Optimal Investment Strategy in Pharmaceutical Research & Development
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Managers in the pharmaceutical industry must constantly make decisions regarding development of new drugs. They are exposed to high expectations and demands from both the government and the society with respect to battling diseases. At the same time, the managers are obliged to make wise economic decisions in order to survive financially. Currently, the profit margins in the industry are high, and pharmaceutical companies reinvest billions of dollars each year into research and development (R&D) projects to maintain these margins. However, a pharmaceutical R&D project is usually complex with several uncertainties related to it. Moreover, today's R&D projects are usually highly innovative and also exposed to stricter governmental regulations, resulting in numerous challenges for pharmaceutical companies. Thus, it has become more important than ever for these companies to manage their R&D investments as efficiently as possible. Still, most pharmaceutical companies use methods that do not account for uncertainty or an active management for valuation of their R&D projects. In this thesis, we consider a specific case study related to magnetic resonance imaging (MRI), which is provided by our collaborating partner, GEHC. This field is currently undergoing high R&D activity, and along with the general industry trend, today's MRI projects are innovative and at the same time exposed to many uncertainties. However, GEHC are also using the traditional valuation method. Our goal is to provide them with a framework incorporating more realistic aspects that will help them to make optimal investment choices throughout their R&D process. We create this framework by accounting for the uncertainties of the project at hand as well as the managers' flexibility with respect to reacting to events as they unfold. The framework is based on two real options models, i.e. a binomial model and a compound option model, incorporating the behaviour of both the project value and GEHC's active project management. Both the models are built around traditional pharmaceutical R&D phases, and are rather intuitive, which we believe to be important in order to get the management to rely on the results. The binomial model comprises the main real options features, and is the easiest to follow for the management. The compound option model is more advanced, but allows for additional realistic features, such as expansion options. Given that GEHC have no alleged prior knowledge of real options valuation, we believe that the binomial model is a good starting point. However, because the compound model provides an even more realistic project value and should be understandable for an interested actor, we suggest that the managers also use this model, at least for comparison.