Sensitivity Analysis

Sensitivity Analysis

Sensitivity analysis evaluates how changes in one or more independent variables affect a dependent outcome under a fixed set of assumptions. Often called “what‑if” analysis, it helps organizations anticipate how different inputs—price, volume, interest rates, costs, etc.—alter financial results or project viability.

How it works

• Build a baseline model that links inputs (independent variables) to outputs (dependent variables).
• Vary one input at a time (or combinations) across a plausible range while holding others constant.
• Record how the output changes to identify which inputs have the largest effect.
• Use results to inform decisions, prioritize monitoring, and refine assumptions.

Common applications:
– Forecasting stock price sensitivity to earnings, shares outstanding, or leverage.
– Measuring bond price sensitivity to interest rate changes.
– Assessing project returns, cash flow variability, or break‑even thresholds.

Business uses

Sensitivity analysis supports decision‑making by enabling management to:
– Identify the most influential drivers of outcomes.
– Reduce uncertainty and prepare contingencies.
– Detect errors or unrealistic assumptions in baseline models.
– Simplify models by removing insignificant variables.
– Communicate a range of possible outcomes to stakeholders.
– Set conditions required to meet strategic targets.

Example

A company sells a widget at $1,000 and sold 100 units last year (sales = $100,000). The manager estimates that a 10% increase in customer traffic raises transactions by 5%. Under sensitivity analysis:
– +10% traffic → transactions +5% → sales = $105,000
– +50% traffic → transactions +25% → sales = $125,000
– +100% traffic → transactions +50% → sales = $150,000

This shows sales sensitivity to customer traffic and helps prioritize marketing or capacity changes.

Advantages and disadvantages

Pros:
– Highlights which inputs most influence outcomes.
– Clarifies risk exposure and areas to monitor or control.
– Can reveal mistakes in initial assumptions.
– Helps reduce uncertainty by mapping possible outcomes.

Cons:
– Relies on historical data and assumptions; results are estimates, not certainties.
– Models can become overly complex when many variables are included.
– Interactions among many inputs may produce misleading or unstable results if not modeled carefully.

Sensitivity analysis in NPV

Applied to net present value (NPV), sensitivity analysis shows how project profitability changes when key inputs vary (discount rate, cash flows, growth rates). For example, altering the discount rate from 6% to 5%, 8%, and 10% while keeping cash flows constant reveals how sensitive NPV is to the chosen rate.

How to calculate

• Identify key input variables and a baseline scenario.
• Choose the range and increments for each input.
• Recompute the model for each input variation.
• Tools commonly used: spreadsheet functions (Excel data tables, goal seek), dedicated financial or simulation software, and visualization aids (tornado charts).
• Analyze results to prioritize inputs and determine trigger points for action.

Sensitivity analysis vs. scenario analysis

• Sensitivity analysis: Varies individual inputs (or small sets) to measure their isolated impact on the outcome.
• Scenario analysis: Builds coherent scenarios (e.g., recession, regulatory change) that change multiple inputs simultaneously to depict plausible future states.

Key takeaways

Sensitivity analysis is a practical method for exploring how changes in inputs affect outcomes. It informs risk management, decision-making, and planning by revealing critical drivers and a range of possible results, but its conclusions depend on the quality of the model and assumptions used.