Mean-Gini Analysis in Project Selection

  • Samuel B. Graves
  • Jeffrey L. Ringuest
  • Andrés L. Medaglia
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 58)


Chapters 5 and 6 showed models for deciding which projects to add to or remove from an existing portfolio. These models, however, did not necessarily generate an efficient portfolio of projects. This chapter will describe a model, adapted from the literature of financial portfolio optimization, which provides a practical means of developing preferred portfolios of risky 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 among candidate project portfolios. We apply our model to a simple artificial 5-project set and then to a set of 30 actual candidate projects from an anonymous operating company (the same examples used in the previous chapter). We demonstrate that we can determine the stochastically nondominated portfolios for this real-world set of projects. Our technique, appropriate for all risk averse decision makers, permits managers to screen large numbers of candidate portfolios to discover those which they would prefer under the criteria of stochastic dominance.


Portfolio Selection Stochastic Dominance Efficient Frontier Nondominated Solution Project Selection 
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Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Samuel B. Graves
    • 1
  • Jeffrey L. Ringuest
    • 1
  • Andrés L. Medaglia
  1. 1.Boston CollegeUSA

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