Credibilistic Mean-Semi-Entropy Model for Multi-Period Portfolio Selection with Background Risk

Credibilistic Mean-Semi-Entropy Model for Multi-Period Portfolio Selection with Background Risk
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DOI:
10.3390/e21100944
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发表时间:
2019-09-26
期刊:
影响因子:
2.7
通讯作者:
Li Q
Li Q
中科院分区:
物理与天体物理3区
文献类型:
--
作者:
Zhang J;Li Q

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在金融市场中,投资者不仅要面对投资组合风险,还要面对背景风险。本文提出了一个具有背景风险的多目标均值-半熵多阶段投资组合选择模型。此外,现实的约束,如流动性,基数约束,交易成本,买入阈值被认为是。为了有效地求解该多目标优化问题,结合遗传算法(DA)和非支配排序遗传算法Ⅱ(NSGA Ⅱ)的优点,设计了一种混合蜻蜓算法-遗传算法(HDA-GA)。此外,在混合算法中,参数优化,约束处理,和外部存档的方法来提高找到精确的近似Pareto最优解的能力,具有高的多样性和覆盖率。最后,我们提供了几个实证研究,以显示所提出的方法的有效性。
In financial markets, investors will face not only portfolio risk but also background risk. This paper proposes a credibilistic multi-objective mean-semi-entropy model with background risk for multi-period portfolio selection. In addition, realistic constraints such as liquidity, cardinality constraints, transaction costs, and buy-in thresholds are considered. For solving the proposed multi-objective problem efficiently, a novel hybrid algorithm named Hybrid Dragonfly Algorithm-Genetic Algorithm (HDA-GA) is designed by combining the advantages of the dragonfly algorithm (DA) and non-dominated sorting genetic algorithm II (NSGA II). Moreover, in the hybrid algorithm, parameter optimization, constraints handling, and external archive approaches are used to improve the ability of finding accurate approximations of Pareto optimal solutions with high diversity and coverage. Finally, we provide several empirical studies to show the validity of the proposed approaches.
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