STRATEGIC ANALYSIS OF FOREST INVESTMENTS USING REAL OPTION: THE FUZZY PAY-OFF MODEL (FPOM)

Abstract

This paper introduces a fuzzy forestry investment decision-making tool based. It will be applied to choose optimal levels of investment when three possible scenarios are conceived, a base case and two extreme alternatives: optimistic and pessimistic. Decision-makers can be seen as being either owners of a forest or investors. For each of these roles the possibility degrees of the scenarios may be represented by means of fuzzy numbers, representing ambiguous net present values (NPV). Real option values (ROV) are then computed based on them. The application to a potential forestry project in Argentina shows that, while in the case of an owner of forestry project the expected benefits are both positive under NPV and ROV, an investor would discard the project if she assumes equal weights for the scenarios in a traditional analysis but would accept it under the fuzzy approach.Keywords: fuzzy number, NPV (net present value), ROV (real option value)JEL Classification: G11,C65(ProQuest: ... denotes formulae omitted.)INTRODUCTIONThe forestry sector exhibits particularities that have recently attracted the interest of institutional investors. The appealing features of this industry are based on the tangibility of its assets, namely the land that sustains the forests, the standing timber as well as the associated milling facilities. In countries like Argentina, forestry is becoming increasingly important, not only in the economic arena but also for its social and environmental impact. That is, the productive side of the industry generates wealth but at certain social and environmental costs. For this reason decision-making in forestry requires solid management tools, to overcome the weaknesses of the established methods. In practice, they usually are very simplistic, to the point to lead to mistaken decisions. This is particularly true of financial tools as Net Present Value (NPV) assessments (Milanesi et al. 2012) and its variants, like Faustmann's formula (Bettinger, et al. 2009). Even their proponents point out the problems associated with their application in forestry contexts under time-varying conditions.As it is typical of highly uncertain and complex activities, the valuation of capital investment in the forestry industry should emphasize on a flexible management of resources (Carmona and Aranda 2003, Milanesi et al. 2012). This goal can be attained by resorting to the method of Real Options Valuation (ROV), which overcomes the weaknesses of conventional models (Milanesi et al. 2012) introducing strategic flexibility in the assessment of investment projects (Smit and Trigeorgis 2004). The method, based on the Black-Merton-Scholes model (Black et al. 1973, Merton 1973) characterizes the values of options as solutions of stochastic differential equations and can be modified and adjusted in different ways. For instance, it allows the selection of particular stochastic processes with a more complex structure of options.In its non-fuzzy version, ROV has been applied to forestry problems by Thomson (1992), Yin and Newman (1997) and Milanesi et al. (2012). The latter, in particular, applied the method to determine an optimal period of harvest. On the other hand, Petrasek and Perez (2010) assessed harvest contracts with American options. While other alternatives are possible, as in other areas, binomial models are a preferred choice for most of these applications (Trigeorgis 1995, Mun 2002). They may be either represented as having a grid or a tree-like structure (Brandao et al., 2005, Smith 2005). Alternatively, it can be captured by the certainty equivalents of implicit probability distributions (Rubinstein 1994).ROV, like all the assessments of risk can be modified introducing fuzzy concepts (Kahraman et al., 2002, Fuller and Majlender 2003). The formal approach replaces probabilities by possibilities (Zadeh 1965, Dubois and Prade 1980, Carlsson and Fuller 2001). There are three main ways in which this fuzzyfication of ROV can be carried out:a. …