R ratio optimization with heterogeneous assets using genetic algorithm

Michael Stein, Svetlozar T. Rachev, Stoyan Stoyanov

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


This paper presents a framework to portfolio optimization that is superior to the mean-variance approaches utilized for asset allocation. We show how a portfolio with heavily differing asset types in various market phases can be managed efficiently by using a ratio-based portfolio optimization approach and provide a general solution to related optimization problems and the technical challenges arising from them. Portfolio optimization is done by using a modified version of the R ratio in a benchmark-free setting for real estate funds of funds (FoFs). We use a genetic algorithm to solve the non-quasi-convex optimization problem and propose the use of genetic algorithms for related ratio-based optimization problems. Our results show the appropriateness of both the modified R ratio and the genetic algorithm used to optimize the fund portfolios in the benchmark-free environment. The algorithm efficiently solves the non-quasi-convex type of problem and related approaches of portfolio optimization are outperformed by the R ratio focused approach.

Original languageEnglish
Pages (from-to)117-125
Number of pages9
JournalInvestment Management and Financial Innovations
Issue number3
StatePublished - 2009


  • Expected tail loss
  • Funds of funds
  • Genetic algorithm
  • Nonquasi-convex
  • Portfolio optimization
  • R ratio
  • Real estate funds


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