Treffer: Credibilistic skewness of LR power fuzzy numbers with applications in portfolio selection.

This study proposes a novel analytical expression to compute the crisp equivalent of the credibilistic skewness for an LR fuzzy number belonging to the power family. Using this expression, a novel multi-objective portfolio selection model is proposed within the credibilistic framework. The model simultaneously optimizes three key objectives–mean, semivariance, and skewness of the portfolio return–while incorporating practical constraints such as budget, cardinality, bounds on the assets allocations, and no short-selling. This work addresses a notable gap in the literature, where higher-order moments like skewness are seldom considered in the context of LR power fuzzy numbers under credibility theory. All the objectives have been evaluated by considering the returns of the entire portfolio instead of dealing explicitly with the returns of individual assets. It eliminates the need for computationally expensive simulations of diverse portfolio attributes resulting from aggregating individual asset returns. The proposed model is effectively solved using an adapted version of a highly efficient multi-objective genetic algorithm (MOGA), specially configured to handle complex portfolio selection problems under practical constraints. Empirical analysis using historical data from the NIFTY 50 Index of the National Stock Exchange (NSE), India, reveals that incorporating credibilistic skewness of LR power fuzzy numbers yields superior performance compared to existing models. Additionally, the proposed model demonstrates superior performance over benchmarks like the NIFTY 50 Index and the naïve 1/n investment strategy, underscoring its practical utility in real-world portfolio construction. [ABSTRACT FROM AUTHOR]

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