Mean-variance-skewness model for portfolio selection with transaction costs

Liu, S.; Wang, Shouyang; Wan-hua, Qiu

doi:10.1080/0020772031000158492

Cited by 94 publications

(31 citation statements)

References 27 publications

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“…But there is a plethora of empirical studies showing that portfolio returns are generally not normally distributed. Consequently, in some recent studies, the concept of Mean-Variance trade-off has been extended to include the skewness of return in portfolio selection, see Konno and Suzuki [6], Leung et al [9], Liu et al [12], Joro and Na [5], Briec et al [2], or the kurtosis, see Maringer and Parpas [14], Lai et al [7], Jondeau and Rockinger [4] among others and also applied in various practical problems, see Rădulescu et al [17][18][19]. Many empirical studies show that investors prefer positive skewness, because it implies a low probability of obtaining a large negative return.…”

Section: T X Xmentioning

confidence: 99%

Convexification Technique and Portfolio Optimization

Fulga¹

2013

SIC

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Abstract:In this paper, a general transformation method which converts a nonconvex optimization problem to an equivalent problem with better properties is proposed. Under certain assumptions, the local convexity of the Lagrangian function of the equivalent problem is guaranteed and thus the class of optimization models to which dual methods can be applied is extended. Practical classes of problems where the proposed method can be applied are given. They include the class of portfolio selection models. Numerical examples illustrate the main results.

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Section: T X Xmentioning

confidence: 99%

Convexification Technique and Portfolio Optimization

Fulga¹

2013

SIC

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“…The importance of higher order moments in portfolio selection was suggested by Samuelson [13]. However, considerations of skewness in portfolio selection problem were started by 1990 and were done by Lai [14], Konno and Suzuki [15], Chunhachinda et al [16], Liu et al [17], Prakash et al [18], Briec et al [19], Yu et al [20], Li et al [21], Bhattacharyya et al [22], Bhattacharyya and Kar [23,24], Bhattacharyya [25], and others. Consideration of a mean-entropyskewness model in portfolio selection problem is introduced by Bhattacharyya et al [12].…”

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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“…Some researchers also have used higher order moments. Arditti [3], Samuelson [4], Kraus and Litzenberger [5], Konno et al [6], Konno and Suzuki [7], Liu et al [8], Prakash et al [9], Lai [10], Chunhachinda et al [11], Briec et al [12], Yu et al [13] are some among those researchers who have invoked these concepts in this area.…”

Section: Introductionmentioning

confidence: 99%

Entropy-Cost Ratio Maximization Model for Efficient Stock Portfolio Selection Using Interval Analysis

Dey¹,

Bhattacharyya²

2013

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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This paper introduces a new stock portfolio selection model in non-stochastic environment. Following the principle of maximum entropy, a new entropy-cost ratio function is introduced as the objective function. The uncertain returns, risks and dividends of the securities are considered as interval numbers. Along with the objective function, eight different types of constraints are used in the model to convert it into a pragmatic one. Three different models have been proposed by defining the future financial market optimistically, pessimistically and in the

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Mean-variance-skewness model for portfolio selection with transaction costs

Cited by 94 publications

References 27 publications

Convexification Technique and Portfolio Optimization

Convexification Technique and Portfolio Optimization

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

Entropy-Cost Ratio Maximization Model for Efficient Stock Portfolio Selection Using Interval Analysis

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