In financial forecasting, which type of analysis is commonly used to assess potential risks?

Sensitivity analysis is a critical tool in financial forecasting for evaluating potential risks. It involves changing one or more input variables in a predictive model to see how these changes affect the outcome. This method allows financial analysts to test the robustness of their forecasts under different assumed conditions, identifying how sensitive the results are to changes in key assumptions. For example, if a company is forecasting its future cash flows, it might assess how various scenarios—like increases in costs, changes in sales volume, or varying interest rates—affect its projected financial performance.

This process is particularly valuable in risk management because it helps stakeholders understand which variables have the most significant impact on outcomes and under what conditions the forecasts might fail. By identifying these critical factors, companies can make more informed decisions, prepare for adverse scenarios, and develop contingency plans.

While the other types of analysis mentioned, such as correlation analysis, variance analysis, and segmentation analysis, serve useful purposes in different contexts, they do not primarily focus on assessing potential risks in the same way that sensitivity analysis does. Correlation analysis looks at relationships between variables but does not typically simulate changes to assess risk. Variance analysis measures the deviation of actual results from budgets or forecasts, and segmentation analysis divides markets into segments but does not directly address risk