Critical Accounting Estimates (CAE)
The term critical accounting estimates refers to those assumptions and approximations that may have a material impact on the financial statements of a company due to the level of subjectivity involved in developing the estimate. The assumptions used when developing critical accounting estimates are outlined in a company’s Form 10-K filing.
Management’s Discussion and Analysis (MD&A) of Financial Condition and Results of Operations is a required disclosure made by companies that fall under the jurisdiction of the Securities and Exchange Commission (SEC). Critical Accounting Estimates, or CAE, is a subsection appearing in the MD&A that outlines the key assumptions used by the company’s accountants and subject matter experts when developing estimates that may have a material impact on the company’s financial statements.
The purpose of this section is to enhance the discussion and analysis of these estimates in a way that:
- Does not duplicate information already appearing in the notes to the financial statements.
- Provides the investor-analyst with additional insights into the variability of the company’s financial and operating performance as the result of estimating errors.
When introducing the Critical Accounting Estimates section of the MD&A, companies will typically include introductory language such as:
“The standards of Generally Accepted Accounting Principles (GAAP) oftentimes require the use of estimates, inputs, and assumptions that are subjective in nature. Differences between actual results and estimates can have a material impact on the company’s financial position, cash flow, and operating results. The following estimates have been identified as those critical to the application of these principles as instituted by the company.”
The materials presented in the CAE section of the MD&A should explain how the estimates were determined, any changes to estimates provided in the past, as well as the likelihood of an estimate changing in the future. Whenever possible, the variability of the data should be quantified. Finally, if similar information has been provided in the past, the accuracy of prior estimates should also be quantified.