.png){kind=icon}
.png){kind=icon}
Return
Complex portfolio selection via convex mixed-integer quadratic programming: a survey
.png){kind=icon}
OA
AI
DOI：10.1111/itor.12541.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
portfolio selection
mixed-integer quadratic programming
convex MINLP
robust and probabilistic optimization
exact methods
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2.9
Papers:
1.8K
Citations:
3.7K