How can you stress-test a financial model for accuracy?

Excel is a great tool for financial modeling, and allows for different assumptions and scenarios to be built easily. As a start, analysts would typically start with 3 scenarios a base case, a down case and an up case, each with different assumptions to reflect more bearish or more bullish views. Key assumptions that are typically adjusted depending on the scenario is sales (price or quantity of items sold), costs (of goods, salaries, rental, distribution costs), growth rates year on year, and interest rates (that would impact borrowing and the discounting factor of cash flows to get an NPV (net present value) of the cash flows. Other scenarios could account for policy changes, or inorganic growth like M&A. Try that + connect with me

I recommend building up scenarios by varying one assumption at a time. Assumptions can interact, so assessing if a combination scenario is 'as expected' can be complex. Change one variable, sense check the result, then repeat with your other variables. Much easier to ratioanlise the result if you've seen the impact of the components.

Stress-testing a financial model is like preparing for life's uncertainties – you've got to anticipate different situations. It's a bit like planning for the future, considering not just sunny days but potential storms. Think of it as choosing scenarios that resemble life's curveballs – economic shifts, industry surprises, and regulatory twists. Just as you'd weigh the likelihood of running into various situations in real life, here, we assign probabilities, a bit like predicting the chances of rain. It's not just about being ready for the best outcomes but also ensuring we can weather the unexpected, making our financial plans as robust as our plans for whatever life throws our way.

Stress-testing a financial model isn't just a technical exercise; it's a strategic one. By choosing scenarios that span the spectrum from industry shifts to regulatory changes, you're not just checking a model's robustness—you're gaining insights into potential pivot points for your investment strategy. It’s about asking, 'What if?' in a structured way. This approach helps in identifying not just risks but opportunities—maybe a market downturn reveals a resilience in your asset not previously accounted for, or a regulatory change opens up a new avenue for growth. It's this depth of understanding that separates good investments from great ones.

In stress-testing a financial model, think of key drivers as the MVPs – the real game-changers. It's like identifying the star players on a team. In our case, these are the variables that really shape our projections, like revenue growth or customer retention. Just as a team's success hinges on its key players, our model's accuracy depends on understanding what truly moves the needle. It's a bit like knowing which skills are most vital – not just in sports, but in the financial game. We not only pick the key drivers tailored to our target, but we also measure their potential impact, a bit like gauging the impact of each player's performance on the final score.

As I stress-test a financial model, I first identify key drivers, including revenue growth and market share, but also crucial non-financial metrics such as regulatory environment and country-specific risks. I quantify the range and distribution of these factors based on historical data, industry benchmarks, and my own assumptions. Constructing scenarios allows me to evaluate the sensitivity of the model to changes in these key drivers. By assessing risk metrics related to country-specific regulations, I enhance my understanding of potential regulatory hurdles and their impact on the model's accuracy and reliability.

Creating a sensitivity analysis for your financial model is a bit like stress-testing a car before a long road trip – you want to know how it handles different terrains. It's akin to checking the suspension or tires, ensuring your vehicle can navigate smoothly through twists and turns. Here, our model is the ride, and sensitivity analysis is our way of predicting how it will respond to changes in the road, represented by key drivers. It's a bit like anticipating bumpy roads and choosing the right tools – be it charts, tables, or simulations – to ensure a comfortable journey.

Every project tends to have a unique structure for scenario analysis, and it's important to mitigate key metrics for a more realistic approach. Sometimes, built-in functions like Goal Seek are not sufficient. For example, when pricing expected electricity for renewable projects in Eastern Europe, prices tend to be more volatile due to factors such as local politics, infrastructure, seasonality, geopolitics, regulations, and tariffs. I prefer building structural scenario analyses for project finance-related matters and assumptions on Excel and then running simulations on Python to add prudence. Generating visual models helps stakeholders gain a better understanding.

Firstly, make top-down back-of-the-envelope calculations to check if the numbers are in the expected range. Secondly, create a diagram outlining the model logic before you start modelling. The diagram will help your colleagues to understand the model and catch errors in the model mechanics. However, it's equally important to embed checks and balances within the model itself. For instance, include complete balance sheets and P&L statements that need to balance. This helps catch errors and ensures that the model is consistent from an accounting perspective. Last but not least, ask your colleague to review the model and formulas. Every model has at least three mistakes, let's find them before your client or boss finds them.

Self-review isn’t easy, but here’s 5 things I would always recommend you do: 1 Use model review software, this will find some easy win errors. 2 Revisit your complex formulae and break them down into smaller steps, rows are cheap so use them! 3 Pro tip! Anything you did after midnight has a high probability of being wrong – go back and review. 4 Modelling is as much about commercial acumen as great Excel skills, review the outputs, could you defend them? 5 Finally, presentation doesn’t change the answer, but it gives people confidence a model has been professionally built. Polish the formatting, consider your audience, delete anything redundant and please run a spell check.

To stress-test the financial model, focus on reviewing and conservatively assessing key assumptions such as revenue growth rate, profit margins, and the exit multiple. These drivers are crucial for value creation and can significantly impact the deal's success. By scrutinizing these assumptions with a cautious outlook and employing methods like sensitivity analysis, identify potential risks that could derail the deal. This approach ensures the model accurately reflects the potential challenges and uncertainties, providing a more robust evaluation of the investment opportunity.

When I review assumptions and outputs, I meticulously examine inputs and outcomes to ensure they align with expectations and conclusions. This involves assessing reasonability, realism, and robustness, crucial for effective communication. Utilizing sensitivity checks, benchmarking, and validation methods, I verify validity prudently. Summarizing and presenting findings clearly enhances stakeholder transparency and understanding. Assumptions and expectations vary based on stakeholders' beliefs, but mitigating these with proven models optimizes alignment.