Corporate Valuation - Massari Mario

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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.).

      Average Uncertainty and Flexibility

      In Area D in Exhibit 1.4 fall the situations that form the background of an evaluation: the scenarios can be credibly delineated and it is likely that management can take the necessary steps or seize the opportunity offered by change.

      With regard to the situations referred to in Area C of Exhibit 1.4, change does not arise as a disruptive and intermittent phenomenon, but can lead back to the observable dynamics of the present.

      As an example, in the valuation analysis of an important business in the spirit industry, the team doing the business analysis had described two scenarios. The first assumed decreasing sale volumes, consistently with the life cycle of a mature industry as observed in other firms within the same industry. The second one assumed instead, due to the strong brand value, a constant sale volume not affected by the general trend in the industry.

      Given the notoriety of the brand and the strength of the commercial network of the business, it was unlikely to assume that management would have reacted passively to a reduction in sales. More realistically, it would have differentiated the products between the traditional ones and the new ones (“white” spirits, etc.).

      Therefore, considering the operational flexibility permitted by the strength of the brand, the unfavorable scenario was modified assuming, after an initial decrease in sales, a return to the original levels with slightly lower margins given the increase in advertising costs.

      The example shows a typical process of financial analysts, which translates into an upgrading of expectations in comparison to the industry due to the strength points of the business that confirm the hypothesis that the management can effectively react to unfavorable market conditions.

      Obviously, the opposite reasoning also holds: when the firm under valuation is weaker than competitors, the average industry expectations can be modified and generate worse scenarios.

      Growth opportunities for firm Alpha can also fall in Area D of Exhibit 1.4. Entering the business of packaging for cosmetic products and offering new services to pharmaceutical companies are a natural evolution of the core business of Alpha. Alpha has in fact adequate technological and managerial resources to sustain growth in those businesses which are similar to the niche in which it already has a leadership position.

      1.5.2 Some Conclusions on Uncertainty and Managerial Flexibility

      The approach outlined in the previous paragraph departs from traditional analysis since it tries to contemplate whether, with different scenarios, the firm's business model can be adapted to new assumptions and what those assumptions imply in terms of the creation of value.

      From a historical standpoint, the first attempts to assess managerial flexibility, when future opportunities in the evolution of a business exist, have concerned themselves with R&D investments, brand and patent

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