Conditional stochastic dominance in R&D portfolio selection

This paper describes a methodology for the selection of research and develo pment (R&D) projects to add to or remove from an existing R&D portfolio, Th e analysis uses the criterion of conditional stochastic dominance to make s election recommendations. This criterion takes into account the effect of a given project on the risk and return of the existing portfolio. We use a m ethodology previously employed to analyze stock portfolios; however, we app ly it using simulation in an R&D portfolio context. We apply the methodolog y to the portfolios of two actual companies and find that it generates prio rities very close to those developed by internal company heuristics, We con clude that this methodology can be applied appropriately in these circumsta nces and that its recommendations are consistent with observed derision mak er behavior. Our results suggest that an R&D manager should not consider pr oject selection decisions in isolation, but, following this methodology, sh ould take into account the context of the existing portfolio.