I’ve come across a lot of preconceptions (and misconceptions) about the idea of financial modelling; many people think it sounds very elaborate and esoteric, even mystical. Some question whether the skill is a science or a creative tradecraft in the industry, and I’ve even heard suggestions that it’s a dark art! But what is financial modelling really – an art, science, or just plain magic?
A financial model takes a theoretical idea or problem and translates it into a practical construct we can use to see what might happen in the future. Like a crystal ball? Well, I suppose so, but it’s far more accurate! For example, you might be worried about business risk and want to know what will happen to profitability if costs from a particular supplier were to increase. You can build a model to represent the business and run scenarios to see how profitability might be affected if these costs were to increase. In this case, inputs are costs, volume and pricing, and outputs are profits. A good financial model has these inputs and outputs clearly defined and as we change the pricing inputs, the profitability projections will automatically change. Being able to perform simple scenarios or sensitivity analysis in a model like this, the instant information can really seem like magic. So Financial Modelling is magic then? Well no, not really. Once you know how it’s done, you can break it down into a set of processes and in the same way as a card trick, it can be taught!
Is Modelling an Art or a Science?
As there are many components that make up a financial model, the skills needed for financial modelling are many and varied. A modeller must be a good mathematician as well as someone who can think creatively and who possesses sound logic in order to pre-empt possible outcomes and make sound and reasonable assumptions about the business. Like a fortune teller, you need to be able to read the situation and ask the right questions, assess possible roadblocks and side-step potential pitfalls. A good modeller will not delve into unnecessary detail, knowing when to use rough estimates for assumptions, and when to perform precisely detailed calculations.
Simply put, the financial modeller must get the calculations correct, understanding the relationship between numbers, which data to collect, and be able to define and explain the assumptions made for future projections. There are also the considerations of other variables, such as stakeholder buy-in and the wider environment. How likely is it that the decision makers are going to change the growth rate assumptions over time? If not, a simple single input for growth will suffice. Otherwise, a separate input cell needs to be created for every single time period of the model, which makes the model more complex to build as well as audit and understand. A good financial modeller can make the trade-off between simplicity, clarity and sophistication or complexity and has the ability to see how their model fits into the bigger picture.
So are these artistic skills or scientific skills? In my opinion, they need both.
The scientific side of modelling is in quantifying the business performance, getting the mathematics correct and understanding the relationships between the quantitative data. In order to engage and motivate investors, the numbers must be accurate and clear. This, in basic terms, means ensuring that data used reflects the true situation clearly and concisely, and highlights the key information. Taking this approach shows that precision and complexity are key features of the financial model, and will evoke confidence to those viewing and using the model.
A financial modeller certainly needs a certain level of artistic skill for the aesthetic design and layout of a model. This is an area that many modellers and analysts struggle with, as aesthetics simply do not come naturally to left-brain thinkers like us. (Now, I know that this left brain / right brain theory has been largely debunked but I still think there’s something to it!) We are mostly so concerned with accuracy and functionality that we often fail to notice the way the model looks to someone else viewing it for the first time. Although it’s just a simple matter of taking our time when formatting, most of us could not be bothered with such trivial details as making models pretty, and consequently most models I see use the standard gridlines, font, and black-and-white colouring that are Excel defaults.
I’m certainly not suggesting that you embellish your models with garish colours, but you should take some pride in your model. I’d highly recommend that you spend a few moments to change the styles and formatting as you build the model so that it looks less like a clunky spreadsheet and more like a professional, reliable, well-crafted model you’ve taken your time over. Research shows that users have more confidence in models with aesthetically pleasing formatting than those without, so one of the fastest and easiest ways to give your model credibility is to simply spend a few minutes on the colours, font, layout, and design.
The artistic nature of a financial modeller is also needed when the model requires forecasting of future performance. Crystal ball gazing is probably not a skill that you possess, so you will need to spend some time thinking about how this is best done accurately. A set of assumptions must be made to make predictions or forecast outcomes in your model.
If a financial model is required for an untested startup or product, then the model really is a true art; even the most basic calculations will involve a lot of assumptions and rationale explanations. Working out which assumptions to use is a difficult task, especially if you don’t have prior history to go on. Sometimes it’s simply a matter of coming up with what you think is a reasonable assumption for the base case, and then performing sensitivity analysis on these inputs to see what the impact would be if you got it wrong (because let’s face it, they are highly likely to be wrong in this sort of situation!) Use your influence to gather data, perform careful research and analysis, or engage subject matter experts to help you to get some comfort around the inputs and assumptions. Before presenting the output of the model to stakeholders, ask for their feedback and input on the logic and calculations which have gone into the model. Use the knowledge of those around you; involve them right from the start of the project. Get your audience and stakeholders to participate in the magic of the financial model building process, because as every great magician knows, audience participation increases engagement and attention span so that because they are involved in your model, they are willing for it (and you) to succeed!
In the 1990s when the internet sector was growing very quickly and the potential returns from internet-based companies soared to giddy heights, investors were lured into decisions based on impossible situations within models. They believed financial models depicting growth rates above 1000%. Of course, a financial model is only as good as the assumptions that go into it and these models were not faulty, rather the assumptions were incorrect. The incredible potential of these companies based on this exciting new product called the world wide web caused irrational behaviour as it triggered a belief that anything was possible and logical reasoning no longer applied. The financial models did not rely upon accurate calculations and had wild assumptions integrated – metrics predicting future performance such as page views and clicks – before there was any understanding of how these metrics could increase revenue. Because these growth rates were based on unsustainable assumptions, it was only a matter of time before the dot-com bubble collapsed to cause widespread misery to all involved.
No matter what else, the calculations within a model must be realistic and must consider mathematical principles as well as incorporating clever and logical guesswork. Like a good card trick, your audience will want to know how you achieved it, and unlike a magic show, you cannot keep your “tricks” to yourself! You need to be able to explain the magic (i.e. how you came up with the calculations) to continue the performance.
1. “Reducing Overconfidence in Spreadsheet Development”, Dr Raymond R. Panko, 2003