Understanding Circularities in Financial Models: Causes, Examples, and Solutions
Circularities are a common challenge in financial modeling. They occur when formulas within a model create feedback loops, causing interdependent cells to reference each other, either directly or indirectly. This article explains what circularities are, how they arise, and provides actionable solutions to resolve them effectively.
What Are Circularities in Financial Models?
A circularity exists when the output of a formula depends on its own calculation, either directly or indirectly. For example, a formula calculating interest expense might depend on the debt balance, but the debt balance itself depends on the interest expense—creating a loop.
While circularities can sometimes be intentional and necessary, such as in debt modeling or project finance, they can also lead to errors, infinite loops, or unstable models if not managed properly.
Common Causes of Circularities
How to Resolve Circularities
1. Circuit Breaker Using IF Formula
A circuit breaker provides manual control to break feedback loops temporarily.
2. Enable Iterative Calculation in Excel
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3. Use Lagged Values
Use the prior period's values to break the loop. For example: Interest Expense = Beginning Debt × Interest Rate
This approach delays the feedback by one period, resolving the circularity without affecting the logical flow significantly.
4. Static Overrides
Temporarily hardcode values for the variables causing circularities, especially during model debugging or to ensure a stable starting point.
5. Model Simplification
Review the model’s structure to eliminate unnecessary interdependencies. For example:
6. Specialized Tools or Software
For complex models, use VBA scripting or modeling software designed to handle circular references efficiently. These tools can iterate calculations without manual toggling.
Best Practices for Managing Circularities
Conclusion
Circularities, though challenging, are often necessary in financial models to represent real-world interdependencies. By understanding their causes and implementing the right solutions—whether through circuit breakers, iterative calculations, or simplified modeling techniques—you can create stable, reliable models.