Interactive portfolio selection involving multicriteria sorting models

• Published in: Annals of operations research Vol. 325; no. 2; pp. 1169 - 1195
• Main Authors: Tlili, Ali, Khaled, Oumaima, Mousseau, Vincent, Ouerdane, Wassila
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.06.2023; Springer; Springer Nature B.V; Springer Verlag
• Subjects: Algorithms; Analysis; Business and Management; Cognitive tasks; Combinatorics; Computer Science; Decision making; Learning; Multiple criterion; Operations Research; Operations Research/Decision Theory; Original Research; Portfolio management; Preferences; Technology application; Theory of Computation; France; SAT/MaxSAT; Portfolio selection; Preference learning; Multicriteria decision; Non-compensatory sorting
• ISSN: 0254-5330; 1572-9338
• DOI: 10.1007/s10479-022-04877-z

Given a set of items, the portfolio selection problem involves selecting a subset of the items subject to resource constraints. We propose in this paper a multiobjective interactive approach based on a constrained Non-Compensatory Sorting model which integrates preferences both on items and portfolios in the same device. More precisely, we combine two evaluations models. The first one assigns items into two categories ( Good / Bad ) and model resource limitation using weighted cardinality constraints in such a way that the portfolio is composed of the items assigned to the good category. The second evaluation level compares portfolios on a set of portfolio-related criteria. We learn the constrained sorting model, based on a learning set, with SAT/MaxSAT language, which proves to be efficient for the preference learning task.