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An extension to the classical mean–variance portfolio optimization model

Author

Listed:
  • Çelen N. Ötken
  • Z. Batuhan Organ
  • E. Ceren Yıldırım
  • Mustafa Çamlıca
  • Volkan S. Cantürk
  • Ekrem Duman
  • Z. Melis Teksan
  • Enis Kayış

Abstract

The purpose of this study is to find a portfolio that maximizes the risk-adjusted returns subject to constraints frequently faced during portfolio management by extending the classical Markowitz mean–variance portfolio optimization model. We propose a new two-step heuristic approach, GRASP & SOLVER, that evaluates the desirability of an asset by combining several properties about it into a single parameter. Using a real-life data set, we conduct a simulation study to compare our solution to a benchmark (S&P 500 index). We find that our method generates solutions satisfying nearly all of the constraints within reasonable computational time (under an hour), at the expense of a 13% reduction in the annual return of the portfolio, highlighting the effect of introducing these practice-based constraints.

Suggested Citation

  • Çelen N. Ötken & Z. Batuhan Organ & E. Ceren Yıldırım & Mustafa Çamlıca & Volkan S. Cantürk & Ekrem Duman & Z. Melis Teksan & Enis Kayış, 2019. "An extension to the classical mean–variance portfolio optimization model," The Engineering Economist, Taylor & Francis Journals, vol. 64(3), pages 310-321, July.
    • Handle: RePEc:taf:uteexx:v:64:y:2019:i:3:p:310-321
    DOI: 10.1080/0013791X.2019.1636440
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