Sensitivity Analysis is a business practice that is fundamental in producing various types of financial forecasts.

There are many applications of sensitivity analysis across various sectors in many different professions. Economists use sensitivity analysis to identify how certain variables in their mathematical models impact predicted outcomes.

Sensitivity analysis can also be used to attempt to identify arbitrage opportunities in securities prices or to model the impact of interest rates on bond prices.

Sensitivity analysis is also used in predictive models for climate change and weather patterns, and even in fields like biology and geography. In corporate finance it is applied to predict how certain variables will impact forecasted financial results.

What is Sensitivity Analysis?

Sensitivity analysis is a practice that attempts to determine how changes in independent variables affect other dependent variables in a mathematical model under a set of certain conditions.

In simple terms, sensitivity analysis is a tool used to analyze outcomes under certain circumstances. Although sensitivity analysis is used across a wide variety of fields and professions, it is most commonly used among corporate finance professionals

One of the major tools used by corporate finance professionals is forecasts, which attempt to predict financial performance based on historical information.

In the process of building forecasts, both in and out of the finance profession, the creator of the model must ground the forecast in a set of assumptions. These assumptions drive the entire predictive model that will be used to make decisions.

The process of defining the assumptions of a model can be arduous and may include macro and micro analysis on the business environment, economic conditions, and other external factors.

Because the model relies so heavily on the assumptions, there exists a need to test it under various circumstances. This process is often referred to as “What-if Analysis”.

What-if analysis is a process used by financial analysts to predict future performance under certain circumstances. The name is another name for sensitivity analysis, which are both used to test outcomes using changes in independent variables.

An example of independent variables in a financial model might be interest rates, occupancy rates, loss-rates, and other metrics that are independent of the model results. Any independent input has a direct impact on the model’s outcomes.

What-if analysis attempts to answer questions like, “what would happen if interest rates increase?”, or “what would happen if rent collections drop?”.

The process of testing model outcomes by answering these types of questions is considered sensitivity analysis.

Sensitivity analysis is different from scenario analysis, which is the process of analyzing a given scenario.

Scenario analysis differs in that the examination of a single scenario is separate from analyzing the impact of certain independent variables under a certain set conditions.

For example, scenario analysis might examine the impact of overall demand shifts, where sensitivity analysis might examine the impact of rising costs and wages on the bottom line.

Why Sensitivity Analysis Matters

When enterprises define their goals, a financial plan is created as a roadmap to achieve them. The forecasts that are the result of the financial plans act as a guide to steer the ship.

Because sensitivity analysis attempts to predict outcomes due to uncertainty, it helps to identify potential risks and subsequently creates courses of action to mitigate them.

In fact, one of the major benefits of performing sensitivity analysis is that it helps to illuminate the financial impact of certain risks.

This is a critical function of financial analysis that stretches beyond corporate finance and budgeting.

For example, sensitivity analysis is used by credit committees to understand how certain assets are impacted by changes in market conditions and the degree to which they are impacted. This analysis helps them navigate the underwriting process to ensure the assets they collateralize are within their risk threshold.

Similarly, businesses might use sensitivity analysis to understand the extent to which minor changes in operations and market conditions could impact the bottom line.

This creates a preemptive environment for proper risk mitigation and controls. Sensitivity analysis also provides credibility to a financial model because it tests the model for various possibilities.

Best Practices When Performing Sensitivity Analysis

Performing sensitivity analysis is simply the process of changing independent variables in a financial model. There is nothing inherently difficult about it; therefore, it is more important to understand the standard practices when performing sensitivity analysis.

Make Sure The Model Is Organized

Creating a structured systematic approach in an excel-based model is important for many reasons, but most importantly, it reduces risk of error.

All of the assumptions should be placed in one place in the model, sometimes on an entirely separate tab. Format the assumptions in a unique way to ensure they are easy to identify.

Use a Systematic Approach when Analyzing

There are two ways to perform scenario analysis: “direct” or “indirect”. The direct method is the process of substituting values directly into the assumptions.

Indirect is the use of percentage changes instead of directly changing the value in a model. The two can be combined, but typically it is best to use them systematically.

Because the results can be difficult to take in, especially when models are large, it is important to use tables, charts, graphs, and illustrations wherever possible.

The results are arguably the most important aspect of the analysis, so ensuring they are easy to read and meaningful is important.

Using Datarails to Perform Sensitivity Analysis

Every finance department knows how tedious building a forecast can be. Regardless of the modeling approach your organization adopts, it requires big data to ensure accuracy, timely execution, and of course, monitoring.

Datarails is an enhanced data management tool that can help your team create and monitor financial models faster and more accurately than ever before.

By replacing spreadsheets with real-time data and integrating fragmented workbooks and data sources into one centralized location, you can work in the comfort of excel with the support of a much more sophisticated data management system behind you.

This takes modeling from time-consuming to rewarding.