Fuzzy multi-objective portfolio model based on semi-variance–semi-absolute deviation risk measures

Wei, Yue; Wang, Yuping; Xuan, Hejun

doi:10.1007/s00500-018-3452-y

Cited by 25 publications

(5 citation statements)

References 40 publications

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“…These studies can be roughly divided into three types. The first type of research is mainly to introduce more realistic risk measures in the mean-variance approach [2]- [5]. Liu and Zhang [2] introduced a lower semi-variance as risk measure.…”

Section: Introductionmentioning

confidence: 99%

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Projecting Financial Technical Indicators Into Networks as a Tool to Build a Portfolio

Chen

2021

IEEE Access

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Using the topological structure of financial networks to build a portfolio has attracted a wide range of research interests. A similarity matrix based on the technical indicators (TIs), and a correlation matrix based on the stock returns, are used to construct the financial networks. Hybrid topological measures are calculated for both networks to select the stocks that are further used to build portfolios. The random matrix theory (RMT) tool is used to filter the risk measurement in the Markowitz optimization process. A general result is presented in this study, which indicates that the portfolios composed of peripheral stocks consistently outperform those composed of central stocks. As shown in the empirical results, the RMT method can improve the Markowitz optimization process and further improve the performance of portfolios. By comparing the different portfolios established under different networks, the results reveal the advantage of the topological measure based on TIs. This study provides an important approach for constructing financial networks from the time series data, and it explores the impact of different similarity measurements on the network-based models for portfolio selection.

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Section: Introductionmentioning

confidence: 99%

“…Amir and Malihe [4] proposed a new risk measure named entropic valueat-risk (EVaR). Yue et al [5] considered simultaneously semi-variance and semi-absolute deviation as double-risk measures. The second type of study is mainly to add new criteria in the portfolio selection process [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Projecting Financial Technical Indicators Into Networks as a Tool to Build a Portfolio

Chen

2021

IEEE Access

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“…Bilbao-Terol et al [13] employed a fuzzy compromise programming model to overcome the fuzzy portfolio selection problem. Some scholars have also focused on the issues of mean variance, mean semi-variance, skewness of a given fuzzy variable, and mean risk curve in the portfolio selection models [14][15][16][17][18]. Tsaur [19] proposed a fuzzy portfolio model with a fuzzy return and fuzzy proportion under incomplete information during a period of depression.…”

Section: Introductionmentioning

confidence: 99%

“…Zhou et al [26] incorporated aggressive-neutral-conservative attitudes alongside VaR constraints and the ε constraint method to solve the fuzzy portfolio selection. Yue et al [17] constructed a fuzzy portfolio selection model within the lower partial risk framework.…”

Section: Introductionmentioning

confidence: 99%

Adjustable Security Proportions in the Fuzzy Portfolio Selection under Guaranteed Return Rates

Huang¹,

Chen

Chiu

et al. 2021

Mathematics

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Based on the concept of high returns as the preference to low returns, this study discusses the adjustable security proportion for excess investment and shortage investment based on the selected guaranteed return rates in a fuzzy environment, in which the return rates for selected securities are characterized by fuzzy variables. We suppose some securities are for excess investment because their return rates are higher than the guaranteed return rates, and the other securities whose return rates are lower than the guaranteed return rates are considered for shortage investment. Then, we solve the proposed expected fuzzy returns by the concept of possibility theory, where fuzzy returns are quantified by possibilistic mean and risks are measured by possibilistic variance, and then we use linear programming model to maximize the expected value of a portfolio’s return under investment risk constraints. Finally, we illustrate two numerical examples to show that the expected return rate under a lower guaranteed return rate is better than a higher guaranteed return rates in different levels of investment risks. In shortage investments, the investment proportion for the selected securities are almost zero under higher investment risks, whereas the portfolio is constructed from those securities in excess investments.

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“…Together with return and risk, one of the fundamental criteria employed by actual investors to build their portfolios is liquidity. Several previous studies on portfolio decision-making have included liquidity as a fuzzy variable (Arenas-Parra et al, 2001;Gupta et al, 2010Gupta et al, , 2011Jalota et al, 2017aJalota et al, , 2017bMansour et al, 2019;Yue et al, 2019). Generally, investors' preference is to own portfolios that contain liquid assets which can be easily liquidated in the future.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Approach to Portfolio Optimization in the Light of the Credibility Theory

García

González-Bueno

Guijarro

et al. 2020

Technological and Economic Development of Economy

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The present research proposes a novel methodology to solve the problems faced by investors who take into consideration different investment criteria in a fuzzy context. The approach extends the stochastic mean-variance model to a fuzzy multiobjective model where liquidity is considered to quantify portfolio’s performance, apart from the usual metrics like return and risk. The uncertainty of the future returns and the future liquidity of the potential assets are modelled employing trapezoidal fuzzy numbers. The decision process of the proposed approach considers that portfolio selection is a multidimensional issue and also some realistic constraints applied by investors. Particularly, this approach optimizes the expected return, the risk and the expected liquidity of the portfolio, considering bound constraints and cardinality restrictions. As a result, an optimization problem for the constraint portfolio appears, which is solved by means of the NSGA-II algorithm. This study defines the credibilistic Sortino ratio and the credibilistic STARR ratio for selecting the optimal portfolio. An empirical study on the S&P100 index is included to show the performance of the model in practical applications. The results obtained demonstrate that the novel approach can beat the index in terms of return and risk in the analyzed period, from 2008 until 2018.

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Fuzzy multi-objective portfolio model based on semi-variance–semi-absolute deviation risk measures

Cited by 25 publications

References 40 publications

Projecting Financial Technical Indicators Into Networks as a Tool to Build a Portfolio

Projecting Financial Technical Indicators Into Networks as a Tool to Build a Portfolio

Adjustable Security Proportions in the Fuzzy Portfolio Selection under Guaranteed Return Rates

Multiobjective Approach to Portfolio Optimization in the Light of the Credibility Theory

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