What Is Sensitivity Analysis?

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What Is Sensitivity Analysis?

Sensitivity analysis shows how different values of an independent variable affect a dependent variable under a given set of assumptions. Companies use sensitivity analysis to identify opportunities, mitigate risk, and communicate decisions to upper management.

Sensitivity analysis is deployed in business and economics by financial analysts and economists and is also known as a "what-if" analysis.

Key Takeaways

• Sensitivity analysis shows how different values of an independent variable affect a dependent variable under a given set of assumptions.
• This model is called "what-if" or simulation analysis.
• Sensitivity analysis helps predict share prices of publicly traded companies or how interest rates affect bond prices.

How It Works

Sensitivity analysis is a financial model that determines how target variables are affected based on changes in input variables. By creating a given set of variables, an analyst can determine how changes in one variable affect the outcome.

The independent and dependent variables are fully analyzed when sensitivity analysis is conducted. Sensitivity analysis allows for forecasting using historical, true data. By studying all the variables and the possible outcomes, important decisions can be made about businesses, the economy, and making investments. Sensitivity analysis can be used to:

• Make predictions about the share prices of public companies. Some variables that affect stock prices include company earnings, the number of shares outstanding, the debt-to-equity ratios (D/E), and the number of competitors in the industry.
• Determine the effect that changes in interest rates have on bond prices. In this case, the interest rates are the independent variable, while bond prices are the dependent variable.

How Businesses Use Sensitivity Analysis

Sensitivity analysis can provide management feedback useful in many different scenarios including:

• Understand influencing factors. What and how external factors interact with a specific project or undertaking. This allows management to see what input variables may impact output variables.
• Reduce uncertainty. Complex sensitivity analysis models inform project members what to be alert for or what to plan for.
• Catch errors. The original assumptions for the baseline analysis may have had some uncaught errors. By performing analytical iterations, management may catch mistakes in the original analysis.
• Simplify the model. By performing sensitivity analysis, users can better understand what factors don't matter and can be removed from the model due to its lack of materiality.
• Communicate results. Upper management may already be defensive. Compiling analysis on different situations helps inform decision-makers of other outcomes they may be interested in knowing about.
• Achieve goals. Management may lay long-term strategic plans that must meet specific benchmarks. By performing sensitivity analysis, a company can better understand how a project may change and what conditions must be present for the team to meet its metric targets.
  

Because sensitivity analysis answers questions such as "What if XYZ happens?", this type of analysis is also called what-if analysis.

Example

A sales manager wants to understand the impact of customer traffic on total sales. The company determines that sales are a function of price and transaction volume. The price of a widget is $1,000, and the company sold 100 last year for a total sales of $100,000.

The manager determines that a 10% increase in customer traffic increases transaction volume by 5%. This allows the company to build a financial model and sensitivity analysis based on what-if statements. It can tell the manager what happens to sales if customer traffic increases by 10%, 50%, or 100%.

Based on 100 transactions, a 10%, 50%, or 100% increase in customer traffic equates to an increase in transactions by 5%, 25%, or 50% respectively. The sensitivity analysis demonstrates that sales are sensitive to changes in customer traffic.

Advantages and Disadvantages

Sensitivity analysis provides several benefits for decision-makers. It acts as an in-depth study of all the variables so the predictions may be more reliable. It allows decision-makers to identify where they can make improvements in the future.

However, the outcomes are based on assumptions because the variables are based on historical data only. Complex models may be system-intensive, and models with too many variables may distort a user's ability to analyze influential variables.

• May help management target specific inputs to achieve more specific results
• May easily communicate areas to focus on or greatest risks to control
• May identify mistakes in the original benchmark
• Generally reduces the uncertainty and unpredictability of a given undertaking

• Heavily relies on assumptions that may not become true in the future
• May burden computer systems with complex, intensive models
• May become overly complicated which distorts an analysts ability to
• May not accurately integrate independent variables as one variable may not accurately the impact of another variable

What Is Sensitivity Analysis in NPV?

Sensitivity analysis in NPV analysis is a technique to evaluate how the profitability of a specific project will change based on changes to underlying input variables. Though a company may have calculated the Net Present Value (NPV), it may want to understand how better or worse conditions will impact the return the company receives.

How Do Businesses Calculate Sensitivity Analysis?

Sensitivity analysis is often performed in analysis software, and Excel has functions to perform the analysis. In general, sensitivity analysis is calculated using formulas with different input cells. For example, a company may perform NPV analysis using a discount rate of 6%. Sensitivity analysis can be performed by analyzing scenarios of 5%, 8%, and 10% discount rates and maintaining the formula but referencing the different variable values.

What Is the Difference Between Sensitivity Analysis and Scenario Analysis?

A sensitivity analysis is not the same as a scenario analysis. Assume an equity analyst wants to do a sensitivity analysis and a scenario analysis around the impact of earnings per share (EPS) on a company's relative valuation by using the price-to-earnings (P/E) multiple. The sensitivity analysis is based on the variables that affect valuation, which a financial model can depict using the variables' price and EPS. For a scenario analysis, an analyst determines a certain scenario such as a stock market crash or change in industry regulation that would affect the company valuation.

The Bottom Line

When a company wants to determine different potential outcomes for a given project, it may consider performing a sensitivity analysis. Sensitivity analysis entails manipulating independent variables to see the resulting financial impacts. Companies employ it to identify opportunities, mitigate risk, and communicate decisions to upper management.

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