Introduction
Financial model validation is the process of examining a numerical model for potential errors and identifying ways to improve its accuracy and reliability. By validating a financial model, you can make sure that the end results are reliable and useful for making decisions.
Financial model validation typically involves testing the logic and calculations used in the model, and conducting a reconciliation of the results with actual results reported by the companies. Validation checks reveal any discrepancies and help you identify areas where the model should be improved.
Common types of validation errors include input errors (e.g., typos, incorrect formula references, data mistakes), calculating errors (e.g., incorrect formulas, incorrect arithmetic, incorrect lookup), and output errors (e.g., incorrect labels, incorrect interpretation of results).
Key Takeaways
- Financial model validation is the process of testing for potential errors
- Input errors, calculating errors, and output errors need to be checked for
- Identify discrepancies using the reconciliation process to improve the model
Build and Validate the Model
Financial models are constructed by combining data, equations and assumptions into a logical system. To ensure accuracy, the model should be built with care and attention to detail, and validated before relying on it. Validation helps enhance confidence in the model and provides a process to check the assumptions and results. Here are key steps to consider when building and validating a financial model.
Clarify the Financial Model Purpose
When starting the process of building a financial model, it’s important to understand the purpose of the model and the questions it will answer. This will help drive the creative process of constructing the model and the types of inputs and outputs required.
Begin with an Accurate Data Source
Before constructing the model, the most accurate, reliable and up-to-date source data should be obtained. Consider the sources of the financial data, such as government statistics, industry trends, and competitor performance, and incorporate them into the model.
Check the Logical Structure of the Model
Once the data is organized, the logical structure of the model should be carefully reviewed to ensure the equations and assumptions are valid. Use a logical flow, broken into smaller, simpler components, and use a spreadsheet design protocol for a consistent and organized approach.
Analyze Outputs
Once the model is constructed, outputs should be tested for accuracy using sensitivity analysis or scenario planning. Sensitivity analysis tests how sensitive outputs are to changes in a particular input, while scenario planning tests multiple combinations of inputs to compare outcomes. This will help validate the results and identify any potential issues.
Find and Rectify Errors
Financial models are useful tools for forecasting, providing insights, making decisions, and monitoring the performance of a business. To ensure those tools are providing accurate and reliable information, financial model validations must be performed. Here we'll provide step-by-step instructions for validating a financial model in Excel.
Start by Finding Formula Errors
Errors can arise from a number of issues. First, check for formula errors by inspecting every formula used in the model and checking for concordance between the calculations used in the model and the output. If the result of a formula does not match the input, there is an error that needs to be rectified.
Other common formula errors include typos in the formula, labels that have been used incorrectly, and formula omissions. The latter is one of the most common reasons for errors, so it's important to make sure every formula used in the model has been specified correctly.
Check for Unreliable Calculations and Move onto Macros
After finding and fixing errors arising from formulas, the next step is to examine any calculations that could be considered unreliable. This means looking for any calculations based on outliers, or extreme values, such as top and bottom line estimates that are far away from the rest of the numbers.
Once any formula errors and reliability issues have been addressed, the last step is to review all macros used in the model. These can be very difficult to debug and troubleshoot, so it's important to review them carefully to make sure they are working correctly. Before running a macro, it's also important to ensure that it is virus-free and secure.
Review Results
Once the model is complete, it is important to review the results for accuracy. The review process should include examinations calculations, results, and observations made by subject matter experts in the relevant domain.
Review Calculations and Results
The most crucial step in validating a financial model is reviewing all of the calculations throughout the workbook. Modelers should walk through each sheet individually to ensure that the formulas used and results produced are accurate and reasonable. Going through each sheet in this manner will identify any errors or warnings in the model earlier rather than later.
Confirm the Results with Subject Matter Experts
It is important to obtain confirmation from subject matter experts in the respective domain before submitting a financial model for review. After the calculations and results for the model have been verified, the subject matter experts should review the model again and assess the model from the perspective of their expertise. Their review should focus on whether the model accurately projects the expected outcome while considering their area of expertise.
For example, if the model is being used to predict the profitability of a new product offering, the review by the subject matter expert should focus on the market the product is entering and any competitive dynamics associated with the product's launch.
Perform Quality Assurance
In order to validate a financial model in Excel, performing Quality Assurance is an essential step. By performing quality assurance, potential errors and mistakes can be identified, and the results produced by the model can be deemed reliable. Quality assurance should take place in order to ensure that the results of the model are accurate, logical, and objective. This can be done in several ways, which are outlined below.
Cross-Check Inputs and Assumptions
Due to the complexity of financial models, mistakes can be easily made. Therefore, the ends the model outputs and the assumptions that are made should be cross-referenced against external sources. This will allow any discrepancies to be identified and corrected, ensuring that all inputs and assumptions are accurate and align with any supporting materials.
Sorting, Sums and Other Tests
Once the inputs and assumptions have been cross-referenced, it is a good idea to carry out further tests on the data. This could include sorting the data in different ways, performing sums or averages, counting the number of entries, or checking for duplicate entries. All of these tests will help to identify any issues with the data or results and ensure the accuracy of the model.
- Check if all entries are in numerical formats.
- Check if all calculations are logical and consistent.
- Check if all average calculations are reasonable.
- Check the currency of all entries and results.
Overall, by cross-checking inputs and assumptions, and carrying out further tests such as sorting, sums, and other tests, the accuracy and reliability of the model can be assessed and potential issues identified and rectified.
Documentation
Managing financial models in Excel requires regular updates and changes to keep them accurate and up to date. One of the key aspects to remember when making changes is to document the updates. This article provides an overview of documentation processes and content to validate financial models in Excel.
Documentation Process and Content
The process of documenting any changes to the financial model should be put into place from the outset. Document each step of the process of creating the model and in detail. Record formulas, assumptions, and data sources so that if changes are required in the future then there is a clear understanding of the original model and its workings.
When documenting updates, record the date and any changes that were made, along with a description of the changes. Include comments within the actual model so that it is easy to understand its current version. This documentation process enables anyone working with the model to understand the updates, maintaining accuracy and providing visibility on changes made. It also creates a record that is helpful to refer back to in the future.
Update Documentation Upon Changes
Documentation should be updated whenever changes are made to the Excel financial model. By regularly reviewing the documentation, it will ensure accuracy of the model and ensure assumptions used are up to date and valid.
When new users are added to the model, the documentation must be made available to them to understand the model. Its content can then be reviewed and adapted as needed. The documentation should be updated in line with any changes made as part of the review process.
Any changes made to the financial model must be thoroughly evaluated and documented. Once the evaluation is complete, communication should be made to all relevant stakeholders so they can review the results and any implications of the changes.
Conclusion
Financial models are essential for helping business decisions, but the reliability of those models relies on their validity. Utilizing tools in Excel, users can validate the formulas and data used in a model before using the resulting analysis in important business decisions. The robustness of the 2D references, range names, data validation and other techniques will improve the confidence that users have in the analysis and output of the financial model.
With proper preparation, analysis and verification of data, the final model is stable and can offer reliable guidance on the future prospects and profitability of a company. Validation tests in Excel give the user peace of mind that the calculations in the model are accurate, and that the user can trust the results.