Hesitant fuzzy linguistic portfolio model with variable risk appetite and its application in the investment ratio calculation

Zhou, Wei; Zhang, Xu

doi:10.1016/j.asoc.2019.105719

Cited by 18 publications

(7 citation statements)

References 46 publications

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“…Compared with the quantitative portfolio selection methods such as modern portfolio theory [36][37][38], the [3,4] and others, the given model also uses some quantitative financial data to ensure the objectivity of the given results. e incomplete rationality of experts could lead to subjectivity in the evaluation information provided by experts and decision-makers, which can en, we add objective quantitative data into the model.…”

Section: Illustrated Examplementioning

confidence: 99%

“…At present, a large number of suitable tools and sophisticated models have been used for portfolio construction and selection research [2]. However, most of the methods that have been constructed require sufficient quantitative data [3,4]. In fact, in the research process, there are situations where important indicators are difficult to quantify, there is no data, or there is little data [5,6].…”

Section: Introductionmentioning

confidence: 99%

“…Nonstatistical methods used in portfolio models include neural networks [10], CEDEA [11], genetic algorithms [12], and fuzzy theory [13,14]. ird, some qualitative portfolio models were developed based on fuzzy environments, such as Wang and Zhu [15], Chiarawongse et al [16], and Zhou and Xu [3,4].…”

Section: Introductionmentioning

confidence: 99%

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Cross-Efficiency Evaluation under Probabilistic Hesitant Fuzzy Environment and Its Application to the Portfolio Selection Process

Zhang

Zhou

Luo

et al. 2021

Mathematical Problems in Engineering

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In the case of insufficient quantitative data, qualitative evaluation information is very important for investment decision-making. However, if it is completely based on qualitative evaluation information, the results may be subjective. In response to this problem, this paper proposes a method, namely, probabilistic hesitant fuzzy cross-efficiency evaluation (PHFCEE) method, based on probabilistic hesitant fuzzy theory, cross-efficiency data envelopment analysis (CEDEA). This method uses probabilistic hesitant fuzzy sets to collect qualitative evaluation information and then uses the cross-efficiency DEA model to fuse quantitative data and qualitative information. And finally, an investment portfolio is built based on the cross-efficiency value and its variance. In addition, this article gives the specific operating steps of the PHFCEE method and uses the construction of a portfolio of 10 stocks in the China CSI 300 Index as an example to illustrate the effectiveness of this method.

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Section: Illustrated Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cross-Efficiency Evaluation under Probabilistic Hesitant Fuzzy Environment and Its Application to the Portfolio Selection Process

Zhang

Zhou

Luo

et al. 2021

Mathematical Problems in Engineering

Self Cite

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“…With the emergence of uncertain factors in the investment process, several scholars discover that because of the constraints of limited rationality, investors’ risk preferences has an important influence on the evaluation of investment schemes (Liang et al , 2020; Lai et al , 2019; Zhou and Xu, 2019); several scholars take into account risk preferences when evaluating investment schemes (Qadan, 2019; Keller et al , 2019; Zhang and Lin, 2019).…”

Section: Empirical Analysismentioning

confidence: 99%

Risk investment decisions within the deterministic equivalent income model

Song

et al. 2020

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Purpose The purpose of this paper is to establish a deterministic equivalent income model (DEIM) based on the risk cost (RC) and risk aversion of investors. The model fully considers both subjective and objective factors that affect risk investment and reasonably evaluates risk investment schemes to choose the correct investment scheme and gain greater investment returns. Design/methodology/approach The utility function is used to measure the extent to which an investor is satisfied by investment returns in various scenarios. Risk aversion expresses subjective attitude of investors to risk. RC represents risk loss in currency. This methodology is based on risk aversion function, utility function and RC theory to establish DEIM. Findings This study shows that investors with different risk preferences have different certainty equivalent returns (CER), so their choices of investment options change accordingly. Practical implications In this paper, the authors use DEIM to test an investment case and conclude that the CER and investment scheme both change with different risk preferences. At the same time, case analysis shows that DEIM is reasonable and stable when evaluating risk investment schemes. Originality/value In this study, the authors innovate by introducing both the RC and risk aversion degree into risk investment schemes evaluation and by deriving a utility function from the absolute risk aversion function to build a utility decision matrix and establish DEIM. The model combines the subjective and objective factors that influence risk investment decisions.

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“…Ozlu and Karaaslan (2019) developed the idea of distance measures for type 2 HFSs as well as their utilizationsin MCGDM problems. The HF-linguistic portfolio structure with changing risk appetite was presented and shown in DMPs by Zhou and Xu (2019). Xue et al (2019) proposed using the evidential reasoning approach with multi-scale HF-linguistic information to assess the threat of landslide dams.…”

Section: Introductionmentioning

confidence: 99%

An Advanced Approach to HFP-Soft Set Based Decision- Making in Uncertain Environment

Das¹,

Gupta²

2022

JAIMLNN

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Uncertainty, vagueness and the representation of imperfect knowledge have been a problem in many fields of research, such as environmental science, artificial intelligence, network and communication, signal processing, machine learning, computer science, information technology, as well as medical science, economics, and engineering. Decisionmaking methods (simply, DMMs) based on hesitant fuzzy sets (HFSs) and soft sets (SSs) have recently received a lot of attention. Karamaz and Karaaslan (2021)proposed an approach to hesitant fuzzy parameterized soft set (HFPSS) based decision making using score function on hesitant fuzzy elements (HFEs) in HFPSS, with the goal of using HFSs and SSs more effectively for uncertainty issues prevalent in most real-world issues, but it has some limitations in solving HFPSS based decision-making problems (briefly, DMPs). In this study, we demonstrated with a real-life example that the Karamaz-Karaaslan DMM is insufficient to solve HFPSS based DMPs and we have proposed an advanced and adjustable DMM for solving HFPSS based DMPs in uncertain environment. Some reallife examples are provided to demonstrate the validity of our proposed modified DMM.

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Hesitant fuzzy linguistic portfolio model with variable risk appetite and its application in the investment ratio calculation

Cited by 18 publications

References 46 publications

Cross-Efficiency Evaluation under Probabilistic Hesitant Fuzzy Environment and Its Application to the Portfolio Selection Process

Cross-Efficiency Evaluation under Probabilistic Hesitant Fuzzy Environment and Its Application to the Portfolio Selection Process

Risk investment decisions within the deterministic equivalent income model

An Advanced Approach to HFP-Soft Set Based Decision- Making in Uncertain Environment

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