Multi-objective imprecise programming for financial portfolio selection with fuzzy returns

Mansour, Nabil; Cherif, Mohamed Sadok; Abdelfattah, Walid

doi:10.1016/j.eswa.2019.07.027

Cited by 55 publications

(23 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…is approach, according to the authors, provides better prediction results. Mansour et al [15] formulated a multiobjective financial portfolio selection approach involving fuzzy parameters, where the distribution options are given by fuzzy numbers from the information provided by the decision environment. Tsai et al [16], in contrast to traditional methods, use more variables included in the fuzzy model to predict and better reflect the issue of stock volatility.…”

Section: Review Of the Scientific Literaturementioning

confidence: 99%

Type-2 Fuzzy Expert System Approach for Decision-Making of Financial Assets and Investing under Different Uncertainty

Janková

Dostál

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Extensive research results of stock market time series using classical fuzzy sets (type-1) are available in the literature. However, type-1 fuzzy sets cannot fully capture the uncertainty associated with stock market developments due to their limited descriptiveness. This paper fills a scientific gap and focuses on type-2 fuzzy logic applied to stock markets. Type-2 fuzzy sets may include additional uncertainty resulting from unclear, uncertain, or inaccurate financial data through which model inputs are calculated. Here we propose four methods based on type-2 fuzzy logic, which differ in the level of uncertainty contained in fuzzy sets and compared with the type-1 fuzzy model. The case study aims to create a model to support investment decisions in Exchange-Traded Funds (ETFs) listed on international equity markets. The created models of type-2 fuzzy logic are compared with the classic type-1 fuzzy logic model. Based on the results of the comparison, it can be said that type-2 fuzzy logic with dual fuzzy sets is able to better describe data from financial time series and provides more accurate outputs. The results reflect the capability and effectiveness of the approach proposed in this document. However, the performance of type-2 fuzzy logic models decreases with the inclusion of increasing uncertainty in fuzzy sets. For further research, it would be appropriate to examine the different levels of uncertainty in the input parameters themselves and monitor the performance of such a modified model.

show abstract

Section: Review Of the Scientific Literaturementioning

confidence: 99%

Type-2 Fuzzy Expert System Approach for Decision-Making of Financial Assets and Investing under Different Uncertainty

Janková

Dostál

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…A vast number of studies have assumed the fuzziness of returns and have utilize possibility measures to select the assets to be included in the investment portfolio (Carlsson et al, 2002;Gupta et al, 2020b; Y. J. Liu & Zhang, 2018;Mansour et al, 2019;Vercher et al, 2007;Wang & Zhu, 2002). Possibility measures are widely used, but they are not self-dual.…”

Section: Introductionmentioning

confidence: 99%

“…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

View full text Add to dashboard Cite

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.

show abstract

“…The introduced new trapezoidal fuzzy numbers were used in the fuzzy mean-variance model and mean-variance-skewness model for the optimal asset allocation. In [ 20 ], a multiobjective fuzzy portfolio selection approach, where possibility distributions are given by fuzzy numbers from the information supplied by the decision-making environment (investor, analyst, financial market environment, etc.) is proposed.…”

Section: Introductionmentioning

confidence: 99%

A Simple View on the Interval and Fuzzy Portfolio Selection Problems

Kaczmarek

Dymova

Sevastjanov

2020

Entropy

View full text Add to dashboard Cite

In this paper, first we show that the variance used in the Markowitz’s mean-variance model for the portfolio selection with its numerous modifications often does not properly present the risk of portfolio. Therefore, we propose another treating of portfolio risk as the measure of possibility to earn unacceptable low profits of portfolio and a simple mathematical formalization of this measure. In a similar way, we treat the criterion of portfolio’s return maximization as the measure of possibility to get a maximal profit. As the result, we formulate the portfolio selection problem as a bicriteria optimization task. Then, we study the properties of the developed approach using critical examples of portfolios with interval and fuzzy valued returns. The α-cuts representation of fuzzy returns was used. To validate the proposed method, we compare the results we got using it with those obtained with the use of fuzzy versions of seven widely reputed methods for portfolio selection. As in our approach we deal with the bicriteria task, the three most popular methods for local criteria aggregation are compared using the known example of fuzzy portfolio consist of five assets. It is shown that the results we got using our approach to the interval and fuzzy portfolio selection reflect better the essence of this task than those obtained by widely reputed traditional methods for portfolio selection in the fuzzy setting.

show abstract

Multi-objective imprecise programming for financial portfolio selection with fuzzy returns

Cited by 55 publications

References 53 publications

Type-2 Fuzzy Expert System Approach for Decision-Making of Financial Assets and Investing under Different Uncertainty

Type-2 Fuzzy Expert System Approach for Decision-Making of Financial Assets and Investing under Different Uncertainty

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

A Simple View on the Interval and Fuzzy Portfolio Selection Problems

Contact Info

Product

Resources

About