Complex portfolio selection via convex mixed‐integer quadratic programming: a survey

In this paper, we review convex mixed‐integer quadratic programming approaches to deal with single‐objective single‐period mean‐variance portfolio selection problems under real‐world financial constraints. In the first part, after describing the original Markowitz's mean‐variance model, we anal...

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Veröffentlicht in:International transactions in operational research Jg. 26; H. 2; S. 389 - 414
Hauptverfasser: Mencarelli, Luca, D'Ambrosio, Claudia
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Blackwell Publishing Ltd 01.03.2019
Wiley
Schlagworte:
Computer Science
Constraint modelling
convex MINLP
Convexity
Empirical analysis
exact methods
mixed‐integer quadratic programming
Operations Research
Portfolio management
portfolio selection
Probabilistic models
Quadratic programming
robust and probabilistic optimization
Variance analysis
ISSN:0969-6016, 1475-3995
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Zusammenfassung:In this paper, we review convex mixed‐integer quadratic programming approaches to deal with single‐objective single‐period mean‐variance portfolio selection problems under real‐world financial constraints. In the first part, after describing the original Markowitz's mean‐variance model, we analyze its theoretical and empirical limitations, and summarize the possible improvements by considering robust and probabilistic models, and additional constraints. Moreover, we report some recent theoretical convexity results for the probabilistic portfolio selection problem. In the second part, we overview the exact algorithms proposed to solve the single‐objective single‐period portfolio selection problem with quadratic risk measure.
Bibliographie:ObjectType-Article-1
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content type line 14
ISSN:0969-6016
1475-3995
DOI:10.1111/itor.12541