Stock portfolio selection using a new decision-making approach based on the integration of fuzzy CoCoSo with Heronian mean operator
The main objective of stock portfolio selection is to distribute capital to selected stocks to get the most profitable returns at a lower risk. The performance of a stock depends on a number of criteria based on the risk-return measures. Therefore, the selection of shares is subject to fulfilling a number of criteria. In this paper, we have adopted an integrated approach based on the two-stage framework. First, the Heronian mean operator (improved generalized weighted Heronian mean an improved generalized geometric weighted Heronian mean) is combined with the traditional Combined compromise solution (CoCoSo) method to present a new decision-making model for dealing with stock selection problems. Second, the Base-criterion method is used to calculate the relative optimal weights of the specified decision criteria. Despite the uncertainties, the advanced CoCoSo-H model eliminates the efficacy of anomalous data and makes complex decisions more flexible. A case study of stock selection for the portfolio under the National stock exchange (NSE) is discussed to validate the applicability of the proposed model. Different portfolios have been constructed using Particle swarm optimization (PSO). The outcome shows the prominence and stability of the proposed model when compared to previous studies.
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