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Portfolio Optimization and Asset Allocation With Metaheuristics: A Review

Portfolio Optimization and Asset Allocation With Metaheuristics: A Review

Jhuma Ray, Siddhartha Bhattacharyya, N. Bhupendro Singh
Copyright: © 2021 |Pages: 19
ISBN13: 9781799880486|ISBN10: 1799880486|EISBN13: 9781799880998
DOI: 10.4018/978-1-7998-8048-6.ch005
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MLA

Ray, Jhuma, et al. "Portfolio Optimization and Asset Allocation With Metaheuristics: A Review." Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms, edited by Information Resources Management Association, IGI Global, 2021, pp. 78-96. https://doi.org/10.4018/978-1-7998-8048-6.ch005

APA

Ray, J., Bhattacharyya, S., & Bhupendro Singh, N. (2021). Portfolio Optimization and Asset Allocation With Metaheuristics: A Review. In I. Management Association (Ed.), Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms (pp. 78-96). IGI Global. https://doi.org/10.4018/978-1-7998-8048-6.ch005

Chicago

Ray, Jhuma, Siddhartha Bhattacharyya, and N. Bhupendro Singh. "Portfolio Optimization and Asset Allocation With Metaheuristics: A Review." In Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms, edited by Information Resources Management Association, 78-96. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-8048-6.ch005

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Abstract

Portfolio optimization stands to be an issue of finding an optimal allocation of wealth to place within the obtainable assets. Markowitz stated the problem to be structured as dual-objective mean-risk optimization, pointing the best trade-off solutions within a portfolio between risks which is measured by variance and mean. Thus the major intention was nothing else than hunting for optimum distribution of wealth over a specific amount of assets by diminishing risk and maximizing returns of a portfolio. Value-at-risk, expected shortfall, and semi-variance measures prove to be complex for measuring risk, for maximization of skewness, liquidity, dividends by added objective functions, cardinality constraints, quantity constraints, minimum transaction lots, class constraints in real-world constraints all of which are incorporated in modern portfolio selection models, furnish numerous optimization challenges. The emerging portfolio optimization issue turns out to be extremely tough to be handled with exact approaches because it exhibits nonlinearities, discontinuities and high-dimensional, efficient boundaries. Because of these attributes, a number of researchers got motivated in researching the usage of metaheuristics, which stand to be effective measures for finding near optimal solutions for tough optimization issues in an adequate computational time frame. This review report serves as a short note on portfolio optimization field with the usage of Metaheuristics and finally states that how multi-objective metaheuristics prove to be efficient in dealing with portfolio selection problems with complex measures of risk defining non-convex, non-differential objective functions.

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