Sparse and robust portfolio selection via semi-definite relaxation

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In investment management, especially for automated investment services, it is critical for portfolios to have a manageable number of assets and robust performance. First, portfolios should not contain too many assets in order to reduce the management fees, transaction costs, and taxes. Second, portfolios should be robust as investment environments change rapidly. In this study, therefore, we propose two convex portfolio selection models that provide portfolios that are sparse and robust. We first perform semi-definite relaxation to develop a sparse mean-variance portfolio selection model, and further extend the model by using -norm regularization and worst-case optimization to formulate two sparse and robust portfolio selection models. Empirical analyses with historical stock returns demonstrate the effectiveness of the proposed models in forming sparse and robust portfolios.
Publisher
TAYLOR & FRANCIS LTD
Issue Date
2019-08
Language
English
Article Type
Article; Early Access
Citation

JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY

ISSN
0160-5682
DOI
10.1080/01605682.2019.1581408
URI
http://hdl.handle.net/10203/263987
Appears in Collection
IE-Journal Papers(저널논문)
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