Conditional stochastic dominance in R&D portfolio selection

This paper describes a methodology for the selection of research and development (R&D) projects to add to or remove from an existing R&D portfolio. The analysis uses the criterion of conditional stochastic dominance to make selection recommendations. This criterion takes into account the effect of a given project on the risk and return of the existing portfolio. The authors use a methodology previously employed to analyze stock portfolios; however, they apply it using simulation in an R&D portfolio context. They apply the methodology to the portfolios of two actual companies and find that it generates priorities very close to those developed by internal company heuristics. They conclude that this methodology can be applied appropriately in these circumstances and that its recommendations are consistent with observed decision maker behavior. Their results suggest that an R&D manager should not consider project selection decisions in isolation, but, following this methodology, should take into account the context of the existing portfolio