Mean-Gini analysis in R&D portfolio selection

Abstract To date no single model has been published which fully satisfies the needs for a practical R&D project selection technique. Some earlier models cannot handle risk well, while others do not provide efficient portfolios. This paper will present a model, adapted from the literature of financial portfolio optimization, which provides a practical means of developing preferred portfolios of risky R&D projects. The method is simple and highly intuitive, requiring estimation of only two parameters, the expected return and the Gini coefficient. The Gini coefficient essentially replaces the variance in the two-parameter mean–variance model and results in a superior screening ability. The model that we present requires estimates of only these two parameters and, in turn, allows for relatively simple determination of stochastic dominance (SD) among candidate R&D portfolios. We apply our model to a simple artificial five-project set and then to a set of 30 actual candidate projects from an anonymous operating company. We demonstrate that we can determine the stochastically non-dominated portfolios for this real-world set of projects. Our technique, appropriate for all risk-averse decision makers, permits R&D managers to screen large numbers of candidate portfolios to discover those which they would prefer under the criteria of SD.