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

Abstract

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.

Discussing an Expected Utility and Weighted Entropy Framework

“…We remark that weighted entropy-or related forms of Shannon entropy-though it has already a long history of almost half a century since its first reference [16], is yet being referred to as a risk measure in portfolio selection strategies (e.g. [38] [39]).…”

Uncertain pricing decision problem in closed-loop supply chain with risk-averse retailer

“…For now, uncertainty theory has been successfully applied to many fields, such as option pricing problem [12], stock problem [13], production control problem [14], supply chain pricing problem [15], uncertain random process [16], project scheduling problem [17], and so on. In this paper, we consider the remanufacturing cost, market demand, and collection cost as uncertain variables.…”

Gray Wolf Optimization Algorithm for Multi-Constraints Second-Order Stochastic Dominance Portfolio Optimization

“…, x n ), which is incorporated into the portfolio return. Therefore, the transaction cost of i th asset and total transaction cost can be expressed as follows [37]:…”