Adoptation of Extended Metaheuristics Considering Risk-Allocated Portfolio Optimization
Adoptation of Extended Metaheuristics Considering Risk-Allocated Portfolio Optimization |
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| © 2023 by IJETT Journal | ||
| Volume-71 Issue-5 |
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| Year of Publication : 2023 | ||
| Author : Jhuma Ray |
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| DOI : 10.14445/22315381/IJETT-V71I5P235 | ||
How to Cite?
Jhuma Ray , "Adoptation of Extended Metaheuristics Considering Risk-Allocated Portfolio Optimization," International Journal of Engineering Trends and Technology, vol. 71, no. 5, pp. 336-349, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I5P235
Abstract
In recent times, the soft computing criterion is competent for tackling practical ambiguities involving numerous techniques, specifically neural networks, approaches to fuzzy logic, and evolutionary computational techniques. Various soft computational-based metaheuristics for minimization of risk governing portfolio optimization by using Particle Swarm Optimization, Differential Evolution and Genetic Algorithm approach focusing on optimization of CVaR (Conditional Value at Risk) measures within various market situations established on diverse objectives and constraints have been discussed within this article. The territory of portfolio optimization is meant for the selection of the range of diversified assets present in a portfolio, thus constructing a portfolio which can be best by considering some stated principles. In the modern era, multiobjective optimization procedures have proven important within the area of business intelligence, measuring the market risk–return paradigm. VaR (Value at Risk), being a prevailing technique in ascertaining downside risk within a portfolio, has been elucidated as pth percentage of returns on a specified portfolio to plan any horizon. Another vigorous technique remains the Conditional Value at Risk to determine the labeled risk entity in a portfolio within unstable market circumstances. The suggested techniques have also proved beneficial in the selection of various financial instruments in comparison to their VaR counterparts. The obtained results depict a promising outlet for determining excellent portfolio returns.
Keywords
Conditional Value at Risk(Cvr), Differential evolution, Genetic algorithm, Particle Swarm Optimization, Value at Risk.
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