Warren Buffett famously said, “If you need to use a computer or a calculator to make the calculation (of value), you shouldn’t buy it (the company)”. Financial analysts on the other end of the spectrum make elaborately detailed models running through reams of excel sheet tabs. As with most things in life, the middle path is the one to tread upon.
Financial modelling simply for the sake of deriving an earning-per share (EPS) number for two years hence is an exercise that adds little value to long term investors. Yet, it would be unfair to dismiss the act altogether. Models don’t write themselves. The assumptions that help populate them are in fact guesses, hopefully educated ones, taken by the investor after having studied the business. In making those assumptions we demonstrate to ourselves, our understanding of the business as well the degree of confidence we have in putting in those numbers.
Instead of sticking to a financial template, constructing models on first principles (a.k.a the actual drivers of particular line items as understood by a business person) shed light on the pressure points in the business and tend to be a lot more useful.
Let’s take the case of a B2B diagnostics company in India. The first line item to model is revenue. Breaking down the revenue of this company we realised it is a function of the number of service providers (franchises), each of whom give a certain number of samples on which the company runs a certain number of tests at a given realisation per test.
The starting point of modeling has to be plotting these numbers historically and understanding patterns therein. When I plotted the numbers historically, it was evident that realisation per test and number of tests you can run on every sample are fairly inelastic numbers. The number of tests on every sample can improve marginally with a higher share of packages in revenue but that move would be gradual. Our focus as investors therefore simply became on how many service providers associated with the company and how many samples they got for each one of them. The questions therefore we asked of the management remained focused on understanding the payoff for a service provider vs. competitor and the incentive structure that made them strive for volumes.
As we slipped to the lines below in modeling this business out, it was surprising to note the resilience in gross margins with minimal or no movement in realisation per test. There we got our second line of questioning- how in an emerging country with positive inflation do we maintain and improve gross margins despite no price hikes? A chunk of our time went in understanding this piece of the business and plotting the future out for the same.
Remainder of the cost items displayed linear growth and were fairly easier to “predict” for us. A laborious exercise to detail these out would not add any value to a long term investor. This is a capital light business and therefore simpler to model given minimal gross block (capex) and working capital (quick cash conversion) requirements. Modeling for other businesses such as manufacturing would have other considerations as well.
A manufacturing company speaking of tripling their revenues in five years. While plotting their topline in conjunction with their gross block numbers reveals if in fact they do have capacity at hand to do so? If they don't, how much money do they intend to raise and spend on it? If they do, are their profits going to generate enough cash flow to fund incremental working capital requirements to fund that growth?
Financial modeling therefore should not be treated as a map leading to ascertaining the EPS two years hence. For long term investors, it can be a powerful compass helping us ascertain the direction of the business, acting as a mirror in case the business deviates from the same; prompting us to ask the right set of questions on the business.
(Harini Dedhia is Head of Research at Tamohra Investment Managers. Views are her own.) 
 "Financial modeling can serve as powerful business compass: Harini Dedhia")
