Here's how you can overcome challenges when applying logical reasoning to financial modeling.

Building robust, dynamic, and accurate financial models for decision-making is no easy feat. There are several considerations you want to keep in mind while interpreting your model for an internal team or external stakeholders. Here are some key factors to consider: Defined Goals and Objectives: 1. Your internal team must be clearly aligned regarding what specific questions the model will answer. Without this clarity, each model iteration is just another shot in the dark. 2. Verify that the stakeholder audience also understands the goals and limitations of the financial model.

Collect Accurate and Relevant Data: 1. Access to relevant data from connected systems to ensure the most up-to-date information is represented in the model. 2. Validate data consistency and accuracy with data cleansing processes (e.g. ETL).

One common mistake we find is the over or undervaluation of the ROA. As we all know, asset depreciation is usually calculated using a conventional method of recognizing asset wear and tear. The most widespread is the linear method, but there are a few others. However, none of these methods accurately reflects the true value of the assets, which is known as their net realizable value. This net realizable value is usually above or below the net asset book value and can mislead us when making decisions. Due to this, we may find ourselves with an erroneous calculation in the rate of return on the Assets, which arises from dividing the net result after taxes by the Total Assets. So, we must be aware we can face this problem anytime.

One of the primary essential issues when designing a financial model is to be clear on its purpose: 1) Is it to identify the value for a leverage buyout operation? 2) Is it to identify the IRR for a Capex investment project? 3) Is it to determine a liquidation value? 4) Is it to calculate a firm value that includes perpetual value? 5) Is it to purchase only the total Assets free of debt? 6) Is it to identify the brand value? 7) Is it to calculate the NPV? which is sometimes confused with Flow at the current value? If we are not clear about the valuation purpose and the sector's drivers that better idetify the firm's value, we can make significant mistakes that affect our clients when buying or selling their company.

To build robust and reliable models make, assumptions transparent & incorporate sensitivities 1. Identify potential biases & flaws in reasoning 2. Consider alternative perspectives & scenarios 3. Evaluate robustness of conclusions 4. Develop a more nuanced understanding of the problem Sensitivity analysis takes this a step further by analyzing how changes in assumptions affect the outcomes. 1. Identify the most critical assumptions driving the results 2. Understand how changes in assumptions impact the conclusions 3. Develop a range of possible outcomes based on different assumptions 4. Communicate the uncertainty associated with findings Highly recommend slider scales for range and combination of assumptions and impact to outcomes

Use realistic model assumptions that account for historical trends and industry benchmarks when building your financial model: 1. Make model assumptions based on historical data, industry trends, and analyst opinions. 2. Ensure transparency in your model by stating all assumptions in the model and discuss the rationale behind each assumption within your team.

I remember coming across a valuation of intangible assets in an enterprise valuation audit and due diligence. Still, the supposed value was so important that, by removing it as an assumption, I could verify that this valuation was hidding a technical bankruptsy. Therefore, I was obliged to review whether the intended valuation of the intangible complied with the assumptions required by the OECD and recommended by the IFRS valuation standards. To my surprise, it did not comply with any of them. Given the circumstances, I strongly advocate the uses of -what if- assumtions to verify some reporting chapters This will not only help in avoiding potential financial pitfalls but also ensure a more robust and accurate valuation for your client

Ever built a financial model so tight you thought it was bulletproof, only to have reality laugh in your face? I've been there. That's where scenario analysis comes in, not just as a tool, but as a game changer. It's like preparing for a trip. You pack for the sun, rain, and everything in-between. Why? Because you know the weather's as unpredictable as the market. By developing best-case, worst-case, and most likely scenarios, you're not just guessing the future; you're preparing for it. This approach transformed how I view financial models. Instead of a static map, they became a dynamic guide through the uncertain terrain of business.

Tips to incorporate scenario/sensitivity analysis into your financial modeling process. Utilize Scenario Analysis: 1. Create multiple scenarios (worst case, base case, best case) to evaluate potential outcomes. No scenario will ever perfectly match reality, but it’s important to prepare in advance for the unknown market dynamics. 2. Use the scenario analysis model to test the model underneath various market conditions (e.g. rising inflation, reduction in interest rates). Utilize Sensitivity Analysis: 1. Conduct sensitivity analyses to measure how certain input variables impact model outcomes. 2. Identify the highest value input variables when creating scenarios and focus on estimating these as accurately as possible.