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dc.contributor.authorKizys, Renatas-
dc.contributor.authorJuan Pérez, Ángel Alejandro-
dc.contributor.authorCalvet Liñan, Laura-
dc.contributor.authorSawik, Bartosz-
dc.contributor.otherUniversity of Portsmouth-
dc.contributor.otherAGH University of Science and Technology-
dc.contributor.otherUniversity of California, Berkeley-
dc.contributor.otherUniversitat Oberta de Catalunya. Internet Interdisciplinary Institute (IN3)-
dc.identifier.citationKizys, R., Juan, A.A., Sawik, B. & Calvet, L. (2019). A biased-randomized iterated local search algorithm for rich portfolio optimization. Applied Sciences, 9(17), 1-23. doi: 10.3390/app9173509-
dc.description.abstractThis research develops an original algorithm for rich portfolio optimization (ARPO), considering more realistic constraints than those usually analyzed in the literature. Using a matheuristic framework that combines an iterated local search metaheuristic with quadratic programming, ARPO efficiently deals with complex variants of the mean-variance portfolio optimization problem, including the well-known cardinality and quantity constraints. ARPO proceeds in two steps. First, a feasible initial solution is constructed by allocating portfolio weights according to the individual return rate. Secondly, an iterated local search framework, which makes use of quadratic programming, gradually improves the initial solution throughout an iterative combination of a perturbation stage and a local search stage. According to the experimental results obtained, ARPO is very competitive when compared against existing state-of-the-art approaches, both in terms of the quality of the best solution generated as well as in terms of the computational times required to obtain it. Furthermore, we also show that our algorithm can be used to solve variants of the portfolio optimization problem, in which inputs (individual asset returns, variances and covariances) feature a random component. Notably, the results are similar to the benchmark constrained efficient frontier with deterministic inputs, if variances and covariances of individual asset returns comprise a random component. Finally, a sensitivity analysis has been carried out to test the stability of our algorithm against small variations in the input data.en
dc.publisherApplied Sciences-
dc.relation.ispartofApplied Sciences, 2019, 9(17)-
dc.rightsCC BY-
dc.subjectconstrained portfolio optimizationen
dc.subjectefficiency indicesen
dc.subjectfinancial assetsen
dc.subjectiterated local searchen
dc.subjectbiased randomizationen
dc.subjectoptimización de cartera limitadaes
dc.subjectoptimització de cartera limitadaca
dc.subjectíndices de eficienciaes
dc.subjectíndexs d'eficiènciaca
dc.subjectactivos financieroses
dc.subjectactius financersca
dc.subjectbúsqueda local iteradaes
dc.subjectcerca local iteradaca
dc.subjectaleatorització esbiaixadaca
dc.subjectaleatorización sesgadaes
dc.titleA biased-randomized iterated local search algorithm for rich portfolio optimization-
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