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In a traditional approach, closer to financial modeling than strategic analysis, estimate of value stems from a passive attitude toward risk. Yet, we have observed that in reality businesses are by far more articulated: in fact, up to a certain level, the phenomena of change can be managed or turned to one's favor through opportune intervention.

1.5.1 Static versus Dynamic Assumption

As a first step, when facing the valuation of an investment plan, acquisition, or firm, it is worth asking which standpoint should be adopted:

● A static view assuming that the current business model will continue to work as it is

● A dynamic view which takes into account the adaptation 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.).

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 acquisition, development of new technologies, and the research and exploitation of natural resources. These attempts assume as a starting point the explicit representation of a firm's results as a consequence of managerial decisions expected to be taken in the future. Generally speaking, these methodologies today fall into the field of so-called real option valuation (ROV).6

In this framework, the value of a project is just the sum of two elements:

1.5.3 Valuing Companies Assuming a Dynamic Standpoint

The dynamic approach was first used in valuations of Internet companies and Internet stocks.

Looking at the background in which the so-called new economy– related ventures originated at the end of the nineties, we can identify the typical characteristics referable to area C of Exhibit 1.4:

● The operators' expectations assumed strong growth in the market.

● A high degree of uncertainty characterized the assumptions about Internet users' behavior and technological evolution.

● Entry barriers on the market were modest: Internet companies could have adapted the business model to eventual shifts.

After the radical U-turn in scenarios and expectations with respect to high-tech stocks, in the first half of 2000, the dynamic valuation models have been harshly criticized. Despite many excesses, the euphoria that drove markets and the analysts has left us an important contribution: the models adopted and the debate that followed have brought attention, also out of the academic studies, to the fact that managerial flexibility can be an appreciable factor in the valuation of businesses and acquisitions, and not only in well-defined investment niches (R&D, natural resources search and development, etc.). On the practical side, the key issue of the dynamic approach is the need for restricting the analysis only to the credible development of a business model.

5

With asymmetric probability distributions, the most likely scenario is different from the expected scenario.

6

For introductory reading on applications of real options in capital budgeting and investment policies, see Chapters 1 and 2 of A. Dixit, R. Pindyck, Investments under Uncertainty (Princeton, NJ: Princeton University Press, 1994), and W. C. Kester, “Today's Options for Tomorrow's Growth,” Harvard Business Review, 60 (1984): 153–160. For an in-depth presentation of the real option applications, see L. Trigeorgis, E. S. Schwartz, Real Options and Investment under Uncertainty: Classical Readings and Recent Contributions (Cambridge: The MIT Press, 2001); N. Kulatilaka, M. Amram, Real Option: Managing Strategic Investment in an Uncertain World (Boston: Harvard Business School Press, 1999); T. Copeland, V. Antikarov, Real Options: a Practitioner's Guide (Texere, 2001). Though the real option approach is extremely insightful in terms of the logic underlying investment decisions, its real-world applications are limited; therefore this book will not cover the real option issue.