Sensitivity analysis is an integral part of financial models, as it allows the modeler to understand how changes in assumptions affect the results of a model. It looks at the impact of varying individual assumptions or inputs on the overall outcome.

But how do you do sensitivity analysis? Which situations can you apply it to? We provide the answers in this guide.

What Is Sensitivity Analysis?

Sensitivity analysis refers to the process of analyzing the impact on a model’s output when one or more of its parameters are changed. It is used to identify which variables have the greatest influence on a model’s output and to determine how much of an effect a minor change in a parameter will have.

Some parameters that you can change in a sensitivity analysis include:

• Amount of capital
• Interest rate
• Cost of materials
• Tax rates
• Discount rate
• Inflation rates
• Production levels

When you conduct a sensitivity analysis, the goal is to determine which variables have the greatest impact on a model’s output. You can use this information to adjust your parameters and optimize your model. Such an analysis also helps identify areas of risk in your business and any opportunities for improvement.

Methods of Sensitivity Analysis

The most common method of sensitivity analysis is the derivative-based approach. In this method, the derivatives of a function with respect to each variable are calculated and analyzed.

It gives an indication of how much the output will change when a small amount of change is made in the input. For example, if the derivative of a function with respect to cost is positive, it means that when the cost increases, the output will also increase.

Besides this, you can use two approaches in sensitivity analysis:

• Switching values
• Scenario analysis

The former is the most straightforward approach, which involves changing one variable at a time while keeping the others constant. It lets you analyze how the output changes with each variable.

The latter approach is more useful when dealing with complex systems. In scenario analysis, various scenarios are simulated by changing multiple variables simultaneously.

Suppose four scenarios are simulated with different combinations of input variables. Then the outputs of the four scenarios can be compared to see which scenario has the most desirable output.

Finally, a Monte Carlo simulation can also be used in a sensitivity analysis. In this method, a set of random numbers is generated and used as input to the function. Then the average output is calculated to show how the output changes with random input values.

How to Use Sensitivity Analysis in Financial Modeling

When you create a financial model, you should be able to forecast the future based on your assumptions and be aware of how sensitive your results are to a change in the assumptions. This is why sensitivity analysis plays such an important role in financial modeling.

Here’s how to use it in financial modeling.

Step 1: Define the Base Case

Before you can begin creating a sensitivity analysis, the first step is to define what your base case is. This will be your starting point and will serve as the basis for all the other assumptions you make.

When creating your base case, include all of the critical assumptions, such as:

• Interest rates
• Inflation
• Revenue growth rate
• Costs and expenses

For example, your base interest rate can be set to 5% and the inflation rate to 2%.

Step 2: Identify the Most Important Variables

Once your base case is established, the next step is to identify which variables are most important to your forecast. It will help you decide which variables to focus on in your sensitivity analysis.

When identifying important variables, consider both quantitative and qualitative measures. For example, the revenue growth rate may be a quantitative measure, and customer loyalty or brand recognition could be a qualitative one.

Step 3: Calculate Output Variables

By changing the input variables in your model, you can calculate how the output variables will be affected by each change. This is where sensitivity analysis comes into play.

Divide the percentage change in the output variable by the percentage change in the input to get the sensitivity of the output variable with respect to the input.

For example, if you changed your base interest rate from 5% to 6%, how would it affect the output variable of return on investment? Using the formula above, you can calculate the sensitivity of return on investment with respect to the interest rate.

Step 4: Analyze and Interpret Results

Consider which variables impact your model most, and focus your sensitivity analysis on them. Analyze the results and interpret what they mean for the overall model.

For example, if the output variable is very sensitive to a particular input variable, it may indicate that the model will be significantly affected if that input changes. If the output is not very sensitive to an input, it may suggest that the model is robust and will remain stable even if that input changes.

Step 5: Create Sensitivity Charts

Finally, you can create charts to visualize the sensitivity of your model. The visual representation will make it easy to compare the sensitivity of different variables and see how they affect your model.

Drawbacks of Sensitivity Analysis

While sensitivity analysis can be immensely helpful in creating financial models, it lacks in a few areas. Here are some of them.

• Inability to Capture Interdependencies: Sensitivity analysis fails to capture potential interactions and correlations between variables, which may influence the overall outcome. For example, a change in interest rate may impact unemployment and GDP, but this correlation is not captured in a sensitivity analysis. 
• Unrealistic Assumptions: While constructing a model, sensitivity analysis assumes that only one factor is changing while the other factors remain constant. This rarely happens in the real world, where multiple factors are affected simultaneously.
• Time-Consuming: Creating a sensitivity analysis model can be time-consuming, especially when multiple variables are included in the model. Proper planning and analysis are required to set up a meaningful sensitivity analysis.
• Inaccurate Results: Without a proper understanding of the variables and their relationship to each other, the results from sensitivity analysis may not be reliable.

Additionally, sensitivity analysis does not consider external economic factors, such as changes in government policies or business cycles that can influence the outcome.

Perfect Your Financial Models With Sensitivity Analysis

Are you struggling to create a financial model to attract potential investors? Numberly is just a call away. We’ll create a comprehensive and easy-to-understand financial model that compares different scenarios, giving potential investors a complete understanding of your business. Schedule a 30-minute call with us for free.