Abstract
The decision of stock selection relies on experts' cognition and investors' behavioral characteristics. This requires the consideration of several conflicting, often fuzzy criteria with uncertainty conditions, which can benefit from multiple attribute decision-making (MADM). Accordingly, an extended regret theory (RT) decision-making method is developed in this study to identify and rank-order superior stocks. First, by extracting the strengths of probabilistic linguistic term sets and cloud models, a novel concept of probabilistic linguistic cloud sets (PLCSs) is proposed to effectively express and handle uncertain preference information. Second, RT is extended to the PLCSs environment. Considering the behavior characteristics of expectation dependence, dual (target and growth) expectations are shown. Third, a distance measure algorithm of PLCSs is defined to calculate the distance between the attribute value and dual expectations, which serves as the basis for the construction of a fuzzy pattern recognition model to determine the optimal membership and attribute weights. Membership is used to modify the RT-based perceived utility, the ranking of alternatives is determined by the modified comprehensive perceived utility. A case study is conducted to demonstrate the proposed method, and its reliability and effectiveness are further verified by comparing it with other methods.
Highlights
Stock selection is a key problem in portfolio construction and management [1], [2]
Gong et al.: Extension of regret theory (RT) Based on probabilistic linguistic cloud sets (PLCSs) Considering Dual Expectations: Application for the Stock Market decision-makers (DMs) may prefer to express their preferences with different degrees and/or distributions of importance, and this information is usually hard to obtain, so they proposed the probabilistic linguistic term sets (PLTSs), which exhibit great flexibility in capturing DMs’ hesitant qualitative assessments [14]
The core focus of this paper is to propose an extended RT decision-making method based on PLCSs considering dual expectations to address stock selection problems
Summary
Stock selection is a key problem in portfolio construction and management [1], [2]. The financial market is a complex and changeable system with many kinds of information. X. Gong et al.: Extension of RT Based on PLCSs Considering Dual Expectations: Application for the Stock Market decision-makers (DMs) may prefer to express their preferences with different degrees and/or distributions of importance, and this information is usually hard to obtain, so they proposed the probabilistic linguistic term sets (PLTSs), which exhibit great flexibility in capturing DMs’ hesitant qualitative assessments [14]. (2) Attribute evaluation values are usually crisp numbers, Z-numbers, or triangular fuzzy numbers, but relevant work on how to calculate the regret/rejoice of DMs in cloud models environment has not been adequately addressed. The core focus of this paper is to propose an extended RT decision-making method based on PLCSs considering dual expectations to address stock selection problems. 1, 2, . . . , n), the CWAA operator reduces to the CAA operator
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