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Multi-parameter Portfolio Selection Model with Some Novel Score-Deviation Under Dual Hesitant Fuzzy Environment
International Journal of Fuzzy Systems ( IF 4.3 ) Pub Date : 2020-04-10 , DOI: 10.1007/s40815-020-00835-8
Weimin Li , Xue Deng

In risk investment, investors have to rely on uncertain information when it is difficult to obtain enough precise data. Dual hesitant fuzzy set (DHFS) is more applicable to deal with uncertain information because it involves membership degrees and non-membership degrees, which can validly describe positive and negative information, respectively. Although there has been research on decision-making based on the DHFS, the focus still remains on ranking the alternatives and choosing the best one, which cannot help investors to find the optimal portfolios. Therefore, to solve this problem, we mainly propose two novel portfolio selection models based on the DHFS in this paper. Firstly, we propose a Max-score dual hesitant fuzzy portfolio selection model with information preference (Model 3) for investors focusing on returns regardless of risks. Secondly, to consider the risks of portfolios, we improve Model 3 and develop a score-deviation dual hesitant fuzzy portfolio selection model with information preference and risk appetite (Model 5). Finally, a case study is conducted to highlight the effectiveness of the proposed models. A detailed sensitivity analysis and an efficient frontier analysis show that Model 5 can validly capture investors’ information preferences and risk appetites. Furthermore, compared with the hesitant fuzzy portfolio model, Model 5 can offer more options to the investors with different information preferences.

中文翻译:

双重犹豫模糊环境下具有新颖得分偏差的多参数投资组合选择模型

在风险投资中,当难以获得足够的精确数据时,投资者必须依靠不确定的信息。双重犹豫模糊集(DHFS)更适用于处理不确定信息,因为它涉及隶属度和非隶属度,分别可以有效地描述正面和负面信息。尽管已经对基于DHFS的决策进行了研究,但重点仍然放在对备选方案进行排名和选择最佳方案上,这无法帮助投资者找到最佳投资组合。因此,为解决这一问题,本文主要提出基于DHFS的两个新颖的投资组合选择模型。首先,我们提出了一种具有信息偏好的Max-score双重犹豫模糊投资组合选择模型(模型3),以关注风险而不考虑收益的投资者。其次,为了考虑投资组合的风险,我们改进了模型3,并开发了一种具有信息偏好和风险偏好的分数偏差双重犹豫模糊投资组合选择模型(模型5)。最后,进行了案例研究以突出所提出模型的有效性。详细的敏感性分析和有效的前沿分析表明,模型5可以有效地捕捉投资者的信息偏好和风险偏好。此外,与犹豫的模糊投资组合模型相比,模型5可以为具有不同信息偏好的投资者提供更多选择。进行案例研究以突出所提出模型的有效性。详细的敏感性分析和有效的前沿分析表明,模型5可以有效地捕捉投资者的信息偏好和风险偏好。此外,与犹豫的模糊投资组合模型相比,模型5可以为具有不同信息偏好的投资者提供更多选择。进行案例研究以突出所提出模型的有效性。详细的敏感性分析和有效的前沿分析表明,模型5可以有效地捕捉投资者的信息偏好和风险偏好。此外,与犹豫的模糊投资组合模型相比,模型5可以为具有不同信息偏好的投资者提供更多选择。
更新日期:2020-04-10
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