Financial Models – Too Much Information?
At Mobius Venture Capital, as in many venture firms, we don’t have analysts (people whose primary responsibility is to run models, cap tables and the like). As a result, each of us does most of our own financial modeling. I actually like this set-up, because it makes sure that I’m both directly responsible for my work and am up to speed on the financials of each of the companies I work with. Reviewing financial models is not the largest part of my job, but is an important part of what I do – for screening new investments; tracking portfolio company performance as well as analyzing follow-on investments into companies in which we already have a financial interest. In the course of reviewing many many many such models, something rather counter-intuitive has struck me: most financial models are too detailed. That’s right – most models have too much information in them; too many assumptions; too many inputs; and are too hard to follow. Now, don’t get me wrong – there is definitely a place and time for a detailed line item budget (say for a rolling 12 month operating plan). That said, trying to detail out line item projections over a 5 year period I think makes models less rather than more useful. When I was in college I really enjoyed theoretical economics. One of the classes that I liked the most was Econometrics. As a relatively green econ student, I remember that my inclination (and that of my classmates) when building econometric models was to put in as much data as possible – the theory being that more data wouldn’t harm the model and would potentially help it. Our professor, Gary Krueger, pounded into us that this was in fact not the case – weak data hurt your model and taking out mediocre variables actually strengthened the veracity of the output (the garbage in/garbage out theory – although he had more colorful way of describing it at the time). I think a lot of modelers fall into a similar trap as me and my classmates first did – instead of simplifying their business to a reasonable and manageable number of inputs and variables, they attempt to put every complexity of their company into the model.
In mathematical terms here’s what I’m referring to: Take one variable V that you have 80% confidence in.
Break that variable into 3 sub-variables – A, B, C – each of which you have 90% confidence in.
Since your confidence in your original variable (V = 80%) is greater than the product of the three sub variables (A*B*C = 73%) you are actually better off sticking to the simpler variable even though you have less confidence in it than in the sub variables individually.
There’s a balance here that is important to strive for because financial models need to be sufficiently detailed as to accurately reflect the business, be able to run realistic sensitivity analysis on, etc. However if you end up with a 10MB model for your start-up (and I’ve seen these), you’ve probably gone too far.
Here are a couple of specific thoughts:
– Before you start modeling list out the key drivers of your business – really distill what the key assumptions are and make sure you call these out in your model
– Add detail where it really helps – a lag from bookings to revenue reflect what is really going
on in your business – that’s good; deciding on an employee by employee basis what various raises are to be in year 4 doesn’t add much (simplify this assumption)
– Break out your assumptions – be explicit about the drivers of the business and group them together (perhaps at the top of each page) so that a reviewer can easily see what each of the drivers are
– Don’t hide assumptions within formulas – formulas should be driven off of numbers that are exposed,
not contained within the formula cell
– Be clear (by color coding or some other mechanism) what cells are assumptions (i.e., you
input a specific number) vs. derived from other cells
– Don’t be afraid to make general assumptions where the detail doesn’t really add value to your
model – for instance on T&E load for employees (I’ve seen many models with 3 tabs to try to calculate things such as year 6 cell phone use per employee)
I could keep going, but I think you get the picture. Perhaps more important than anything else, don’t forget to step back from the model when you’re done and look at the macro trends that you are predicting. Does the revenue ramp make sense? Do the revenue and expense totals per employee seem reasonable and do they grow (or shrink) logically? Are variables such as your days receivables and payables in the ball-park? Are your working capital assumptions generally reasonable?
More variables and assumptions are perhaps not the key to better modeling – smarter and more well thought out ones are.