What Is Sensitivity Analysis?
Sensitivity analysis tests how much a financial model's answer moves when you change its most important assumptions. Rather than presenting a single point estimate, the analyst shows a grid of outcomes across plausible ranges of inputs, acknowledging that every projection is uncertain.
In valuation work, the classic output is a two-way table with WACC on one axis and the terminal growth rate or exit multiple on the other, with each cell showing the implied share price or enterprise value. In LBO models, the equivalent table shows IRR across entry and exit multiples or leverage levels.
How It Works
The analyst identifies the one or two inputs with the biggest impact on the output, defines a realistic range for each, and recalculates the model at every combination, typically using Excel's data table function. The result reveals both the valuation range and how steep the gradient is, meaning how fragile the conclusion is to small assumption changes.
Sensitivity analysis varies one or two inputs at a time, which distinguishes it from scenario analysis, where several assumptions are changed together to reflect a coherent story like a recession case. Good models include both, and interviewers sometimes probe whether candidates know the difference.
Example
Suppose a DCF values a stock at $50 per share using a 10% WACC and a 3% terminal growth rate. Rerunning the model shows the value falls to $43 at an 11% WACC and rises to $59 at a 9% WACC, while moving terminal growth between 2% and 4% swings the value from $45 to $57.
The full two-way table therefore brackets the valuation roughly between $40 and $62, and the banker would present that range rather than the single $50 estimate. A one-percentage-point change in WACC moving the value about 15% is a vivid reminder of how assumption-driven DCF outputs are.
Why It Matters
Sensitivity tables turn a false-precision point estimate into an honest range, which is how valuations are actually presented to boards, investment committees, and clients. They also focus diligence by revealing which assumptions matter most, so teams spend their time pressure-testing the inputs that actually move the answer.
On the job, junior bankers build these tables constantly, and in interviews being able to say which two variables you would sensitize in a DCF, namely the discount rate and the terminal value assumption, signals practical modeling awareness.
