How to Perform a Financial Sensitivity Analysis

Financial models are great tools to assess the health and financial performance of your business. 

However, with all of the factors that affect a company’s projected performance, building an accurate model in Excel can quickly become an intimidating and complicated project to manage.

That’s where sensitivity analysis comes into play. This tool allows financial analysts to create a multitude of possibilities for any given scenario by methodically calculating the effect of different independent variables on the outcome. 

Here, we’ll go over what sensitivity analysis is, how to perform one, as well as one way to simplify this process for both analysts and business leaders.

What is sensitivity analysis?

Also known as what-if analysis, what-if simulation exercises, and data tables, sensitivity analysis is an instrument that financial modelers rely on heavily in their work. It perfectly complements scenario analysis, a similar tool that’s often used by analysts as well (more on that later). 

Data tables allow users to see the impact of multiple independent variables on a dependent variable under a set of very specific conditions. By changing the inputs on a given data table, analysts can observe how these variations affect the output. 

In this way, performing sensitivity analysis can also uncover crucial independent variables that have the largest impact on the scenario at hand. 

Say, for example, that your retail store is making preparations for Black Friday weekend. As the business owner, you want to know how much revenue your company would bring in by running a video advertising campaign on Facebook, based on the revenue that similar campaigns have brought in years past. 

With sensitivity analysis, you can then determine how much money you’d stand to make that weekend. You’d then be able to validate whether creating a new set of Black Friday video ads would actually pose a benefit to your business. 

Although this is just one way to use what-if scenarios, this practice becomes especially useful for complex “black box” scenarios in fields like economics, geography, and engineering that are otherwise impossible to analyze.

The importance of sensitivity analysis

In life and in business, we operate under the assumption that we’ll never know every single outcome of every decision we make. 

However, when you’re leading a multi-billion dollar company, you’ll want to take every precaution necessary before making a decision that drastically affects the future of your business.

Sensitivity analysis helps you learn more about the impact that any one decision can make on the overall success (or failure) of a project. But that’s not all.

Not only does it reveal potential obstacles and untapped opportunities for growth, what-if experiments also help you define the limitations and scope of your undertaking as well. You can even use sensitivity analysis to audit your financial model for any errors.

As you can see, sensitivity analysis has many uses for business leaders and managing directors alike.

With the right data and calculations, it can accurately predict multiple outcomes of a scenario so that you can tweak things and make changes long before things go terribly wrong. 

However, you can also use these what-if scenarios to gauge the importance of every individual factor on the outcome of your project. Applying this knowledge and allocating more resources to the factors that will make the biggest impact can help ensure your project’s success.

How to conduct sensitivity analysis

Although the specifics of sensitivity analysis can get very complicated very quickly, there is one principle that drives the entire practice: 

Change your model, one input at a time, and observe the changes that follow.

When setting up your data table, you’ll want to take three things into account: 

• The design of your experiment
• The things you want to change (your independent variables or inputs)
• The thing you want to observe (your dependent variable or output)

Once you have these three crucial parts laid out, you can then conduct sensitivity analysis. The key to success lies in systematically and methodically calculating each independent variable’s effect on the dependent variable. 

Here’s how to do it:

• Define the specific independent variables that will affect the outcome of your project
• Change one input and observe the effect it has on your output, while leaving all other independent variables the same
• Calculate the percentage change in both the input and the output
• Determine the dependent variable’s sensitivity to the independent variable by dividing the percentage change of the dependent variable by the percentage change of the independent variable

You’d then repeat each of these steps for every input variation to determine your dependent variable’s sensitivity to each of these factors. The higher the output’s sensitivity to a certain input, the bigger the input’s impact on the outcome. 

This approach, known as “local sensitivity analysis” or “one-at-a-time (OAT) analysis,” helps you look at the effect that a single change in your scenario can have on your entire project. 

However, this isn’t the only type of sensitivity analysis that a financial modeler can perform. 

Tools like the popular “global sensitivity analysis” approach can be implemented alongside Monte Carlo simulation experiments. In comparison to the specific set of inputs required by local sensitivity analysis, this simulation can be used to analyze an entire range of inputs as well.

Sensitivity analysis vs. scenario analysis: What’s the difference?

Like we mentioned earlier, sensitivity and scenario analysis often go hand-in-hand, so it’s easy for those who are new to financial modeling to confuse the two. However, there are several fundamental differences in these two tools that make them valuable to financial analysts. 

As you know now, modelers will use sensitivity analysis to understand the possible effects that a set of inputs would have on a project given certain conditions. The result? Multiple future scenarios that may play out depending on what action is taken.

On the other hand, scenario analysis involves examining one specific scenario in great detail, using a set of distinct, established variables. 

By gathering enough data to create a “snapshot” of the situation in question, an analyst can then accurately predict all the possible outcomes of this specific situation. Here, there is no action to take. It’s more an analysis of future opportunities constrained by the limits of a real-life scenario.

Scenario analysis is commonly used to assess the feasibility of financial investments, and can even be used to reveal opportunities or issues that an analyst may not have previously considered. 

However, in both cases, the accuracy of the analyses performed completely depends on the data and calculations used by the analyst conducting the experiment.

The advantages and disadvantages of sensitivity analysis

Sensitivity analysis is used to predict the different outcomes of a scenario, given a set of conditions. 

Because this simulation also tests a scenario across a huge range of possibilities, running one on a financial model adds credibility to it or, alternatively, reveals any errors an analyst might have made in putting the model together.

Sensitivity analysis is crucial for business leaders as well, as they can use these experiments to determine whether or nor a certain decision holds risk, what decision to make in a situation that impacts the future of their business, as well as what criteria to focus on to ensure their company’s success. 

However, this tool is not without its disadvantages either.

Business decisions aren’t made in a vacuum, yet what-if simulations run on the assumption that each independent variable has a one-to-one effect on the output. This model disregards the importance of interactions between variables, which often have a considerable impact in real-life situations. 

And as mentioned earlier, sensitivity analysis lives and dies by the historical data, assumptions, and calculations that the analyst makes. If even one data point is wrong, the entire model may be inaccurate — which poses huge problems for any business leader making decisions on this faulty information.

Bring sensitivity analysis into the 21st century with Synario

Although modelers have conducted sensitivity analysis in Excel for decades, new advances in technology allow financial analysts to run what-if simulations with ease. 

With Synario’s intelligent financial modeling software, incorrect Excel calculations are now a relic of the past. 

In fact, you don’t even need mastery of Excel to build accurate financial models. After inputting your data and the rules for your scenario, Synario creates your model in mere seconds. 

Take advantage of the software’s Multiverse Modeling feature to run an unlimited number of scenarios, so you can compare and contrast different outcomes of your what-if experiment in real time. 

Synario’s modeling intelligence tools also ensure that accurate data brings you accurate results.  When was the last time you placed complete trust in your financial model? 

When you make this software a key part of your modeling process, you can stop stressing over your calculations and save hundreds of hours spent poring over your spreadsheets in the process.

Leave the number-crunching to Synario, and start focusing on the tasks that really matter instead.