Forecasting

Forecasts have a legitimate place in a comprehensive company analysis and in determining the true value of a business. Through a high-quality research, decision-making and evaluation process, the likelihood of a true understanding of the laws of business is greatly increased, which in turn reduces the likelihood of bad investment decisions that are likely to prove costly sooner or later.

Before delving into the actual forecasting process and its pitfalls, it’s sensible to recall the fact that forecasts are never foolproof data and worth anchoring to, but in the end only and only best guesses based on current information. 

Three key elements in the forecasting process

Since forecasting as an independent exercise is an assessment of the development and direction of a company's business, the most logical starting point for the forecasting process is the business model. The in-depth dissection of the earnings and value creation model helps the forecaster to answer questions fundamental to a true understanding of the activity, and I think this part of the process can be divided into three complementary parts.

Of course, the actual forecasting model is also worth noting in terms of process flow, but given the technical nature of model construction, I do not consider it particularly important to discuss it here.

Review of revenue streams, cost items and capital needs

The review of revenue streams, cost items, cash flows, and capital requirements culminates at a practical level in the nature of the service, application or overall solution that the company provides, the cost structure and the dynamics between these and annual investment needs.

Allow me to offer a concrete—if somewhat black-and-white—example of this dynamic, and especially of how to visualize it. Firstly, we have an industrial equipment manufacturing business operating under fixed capacity constraints and with potential economies of scale in production and, secondly, a capital-light software business operating with relatively low variable costs.

For the former, growth beyond a certain level usually requires front-loaded capital investments that generate cash-flow with delay. For the latter, similar capacity investments, reflecting the earnings model, go into personnel costs and are realized on either side of the expected return at a much faster pace. If this basic logic between different business models were for some reason overlooked by the forecaster, both the earnings and balance sheet forecasts and the view of the quality of the business might appear too optimistic.

Assessment of strategic choices made, historical performance, current value chain position and the forces influencing these

In turn, an assessment of the strategic choices made, the current value chain position and the forces influencing these will help the forecaster to better understand, e.g., the company's core operational activities, their underlying industrial logics and the verticality of value creation (i.e. the value chain choices made, such as in-house manufacturing or outsourcing) and the company's bargaining power in both directions of the value chain.

In my experience and on a concrete level, weighing up these points helps to understand the history of the company and thus the current situation, providing nice support for assessing possible future developments. Reflecting the above, I think it is essential in this part of the process to also take into account the first set of industry-level and hence competitive variables (e.g. industry demand drivers, the set of competitors and their actions), because since strategic choices are neither made nor taken in a vacuum, assessing their impact in such an environment is not in any way meaningful.

Competitive advantages and their sources

We have written a separate article on competitive advantages and their sources and deep into that in the coming section.

Pitfalls in forecasting

Since forecasting, as suggested in the introduction, is both an art of best guesses based on current information and driven by human activity, it’s obvious that the process is fraught with a large number of pitfalls or, more familiarly put: psychological delusions. From my own observations, the strongest of these are related to over-optimism, a sense of control or outright illusion, information overload, seeking validation and anchoring.

Optimism and its dangerous big brother, over-optimism, are undeniably inherent human traits. While optimism is desirable when it comes to life and the pursuit of happiness, the opposite is true when it comes to assessing the direction of business and particularly when creating sufficient safety margins. In forecasting, over-optimism manifests itself in overestimating one's own abilities. This rather dull and often initially unconscious feature is in a sense inflated by the illusion that variables that are completely outside one's control are somehow controllable by the person sitting in front of the screen. Based on my observations, this control bias takes a particularly strong form in situations where there is a large mass of options for the variables to be predicted or selected. It’s therefore quite understandable that the illusion of control is particularly present in the assessment of the future of business and its multivariate environment.

Next on the list is information overload. The amount of information available today is gargantuan. Information has become more detailed than in past decades, or at least it seems like that. Intuitively, this could be linked to a better prediction process and thus a more accurate outcome. However, based on the research references I have read (e.g. C. Tsai, J. Kalyman & R. Hastie, "Effects of Amount of Information on Judgement Accuracy and Confidence") and sometimes harsh personal experience, the abundance of information has only falsely led to increased self-confidence instead of improved reasoning ability.

This has led to excessive optimism, a sense of control that can even be dismissed as utopian, and a shift from rational to emotional decision-making. Thus, to some extent, ignorance is bliss and too much is too much when it comes to information. However, it is not at all necessary to enter the information fallacy, as with other thought traps. The first step in avoiding this fallacy is to recognize and acknowledge it. However, I understand that the best way to escape the fallacy definitively is to limit the focus to what is really relevant to the decision-making process.

Confirmation seeking or confirmation bias, as the name implies, means that in the forecasting process, the forecaster seeks and applies only those pieces of information that confirm and support the decision that has already been made. In the world of equity research, a good example of this is where new information about a business that was previously considered at least valid, such as a new product launch or a key employee hire, is interpreted as being larger than its true size and this information is used to confirm an existing view. Against this background, the confirmation bias is also quite strongly linked to anchoring, which in the context of forecasting (and in simplified terms) refers to both the lock-in to old forecasts and the conscious or unconscious rejection of information that might change the prevailing view.

Combating both the confirmation bias and the anchoring bias requires, to loosely paraphrase the late philosopher Karl Popper, a great deal of skepticism and an active search for arguments against one's own hypotheses. Done properly, this exercise is laborious and disciplined, but I believe very rewarding in terms of its expected long-term benefits.