Nowadays it is increasingly important to enhance the efficiency and robustness of the allocation in a financial instruments’ portfolio, especially, in the occurrence of an increased market volatility. In this paper, a market volatility-robust (i.e. counter cyclical) investment portfolio formulation procedure under the modified Markowitz’s framework with the use of sampling methods and genetic algorithms is established. In essence, the developed model relies on many input samples of rates of return that are further implemented in evolution simulations based on the survival-of-the-fittest principle in order to overcome the risk of obtaining sub optimal investment proportions. It is demonstrated that a similar portfolio composition approach, in comparison to Newton’s optimisation, produces more diverse allocations and allows for a more efficient mitigation of increased market volatility reverberations. For those reasons, the presented research contributes to existing allocation techniques and directly addresses the task of minimizing the adverse implications of market risk, what further allows for a rational investment decision-making and, importantly, holds capacity for further development.
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