Viability of infeasible portfolio selection problems: A fuzzy approach

León, Teresa; Liern, Vicente; Vercher, Enriqueta

doi:10.1016/s0377-2217(01)00175-8

Cited by 112 publications

(28 citation statements)

References 11 publications

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“…We can find several and suitable Soft Computing approaches to the portfolio selection problem in the two stages of the selecting process: in the modeling of beliefs and the incorporation of imprecise knowledge, for the approximation of uncertain performance of future returns on assets and portfolios and in the development of optimization procedures, mainly through fuzzy mathematical programming techniques and evolutionary algorithms [2,3,4,5].…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of forecasting portfolio returns using Soft Computing technologies

Rubio-Manzano¹,

Bermúdez²,

Vercher³

2015

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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We propose using fuzzy time series (FTS) to forecast the future performance of returns on portfolios. We model the portfolio selection problem by means of possibilistic moments, and approximate the uncertainty of the return on a given portfolio by trapezoidal fuzzy numbers.Some modifications into the classical models of FTS, based on weighted operators, allow us generating trapezoidal numbers as forecasters of the performance of portfolio returns. We incorporate our proposals into classical FTS methods and analyze their effectiveness with respect to FTS models with a possibilistic interval-valued mean approach, using historical returns on assets from the Spanish stock market.

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Section: Introductionmentioning

confidence: 99%

Comparative analysis of forecasting portfolio returns using Soft Computing technologies

Rubio-Manzano¹,

Bermúdez²,

Vercher³

2015

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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“…In this respect, to tackle the uncertainty in the financial market, stochastic-fuzzy and fuzzy-stochastic methodologies are extensively used in portfolio modeling. Authors like Konno and Suzuki [15], Leon et al [26], Vercher et al [27], Bhattacharyya et al [12,22], Dey and Bhattacharyya [28], etc. used fuzzy numbers to replace uncertain returns of securities, and they define portfolio selection as a mathematical programming problem in order to select the best alternative.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty theory based multiple objective mean-entropy-skewness stock portfolio selection model with transaction costs

Bhattacharyya¹,

Chatterjee

Kar

2013

J. Uncertain. Anal. Appl.

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Purpose: The aim of this paper is to develop a mean-entropy-skewness stock portfolio selection model with transaction costs in an uncertain environment. Methods: Since entropy is free from reliance on symmetric probability distributions and can be computed from nonmetric data, it is more general than others as a competent measure of risk. In this work, returns of securities are assumed to be uncertain variables, which cannot be estimated by randomness or fuzziness. The model in the uncertain environment is formulated as a nonlinear programming model based on uncertainty theory. Also, some other criteria like short-and long-term returns, dividends, number of assets in the portfolio, and the maximum and minimum allowable capital invested in stocks of any company are considered. Since there is no efficient solution methodology to solve the proposed model, assuming the returns as some special uncertain variables, the original portfolio selection model is transformed into an equivalent deterministic model, which can be solved by any state-of-the-art solution methodology. Results: The feasibility and effectiveness of the proposed model is verified by a numerical example extracted from Bombay Stock Exchange, India. Returns are considered in the form of trapezoidal uncertain variables. A genetic algorithm is used for simulation. Conclusions:The efficiency of the portfolio is evaluated by looking for risk contraction on one hand and expected return and skewness augmentation on the other hand. An empirical application has served to illustrate the computational tractability of the approach and the effectiveness of the proposed algorithm.

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“…By incurring fuzzy approaches quantitative analysis, qualitative analysis, experts' knowledge and investors' subjective opinions can be better integrated into a portfolio selection model. Authors like Konno and Suzuki [25], Leon et al [26], Vercher [27], Bhattacharyya et al [12] and others use fuzzy numbers to embody uncertain returns of the securities and they define the portfolio selection as a mathematical programming problem in order to select the best alternative. In possibilistic portfolio selection models, two types of approaches are noticed.…”

Section: Introductionmentioning

confidence: 99%