Squirrel Search Algorithm for Portfolio Optimization

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dc.contributor.author Dhaini, Mahdi
dc.date.accessioned 2022-08-31T10:55:54Z
dc.date.available 2022-08-31T10:55:54Z
dc.date.copyright 2019 en_US
dc.date.issued 2019-10-11
dc.identifier.uri http://hdl.handle.net/10725/13979
dc.description.abstract Portfolio Optimization is a standard financial engineering problem. It aims for finding the best allocation of resources for a set of assets. This problem has been studied extensively in the literature where different models have been proposed since the classical Mean-Variance model was introduced by Harry Markowitz in 1952 and the later modified version by William Sharpe. The inclusion of real-life constraints to the problem has led to the introduction of the extended Mean-Variance model. However, the successes of nature-inspired algorithms in hard computational optimization problems have encouraged researchers to design and apply these algorithms for a variety of optimization problems. In this paper, we design and adapt a Squirrel Search Algorithm (SSA) for the unconstrained and constrained portfolio optimization problems. SSA is a very recent swarm intelligence algorithm inspired by the dynamic foraging behavior of flying squirrels. The proposed SSA metaheuristic approach is compared with a variety of approaches presented in the literature such as traditional single metaheuristics, hybrid metaheuristic approaches and multi-objective optimization approaches for portfolio optimization. Comparative analysis and computational results using different performance indicators show the superiority of the proposed approach for the unconstrained portfolio optimization using both extended Mean-Variance and Sharpe models. For the constrained version of the problem, the proposed approach has also achieved highly competitive results for the different models adopted. en_US
dc.language.iso en en_US
dc.subject Portfolio management -- Mathematical models en_US
dc.subject Mathematical optimization en_US
dc.subject Heuristic algorithms en_US
dc.subject Lebanese American University -- Dissertations en_US
dc.subject Dissertations, Academic en_US
dc.title Squirrel Search Algorithm for Portfolio Optimization en_US
dc.type Thesis en_US
dc.term.submitted Fall en_US
dc.author.degree MS in Computer Science en_US
dc.author.school SAS en_US
dc.author.idnumber 201400349 en_US
dc.author.commembers Bou Mosleh, Anwar
dc.author.commembers Haraty, Ramzi
dc.author.department Computer Science And Mathematics en_US
dc.description.physdesc xii, 72 leaves: ill. en_US
dc.author.advisor Mansour, Nashat
dc.article.pages https://doi.org/10.26756/th.2022.441
dc.keywords Portfolio Optimization en_US
dc.keywords Squirrel Search Algorithm en_US
dc.keywords Markowitz en_US
dc.keywords Sharpe en_US
dc.keywords Mean-Variance en_US
dc.description.bibliographiccitations Bibliography: leaves 64-72. en_US
dc.identifier.doi https://doi.org/10.26756/th.2022.441
dc.author.email mahdi.dhaini@lau.edu.lb en_US
dc.identifier.tou http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php en_US
dc.publisher.institution Lebanese American University en_US
dc.author.affiliation Lebanese American University en_US

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