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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.
Additional information
Notes on contributors
Çelen N. Ötken
Çelen N. Ötken received a B.Sc. in industrial engineering from Ozyegin University in 2019. Currently, she is working as a data science project specialist at Ozyegin University.
Z. Batuhan Organ
Zeynel Batuhan Organ received a B.Sc. in industrial engineering with a minor in computer science from Ozyegin University in 2018. Since graduation, he has been working as a data scientist in Invent Analytics.
E. Ceren Yıldırım
Elif Ceren Yıldırım received a B.Sc. in industrial engineering from Ozyegin University in 2018. Currently, she is working as a business intelligence lead in Vodafone Turkey.
Mustafa Çamlıca
Mustafa Çamlıca graduated from Ozyegin University in 2018 with a B.Sc. in industrial engineering. Currently, he is a master’s student at Istanbul Technical University in the Department of Industrial Engineering and is working as a data analytics assistant specialist in Borusan Makina–Caterpillar.
Volkan S. Cantürk
Volkan S. Cantürk graduated from Ozyegin University in 2019 with a B.Sc. in industrial engineering and a minor in hotel management. He is currently working as an operator marketing specialist at Unilever Food Solutions.
Ekrem Duman
Ekrem Duman obtained his B.Sc. in electrical and electronics engineering from Bogazici University and M.Sc. and Ph.D. in industrial engineering from the same university. Currently he works as a professor in the Department of Industrial Engineering at Ozyegin University. In addition to theoretical studies, he is very interested in applications and has been involved in many industrial (mostly banking) projects as a coordinator or consultant.
Z. Melis Teksan
Zehra Melis Teksan received her B.Sc. and M.Sc. in industrial engineering from Bogazici University and her Ph.D. in industrial and systems engineering from the University of Florida. Currently she works as an assistant professor in the Department of Industrial Engineering at Ozyegin University. Her main research focus lies in the field of production planning and inventory theory.
Enis Kayış
Enis Kayış received B.Sc. degrees in mathematics and industrial engineering from Bogazici University, an M.Sc. in statistics, and a Ph.D. in management science and engineering from Stanford University. Currently he works as an assistant professor in the Department of Industrial Engineering at Ozyegin University. His current research interests include data-driven decision making and business analytics with applications in supply chain management and health care operations.