Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/246426 
Year of Publication: 
2020
Citation: 
[Journal:] Operations Research Perspectives [ISSN:] 2214-7160 [Volume:] 7 [Publisher:] Elsevier [Place:] Amsterdam [Year:] 2020 [Pages:] 1-11
Publisher: 
Elsevier, Amsterdam
Abstract: 
This study presents an improvement to the mean-variance portfolio optimization model, by considering both the integer transaction lots and a robust estimator of the covariance matrices. Four robust estimators were tested, namely the Minimum Covariance Determinant, the S, the MM, and the Orthogonalized Gnanadesikan-Kettenring estimator. These integer optimization problems were solved using genetic algorithms. We introduce the lot turnover measure, a modified portfolio turnover, and the Robust Sharpe Ratio as the measure of portfolio performance. Based on the simulation studies and the empirical results, this study shows that the robust estimators outperform the classical MLE when data contain outliers and when the lots have moderate sizes, e.g. 500 shares or less per lot.
Subjects: 
Finance
Markowitz portfolio
Transaction lots
Robust estimation
Genetic algorithm
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.