Mean–Gini analysis in R&D portfolio selection

Mean–Gini analysis in R&D portfolio selection 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png European Journal of Operational Research Elsevier

Mean–Gini analysis in R&D portfolio selection

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Publisher
Elsevier
Copyright
Copyright © 2002 Elsevier B.V.
ISSN
0377-2217
eISSN
1872-6860
DOI
10.1016/S0377-2217(02)00708-7
Publisher site
See Article on Publisher Site

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.

Journal

European Journal of Operational ResearchElsevier

Published: Apr 1, 2004

References

  • Portfolio Selection Using Stochastic Dominance Criteria
    McNamara, J.R
  • Mean–Gini portfolio theory, and the pricing of risky assets
    Shalit, H; Yitzhaki, S

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