An extension to the classical mean–variance portfolio optimization model. Issue 3 (3rd July 2019) - Ötken, Çelen N.; Organ, Z. Batuhan; Yıldırım, E. Ceren; Çamlıca, Mustafa; Cantürk, Volkan S.; Duman, Ekrem; Teksan, Z. Melis; Kayış, Enis
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.