Corporate Valuation. Massari Mario

over the next years, to do the following:

      ● Consolidate its positioning on the European market, within which it is among the major players.

      ● Increase its presence in the Canadian and U.S. markets.

      The next paragraphs will offer an overview of the main assumption formulated by Kappa management to develop its business plan (which spans a three-year time horizon).

      Assumptions on the Revenues

      Based on Kappa reference sector, revenues forecast have been developed on the basis of these expectations:

      ● Revenues linked to already-earned orders. Kappa has indeed a backlog of already-earned commissions, on which it is already working and already subjected to a final contract. Generally, the contracts signed by Kappa envisage fixed remuneration, whose payment is split in multiple tranches, based on the percentage of completion of the work. Moreover, the contracts precisely state the timeline of work completion status; as a consequence of this, there is usually a great visibility around both the quantum and the temporal distribution of revenues.

      ● Revenues from the new orders under acquisition/auction. In this respect, it is important to distinguish among:

      ● Orders for which negotiation with the counterparty is in already advanced phase. In this case, there is good visibility around nature of the activity to carry out and quantum of the payments. In some cases, there can also be some commercial offers already submitted.

      ● Orders for which negotiation with the counterparty is in initial phase. In these cases, the nature of the activity to carry out is known only partially, as is the compensation amount.

      ● Orders for which negotiation with the counterparty has not been initiated yet. In these cases, both the activity type and the compensation amount are not visible at all, but can just be hypothesized.

      Clearly, the trustworthiness of the assumptions carried out on the revenues varies as a function of the commission under consideration: high in the first case above, very low in the third one. Expected revenues linked to future potential commissions have been weighted by the probability of winning the commissions.

      ● Revenues from the orders that Kappa management thinks the company will win in the future. This is based on its past experience and knowledge of market dynamics.

Exhibit 2.17 shows the composition of the revenues expected over the time horizon covered by the business plan.

Exhibit 2.17 Expected revenues composition over business plan horizon

      Assumptions on the Direct Costs

      Kappa management has identified, in particular, three types of direct costs:

      1. Labor costs of internal staff: specialized digital technicians working for Kappa under an exclusivity agreement

      2. Labor costs of external staff: specialized digital technicians working for Kappa with no exclusivity obligations

      3. Services costs: specialized activities that Kappa outsources to other companies

      Having said this:

      ● In the case of already-won orders, the company has carried out detailed costs budgeting activity and, as such, there is great visibility around the relevance of the expected costs and of their temporal distribution.

      ● In the case of the orders under auction, the degree of precision of the estimated and, consequently, of their trustworthiness, is a function of how advanced the auction/negotiation with the counterparty is.

      ● In the case of the orders that Kappa management believes the company will likely win, direct costs have been assumed equal to a fixed percentage of revenues (specifically, 70 percent).

      Over the business plan years, the percentage incidence of direct costs over total revenues is equal to 68 percent (t1) and 70 percent (years t2 and t3).

      1

      One of the limitations of such models is the assumption of normal distributions of results. In such a case, the highest probable expected output is the average output. Quite often, though, prospective results are mutually exclusive, thus making expected “average” results unlikely. The very idea of average thus loses its significance.

      2

      Irving Fisher, The Rate of Interest (New York: Macmillan 1907) and The Theory of Interest (New York: Macmillan 1930).

      3

      It's well known that the different phases of the life cycles of firms and industries are characterized by different levels of uncertainty. Scholars are of the opinion that there exi

1

One of the limitations of such models is the assumption of normal distributions of results. In such a case, the highest probable expected output is the average output. Quite often, though, prospective results are mutually exclusive, thus making expected “average” results unlikely. The very idea of average thus loses its significance.

2

Irving Fisher, The Rate of Interest (New York: Macmillan 1907) and The Theory of Interest (New York: Macmillan 1930).

3

It's well known that the different phases of the life cycles of firms and industries are characterized by different levels of uncertainty. Scholars are of the opinion that there exists a stable and demonstrated relationship among the risk profile type of industry and life cycle of the very same industry. In the initial phases of the cycle (so-called “introduction” and “development”), the risks are particularly elevated, with the consequent possibility of huge losses of invested capital. In general, in the innovative sectors uncertainty and the difficulty of forecasting are well-known risk factors.

4

The most widely used models are the Porter five forces – Michael Porter, Competitive Strategy (New York: The Free Press, 1980) – and the strategies and competitive advantages matrices – M. Porter, Competitive Advantage: Creating and Sustaining Superior Performance (New York: The Free Press, 1985).

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