English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The Markowitz-based portfolio selection turns to an NP-hard problem whenconsidering cardinality constraints. In this case, existing exact solutionslike quadratic programming may not be efficient to solve the problem. Manyresearchers, therefore, used heuristic and metaheuristic approaches in order todeal with the problem. This work presents Asexual Reproduction Optimization(ARO), a model free metaheuristic algorithm inspired by the asexualreproduction, in order to solve the portfolio optimization problem includingcardinality constraint to ensure the investment in a given number of differentassets and bounding constraint to limit the proportions of fund invested ineach asset. This is the first time that this relatively new metaheuristic is inthe field of portfolio optimization, and we show that ARO results in betterquality solutions in comparison with some of the well-known metaheuristicsstated in the literature. To validate our proposed algorithm, we measured thedeviation of obtained results from the standard efficient frontier. We reportour computational results on a set of publicly available benchmark testproblems relating to five main market indices containing 31, 85, 89, 98, and225 assets. These results are used in order to test the efficiency of ourproposed method in comparison to other existing metaheuristic solutions. Theexperimental results indicate that ARO outperforms Genetic Algorithm(GA), TabuSearch (TS), Simulated Annealing (SA), and Particle Swarm Optimization (PSO) inmost of test problems. In terms of the obtained error, by using ARO, theaverage error of the aforementioned test problems is reduced by approximately20 percent of the minimum average error calculated for the above-mentionedalgorithms.

Markowitz-based cardinality constrained portfolio selection using Asexual Reproduction Optimization (ARO)