Experimental Study on a Hybrid Nature-Inspired Algorithm for Financial Portfolio Optimization
Hybrid intelligent schemes have proven their efficiency in solving NP-hard optimization problems. Portfolio optimization refers to the problem of finding the optimal combination of assets and their corresponding weights which satisfies a specific investment goal and various constraints. In this study, a hybrid intelligent metaheuristic, which combines the Ant Colony Optimization algorithm and the Firefly algorithm, is proposed in tackling a complex formulation of the portfolio management problem. The objective function under consideration is the maximization of a financial ratio which combines factors of risk and return. At the same time, a hard constraint, which refers to the tracking ability of the constructed portfolio towards a benchmark stock index, is imposed. The aim of this computational study is twofold. Firstly, the efficiency of the hybrid scheme is highlighted. Secondly, comparison results between alternative mechanisms, which are incorporated in the main function of the hybrid scheme, are presented.
Keywordsant colony optimization algorithm firefly algorithm portfolio optimization hybrid NII algorithm
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