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A biased-randomized iterated local search algorithm for rich portfolio optimization

A biased-randomized iterated local search algorithm for rich portfolio optimization
A biased-randomized iterated local search algorithm for rich portfolio optimization
This 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.
constrained portfolio optimization, metaheuristics, efficiency indices, financial assets, iterated local search, biased randomization
2076-3417
Kizys, Renatas
9d3a6c5f-075a-44f9-a1de-32315b821978
Juan, Angel A.
727ca41c-da96-40ea-8ea9-b27ab03aee49
Sawik, Bartosz
d28e4275-e81f-418b-80b1-c065028ef3fb
Calvet, Laura
0c8e51bc-5ec3-469b-a8ab-cb2b1c760c33
Kizys, Renatas
9d3a6c5f-075a-44f9-a1de-32315b821978
Juan, Angel A.
727ca41c-da96-40ea-8ea9-b27ab03aee49
Sawik, Bartosz
d28e4275-e81f-418b-80b1-c065028ef3fb
Calvet, Laura
0c8e51bc-5ec3-469b-a8ab-cb2b1c760c33

Kizys, Renatas, Juan, Angel A., Sawik, Bartosz and Calvet, Laura (2019) A biased-randomized iterated local search algorithm for rich portfolio optimization. Applied Sciences (Switzerland), 9 (3509), [3509]. (doi:10.3390/app9173509).

Record type: Article

Abstract

This 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.

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Accepted/In Press date: 19 August 2019
Published date: 26 August 2019
Keywords: constrained portfolio optimization, metaheuristics, efficiency indices, financial assets, iterated local search, biased randomization

Identifiers

Local EPrints ID: 434752
URI: http://eprints.soton.ac.uk/id/eprint/434752
ISSN: 2076-3417
PURE UUID: bdf8fd89-90e1-4e10-a901-ac855375752f
ORCID for Renatas Kizys: ORCID iD orcid.org/0000-0001-9104-1809

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Date deposited: 08 Oct 2019 16:30
Last modified: 28 Apr 2022 02:27

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Contributors

Author: Renatas Kizys ORCID iD
Author: Angel A. Juan
Author: Bartosz Sawik
Author: Laura Calvet

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