, because both make the report actively misleading rather than merely unhelpful.
Testing immaterial assumptions creates a misleading impression of robustness — the report appears to have been stress-tested, and a reader who does not check which inputs were varied will believe the conclusion is stable. It is the sensitivity equivalent of a boilerplate assumption: the form of analysis without its substance.
Disclosing only favourable sensitivities goes further, creating an impression of bias or selective reporting. In a CIRP, where the same report is read by creditors with directly opposed interests, a one-sided sensitivity table is the fastest available route to a challenge on the valuer's independence rather than on the analysis.
The good-practice list for presentation
- Focus sensitivity disclosure on material value drivers.
- Present results as the impact on value, not the change in the assumption.
- Clearly disclose the assumptions tested and the ranges applied.
- Specifically highlight assumptions exhibiting high sensitivity.
- Link the analysis to the assumptions and methodologies discussed elsewhere in the report.
The last of these is what stops the analysis reading as a bolted-on annexure. A sensitivity on the discount rate should point back to the WACC derivation; a sensitivity on asset haircuts should point back to the evidence behind them.
The practical test
Ask whether the analysis answers the question most likely to be raised by a reviewer, creditor, tribunal or opposing expert:
"How would the valuation conclusion change if a key assumption proves incorrect?"
If it does not adequately address that question, further refinement may be necessary. Sensitivity analysis should not be treated as a compliance exercise — the assumptions selected, the ranges applied and the presentation should all be capable of independent justification.
The toolkits
Annexure D provides an illustrative Sensitivity and Scenario Testing Framework (D.1) and a Sensitivity Testing Guide (D.2), to assist in identifying key value drivers, performing the analysis, evaluating uncertainty and documenting the results defensibly.
Common mistakes
- Reporting a sensitivity disclosure with no value column.
- Selecting a range and never explaining why that range.
- Showing the downside on one assumption and the upside on another.
- Placing the analysis in an annexure disconnected from the assumptions it tests.