We at Sokrates advisors conduct strategic due diligence for private equity buyers. The core of it lies in predicting the future performance of a private company. Strategic due diligence is a forward-looking statement, in other words, a prediction, a forecast.
To understand why the forecast is the very essence of a strategy due diligence, let’s step back a hundred years. About a century ago the economist Frank Hyneman Knight made a careful distinction between uncertainty and risk. In his doctoral thesis published in 1921 under the title Risk, Uncertainty, and Profit, he defined “risk” as “quantity susceptible of measurement”. Risk, he wrote, “is not in effect an uncertainty at all”. Risk is measurable while uncertainty is “unmeasurable”.Frank Knight went on to become one of the founders of the Chicago School of Economics and to teach economics to a few future Nobel Laureates including Milton Friedman, George Stigler and James Buchanan.
The distinction between risk and uncertainty is indeed fundamental to economic life: risky events (or assets) can be expressed in probabilistic terms, uncertain events cannot. Risk can be measured as a probability distribution, uncertainly cannot. Since risk can be priced it is possible to buy insurance against risk either from a market-based source or with company funds in the form of a hedge. Uncertain events are harder (or impossible) to price or to insure. Risk and uncertainly are, essentially, events and assets of a different nature.
But there is another way to look at this distinction. We can see it as relationship between knowledge and future events. Whether an event or an asset is seen as risky or uncertain depends on our current knowledge. This implies that by building and accumulating knowledge, we can move events and assets from the universe of uncertainty to the realm of risk. Or, if we see “uncertainty-to-risk” as a continuum, we can indeed move the needle toward quantifiable risk.
This is precisely what we try to achieve when we conduct strategic due diligence for private equity buyers. We move the needle of knowledge from uncertainty to measurable risk. In this effort, the most essential tool is the forecast. If we have to reduce the due diligence forecast to its simplest expression, we essentially predict the sales and the EBITDA of the target company, with a five-year horizon. So this is simply it: two numbers and a timeline.
The quality of the forecast will move the needle of knowledge or not. The predictive reasoning leading to the forecast has three key steps:
- Analyze historic data to uncover past pattern that accurately describe past market trends and company trajectory. Although you have to be ready for big data, the data does not necessarily have to be “big” to uncover past patterns. One thing to keep in mind is that the CAGR is not a pattern (more on this, some other time perhaps).
- Examine the current state of affairs in order to detect changes and patterns that could modify or reaffirm historic patterns. For instance, you could look for inflexion or tipping points that may suggest a future that is different from the past. For a due diligence practitioner, this is perhaps the hardest and the trickiest part.
- Express your forecast in probabilistic terms in order for it to be measurable. Making a measurable forecast means that we:
- express the forecast in numbers (like sales and EBITDA),
- formulate probabilistic parameters, like confidence intervals, probability distributions, etc.,
- define a specific timeframe.
Although this last step sounds less exciting for the prospective buyer, it is in fact essential for two reasons. First, if you cannot express future events in probabilistic terms, it means that you are still lingering in the universe of uncertainty. You are not in the realm of risk. Second, the forecast’s accuracy can actually be measured at the end of the time horizon. For us as due diligence practitioners this is an objective and powerful feedback that help us improve our predictive framework and forecasting ability.
Note: our predictive framework is broadly based on academic research and literature on the topic. For a short reading, cf. On the psychology of prediction, Daniel Kahneman, Amos Tversky, Psychological Review, 1973, Vol. 80 No 4
For a longer read, check the excellent book: Superforecasting, the art & science of prediction, Philip Tetlock and Dan Gardner, Random House, 2015
This article was originally published as a blog post on our corporate web site: http://www.sokrates-advisors.com