Originating multiple-objective portfolio selection by counter-COVID measures and analytically instigating robust optimization by mean-parameterized nondominated paths

IF 3.7 4区 管理学 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Operations Research Perspectives Pub Date : 2022-01-01 DOI:10.1016/j.orp.2022.100252
Yue Qi , Kezhi Liao , Tongyang Liu , Yu Zhang
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引用次数: 1

Abstract

The COVID-19 pandemic is unleashing crises of humanity, economy, and finance. Portfolio selection is widely recognized as the foundation of modern financial economics. Therefore, it is naturally crucial and inviting to utilize portfolio selection in order to counter COVID-19 in stock markets. We originate a counter-COVID measure for stocks, extend portfolio selection, and construct multiple-objective portfolio selection. Because of the uncertainty in measuring counter-COVID, we perform robust optimization. Specifically, we analytically compute the optimal solutions as a trail of an optimal portfolio due to the change of counter-COVID. We call the trail as mean-parameterized nondominated path. Moreover, the path is a continuous function of the change, so the portfolio relatively mildly varies for the change. In contrast, researchers typically still focus on 2-objective robust illustrations and infrequently explicitly compute the optimal solutions for multiple-objective portfolio optimization.

To the best of our knowledge, there is limited research for multiple-objective portfolio selection of COVID and for the robust optimization of multiple-objective portfolio selection. In such an area, this paper contributes to the literature. The implications to fight COVID are that investors minimize risk, maximize return, and maximize counter-COVID in stock markets and that investors ascertain the multiple-objective portfolio selection as relatively robust.

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通过反covid措施发起多目标投资组合选择,并通过平均参数化非主导路径解析引发鲁棒优化
新冠肺炎大流行正在引发人类、经济和金融危机。投资组合选择被广泛认为是现代金融经济学的基础。因此,为了在股市中应对COVID-19,利用投资组合选择自然是至关重要的。我们提出了股票的抗covid措施,扩展了投资组合选择,构建了多目标投资组合选择。由于测量抗covid的不确定性,我们进行了鲁棒优化。具体来说,我们分析计算了最优解决方案,作为由于counter-COVID变化的最优投资组合的踪迹。我们称这条轨迹为平均参数化非支配路径。此外,路径是变化的连续函数,因此投资组合相对温和地随变化而变化。相比之下,研究人员通常仍然集中在两目标的鲁棒性说明上,很少明确地计算多目标投资组合优化的最优解。据我们所知,关于COVID的多目标投资组合选择和多目标投资组合鲁棒优化的研究有限。在这方面,本文有一定的文献贡献。对抗COVID的含义是投资者在股票市场中最大限度地降低风险,最大限度地提高回报,最大限度地提高抗COVID能力,并且投资者确定多目标投资组合选择相对稳健。
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来源期刊
Operations Research Perspectives
Operations Research Perspectives Mathematics-Statistics and Probability
CiteScore
6.40
自引率
0.00%
发文量
36
审稿时长
27 days
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