Multiobjective approach to portfolio optimization in the light of the credibility theory

Fernando Garcia, Jairo González-Bueno, Francisco Guijarro, Javier Oliver, Rima Tamošiūnienė

Research output: Articles / NotesScientific Articlepeer-review

19 Scopus citations


The present research proposes a novel methodology to solve the problems faced by investors who take into consideration different investment criteria in a fuzzy context. The approach extends the stochastic mean-variance model to a fuzzy multiobjective model where liquidity is considered to quantify portfolio’s performance, apart from the usual metrics like return and risk. The uncertainty of the future returns and the future liquidity of the potential assets are modelled employing trapezoidal fuzzy numbers. The decision process of the proposed approach considers that portfolio selection is a multidimensional issue and also some realistic constraints applied by investors. Particularly, this approach optimizes the expected return, the risk and the expected liquidity of the portfolio, considering bound constraints and cardinality restrictions. As a result, an optimization problem for the constraint portfolio appears, which is solved by means of the NSGA-II algorithm. This study defines the credibilistic Sortino ratio and the credibilistic STARR ratio for selecting the optimal portfolio. An empirical study on the S&P100 index is included to show the performance of the model in practical applications. The results obtained demonstrate that the novel approach can beat the index in terms of return and risk in the analyzed period, from 2008 until 2018.

Original languageEnglish
Pages (from-to)1165-1186
Number of pages22
JournalTechnological and Economic Development of Economy
Issue number6
StatePublished - 17 Nov 2020
Externally publishedYes


  • Credibilistic STARR ratio
  • Credibilistic Sortino ratio
  • Evolutionary multiobjective optimization
  • Fuzzy portfolio selection
  • Mean-CVaR-li-quidity
  • Mean-semivariance-liquidity
  • Trapezoidal fuzzy numbers


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