of the business model to new scenarios
If we want to frame the issue in general terms, the valuation boundaries are determined – with regard to the choice of the correct standpoint – by two main factors:
1. The level of uncertainty, which characterizes the estimate measured as the impact that information unavailable at the time of the valuation can have on the valuation result itself; or, in other words, how far it is from the idea of probability based on the repetition of past results
2. Managerial flexibility – that is, how much the business model allows management to handle unfavourable scenarios or pick new opportunities in favorable situations
Exhibit 1.4 presents a graph that permits us to frame the context that drives the valuation with respect to the degree of uncertainty and management flexibility.
Exhibit 1.4 Valuation framework as a function of uncertainty and managerial flexibility
Limited Uncertainty and Flexibility
In Exhibit 1.4, area A identifies situations in which the frame of reference of the estimate is delineable in clear terms and the business model does not permit significant room to manoeuvre. A typical example is the business of gas and electricity grids; in these business areas, results emerge from a model in which relationships between macroeconomic variables, tariffs, transported volumes, and costs are definable with a close approximation and can be consistently projected in the future with a high degree of credibility.
Uncertainty factors consist of the evolution of energy consumption that, as is well known, is a function, in the short term, of climate factors and, in the long term, of the general trend of the industry as a whole; changes in industry regulations; and the intensity of competitive pressure from the supply side.
In these businesses, shifts in consumption translate directly into operating margin decreases/increases since the cost structure is extremely rigid and management has very limited flexibility to keep up with unfavorable trends in demand.
In the previously sketched framework, the representation of uncertainty is consistent with the assumptions generally adopted by finance textbooks. In particular, it is possible to forecast different scenarios and to expect credibly that realized results of the business will fall in between the two most extreme cases (the most and the least favorable).
To keep the analysis simple, analysts in general limit themselves to just three scenarios (optimistic, the most probable, and pessimistic). Therefore, uncertainty can effectively be depicted by means of a triangular distribution.
In the case of public utilities, the gap between scenarios is generally quite small; in other industries, the gap can significantly widen. Generally speaking, the scenario expected in average conditions is also the most likely to happen. Exhibit 1.5 graphically depicts the point.
Exhibit 1.5 Moderate uncertainty scenario
In the framework similar to Exhibit 1.5, it is not unusual for analysts to work out only the most probable scenario5 with respect to cash flow projections.
High Uncertainty and Limited Flexibility
Area B in Exhibit 1.4 identifies those situations in which information useful to assess the performance of a business is not available at the time of the valuation, and flexibility to manage unfavorable events or to improve favorable ones is very limited.
For example, a company in the waste management industry had assumed the construction of a new landfill in its business plan. The project kickoff, though, was under litigation with the environmental groups that opposed the project, despite the fact that set-aside for dumping was a part of a regional plan.
The legal experts had identified a negligible risk of abandoning the project.
In similar situations, the following procedure could be adopted that has the merit of highlighting the risk profile of the venture:
● Delineate the scenarios (in our case, accomplishment of the dumping or abandonment of the venture).
● Calculate the net present value for each of the scenarios.
The procedure described has unquestionable effectiveness in terms of information transparency: it avoids the assessment of an “average” result (the mathematical average of two different scenarios) because this “average” event cannot, by definition, take place.
An example can clarify the idea. The existing landfill can generate returns equal to 400 per year, in the most probable scenario. The construction of the new facility can generate additional returns of 1,200. The total expected returns if the project is completed are therefore 1,600. Yet, the probability of making the second facility is 50 percent. Exhibit 1.6 depicts the situation.
Exhibit 1.6 High uncertainty scenario
One can see that the representation is very different than that presented in Exhibit 1.5. In this case, the uncertainty framework is closer to a coin toss: as a matter of fact, either you get the favorable scenario or the unfavorable one.
In valuating businesses, similar situations are rather frequent and involve:
● The valuation of start-ups, of ventures in the initial phases of their life cycle, and of innovative businesses
● The valuations with specific risk characters (e.g., license or contract renewals, environmental risks, strategic supplier dependence, high customer concentration, dependence on key persons)
High Uncertainty and Flexibility
Area C in Exhibit 1.4 depicts situations in which high uncertainty is accompanied by a wide range of managerial choices, which can, consequently, open new scenarios (in other words, some scenarios are extremely management decisions–related, decisions that can be the response to alternative scenarios).
Going back to the public utilities case, many analysts have approached the valuation of energy distribution firms by estimating the value of the growth opportunities offered by the option of using the commercial network to offer different services to the final users.
Given the uncertainty associated with such initiatives, it is reasonable to assume that a multiservice business model can be developed using a step-by-step process: the firm can, in an early stage, offer just services related to the core business (e.g., combine the energy distribution with the sale activities, installation and maintenance of home appliances), to further expand into a wider range of services in case of success of the trial phase (in-house insurance, consumer credit services, etc.).
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