An extension to the classical mean–variance portfolio optimization model
Author
Type :
Article
Publication Status :
Published
Access :
restrictedAccess
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.
Source :
Engineering Economist
Date :
2019-07
Volume :
64
Issue :
3
Publisher :
Taylor & Francis
URI
http://hdl.handle.net/10679/6709https://www.tandfonline.com/doi/abs/10.1080/0013791X.2019.1636440
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