We raised a $20m Series A led by Coatue + Accel! Click here to read the announcement.

Financial modelling terms explained

A sensitivity analysis is a financial analysis used to evaluate the impacts of different assumptions and possible outcomes on a company's earnings. It allows a company to determine which factors are more important and to better understand future outlooks.

Sensitivity analysis is a technique used to determine how sensitive the results of a financial model are to changes in the assumptions used to generate those results. It can be used to identify which assumptions are most important to the overall results, and to help determine how much uncertainty is associated with those results. Sensitivity analysis can be used to test different scenarios to see how the results of the model would change under different conditions.

Sensitivity analysis is a technique used to determine how sensitive the results of a financial model are to changes in the underlying assumptions. It can be used to assess the impact of changes in key variables on the model's output, to identify areas of the model that are most sensitive to changes in the assumptions, and to help inform decision making.

There are a number of different ways to perform sensitivity analysis, but the most common approach is to use a series of what-if scenarios. In a what-if scenario, you take a particular assumption and ask how the model's output would change if that assumption were to change. You can then compare the results of different what-if scenarios to identify the most sensitive areas of the model.

Sensitivity analysis can be a valuable tool for financial modelers because it helps them to understand how changes in the assumptions can impact the model's output. This information can be used to make more informed decisions about the model and the business it is modelling.

There are a few things to watch out for when performing sensitivity analysis. The first is that you need to make sure that you are using the right inputs in your analysis. This means that you need to make sure that your assumptions are realistic and that you are using the right data to calculate your results.

Another thing to watch out for is that you need to be careful when changing inputs. This means that you need to make sure that you are only changing one input at a time and that you are understanding the impact of each change.

Finally, you need to be aware of the limitations of your analysis. This means that you need to understand the assumptions that you are making and the limitations of your data.

There are five types of sensitivity analysis:

1. Parametric Sensitivity Analysis: This type of analysis examines how the output of a financial model changes when one or more input parameters are changed.

2. Scenario Sensitivity Analysis: This type of analysis examines how the output of a financial model changes when different scenarios are simulated.

3. Robustness Sensitivity Analysis: This type of analysis examines how the output of a financial model changes when it is subjected to different types of stress tests.

4. Sensitivity to Assumptions: This type of analysis examines how the output of a financial model changes when different assumptions are made about the underlying data.

5. Sensitivity to Model Structure: This type of analysis examines how the output of a financial model changes when the structure of the model is changed.

Sensitivity analysis is used to measure how much the value of a financial model changes when input variables are changed. This information can be used to make more informed decisions about which inputs are most important to the model and to identify which inputs have the greatest impact on the model's outcome. Sensitivity analysis can also help to identify which inputs are most likely to cause the model to fail.

Start building your own custom financial models, in minutes not days.