How to Leverage Dynamic Arrays in Excel for Better Financial Modeling
Nicholas Hay and Cameron Hay the co-founders of Finomatic Consulting have been kind enough to share one of the fully dynamic 3-statement models. The model is an example of the type of models they build for their customers and can be downloaded here for free Nicholas Hay and Cameron Hay the co-founders of Finomatic Consulting.
This is the link: https://www.thefpandaguy.com/excel-files
The finance sector is rapidly evolving, and professionals constantly look for tools to keep up. Dynamic arrays in Excel transform how financial models are built, making them more flexible and reducing errors. This feature is crucial for adapting to the ever-changing demands of the market.
Nicholas Hay and Cameron Hay the co-founders of Finomatic Consulting are leading this advancement. Nicholas, with his background in languages and an MBA, and work experience in investment management.
Cameron, a trained chartered accountant who spent six years providing M&A and fundraising advice to technology companies, uses their deep expertise to enhance financial strategies for SAS companies.
In this article, we explore how dynamic arrays enhance Excel's capabilities based on insights from Nicholas and Cameron Hay. You'll learn how these tools can streamline your financial modeling processes and why embracing them could be a game-changer for your business.
Let's dive in!
Advantages of Dynamic Arrays in Excel in Financial Modeling
Dynamic arrays significantly enhance Excel's modeling capabilities by allowing arrays to expand or contract based on input changes. This flexibility reduces the static constraints of traditional Excel setups, leading to more dynamic and robust financial models.
Photo by RDNE Stock project on pexelsThe Shift Towards Dynamic Arrays
The shift towards dynamic arrays typically follows a gradual adoption, where the benefits become increasingly apparent:
Increased Speed and Robustness: Models become quicker and more robust due to structured and efficient recalculations.
Error Reduction: Dynamic arrays minimize potential error points by reducing the number of necessary formulas. This simplification leads to easier and more reliable checks within models.
Maintenance and Client Handover
Dynamic arrays simplify the maintenance of Excel tools:
Easier Adjustments: When clients require modifications, the structured nature of dynamic arrays and lambdas ensures fewer disruptions and errors.
Client Independence: Once handed over, clients can manage the models without frequent developer intervention, provided no extensive changes are made to the core structures.
Client Understanding and Training
While dynamic arrays and LAMBDAS represent advanced Excel features, they can initially challenge unfamiliar users. However, educating clients about manipulating inputs and observing outputs can demystify the models.
Clients proficient in Excel find the transition easier, as the fundamental layout of financial statements remains unchanged.
Favorite Functions of Dynamic Arrays in Excel
Preferences for specific dynamic array functions vary based on the modeling needs:
Expand Function: Useful for ensuring consistent timeline widths across models and is Nicolas Hay’s favorite dynamic array.
Sequence Function: Appears simplistic and of limited use initially but proves powerful when integrated with other functions, particularly for date manipulations. Cameron find this to be his favorite dynamic array.
Handling Complexities with Corkscrews
Dynamic arrays alone cannot manage certain complex scenarios like corkscrews, which require the integration of LAMBDAS to handle dependencies and prevent circular references effectively.
Engaging with experts and leveraging community resources, such as the full-stack modeler community, can provide solutions and insights into overcoming these modeling challenges.
Understanding And Utilizing LAMBDAS with Dynamic Arrays in Excel
LAMBDAS are custom functions created in Excel using existing Excel functions that allow users to define their functions, which can then be treated as native Excel functions. This capability is particularly useful when building fully dynamic models in particular when managing corkscrews.
LAMBDAS makes creating fully dynamic models possible by solving some of the issues with fully dynamic models including corkscrews among others. For more on this, please review Craig Hatmaker’s website Beyond Excel. He provides copies of his 5G LAMBDAS and shows how you can use them in various situations.
Photo by Kampus Production on PexelsKey Benefits of Using LAMBDAS
Simplification of Formulas: LAMBDAS can significantly reduce the number of formulas in a spreadsheet. For instance, transforming a block of 27,000 formulas into a single formula showcases LAMBDAS's efficiency.
File Size and Performance: Using LAMBDAS and dynamic arrays can reduce the file size and improve the efficiency of calculations, even for complex models involving extensive data.
Adoption and Learning Curve: While LAMBDAS enhance Excel's functionality, their adoption will take time, similar to other advanced Excel features like Power Query, and Power Pivot.
Practical Implementation of LAMBDAS
LAMBDAS can be developed in-house or imported from external sources, with experts often providing more reliable and tested functions.
For example, importing LAMBDAS developed by specialists such as Craig Hatmaker can ensure better functionality and reliability than creating them without sufficient expertise.
Transitioning Fully to Dynamic Arrays in Excel
Commitment is crucial for users considering transitioning to dynamic arrays fully supported by LAMBDAS. Mixing traditional methods with dynamic arrays limits the benefits.
Therefore, it is advised to fully commit to using dynamic arrays and LAMBDASto leverage their full potential. Transitioning an old model to dynamic arrays can be a learning process, but the long-term benefits justify the effort according to Cameron and Nicolas Hay.
Resources and Learning
Websites like BeyondXL offer resources where users can learn about LAMBDAS, with tutorials and examples available to enhance understanding. This approach allows users to think of Excel models in terms of larger data blocks rather than individual cells, streamlining processes and calculations.
Users can achieve more streamlined, efficient, and robust data management practices by incorporating LAMBDAS into Excel workflows.
Insights on Financial Modeling Errors and Best Practices
Cameron and Nicholas Hay shared their experiences and lessons from encountering poorly structured financial models. These insights emphasize the critical importance of structure and verification in financial modeling to prevent errors and enhance model reliability.
Common Financial Modeling Mistakes
Overcomplicated Color Schemes: Using excessive colors can obscure the data's clarity and hinder understanding.
Improper Aggregation: One notable error involved summing both individual staff salaries and their subtotals, which, when compounded with growth and inflation factors, led to disproportionately inflated staff costs.
Unrealistic Assumptions: To demonstrate profitability, one model drastically reduced costs while unrealistically doubling growth projections, all hardcoded without a sustainable basis.
Key Takeaways for Effective Financial Modeling
Clarity and Simplicity: Ensure the model is easy to manage and understand. Avoid unnecessary complexity that can obscure data interpretation.
Structure and Verification: Maintain a well-organized structure to identify and rectify errors quickly. This approach is crucial for models used in dynamic business environments like mergers and acquisitions.
Realistic Assumptions: Base projections on realistic assumptions and provide clear visibility into the inputs driving these assumptions.
Visualization: Use charts and graphs to make data trends and discrepancies immediately apparent, facilitating quicker and more accurate analyses.
By adhering to these practices, financial models become more robust and trustworthy, providing a reliable foundation for business decisions.
Conclusion
In conclusion, dynamic arrays in Excel transform financial modeling by enhancing flexibility and accuracy. This feature allows arrays to adjust automatically, reducing errors and simplifying processes.
By integrating dynamic arrays with lambdas, Excel users gain powerful tools for efficiently managing extensive data and complex calculations. This advancement streamlines workflows and ensures models are robust and adaptable.
As the financial sector evolves, adopting these technologies is crucial for maintaining a competitive edge and making informed decisions. Therefore, fully embracing dynamic arrays is advisable for anyone looking to improve their financial modeling capabilities.
