Navigating Computational Intelligence Approaches for Efficient Portfolio Selection in VUCA and BANI Financial Landscape

Abstract

The complexity and unpredictability inherent in efficient markets, intensified by VUCA, and BANI environments, demand intelligent, flexible, and adaptive strategies for effective portfolio selection. Traditional optimization techniques regularly struggle to account for the dynamic behavior and rapid fluctuations inherent in such volatile conditions. In these contexts, computational intelligence techniques integrating evolutionary algorithms, neural networks, fuzzy logic, and hybrid approaches have emerged as an effective tool to tackle the complex portfolio selection and evaluates their performance using both historical and real stock market data. By adapting to actual data and predictive algorithms, the proposed framework, dependent on computational intelligence, supplying enhanced decision-making amid uncertainty. This work contributes to the growing body of intelligent financial analytics literature by presenting a robust, data-driven decision-support system designed to navigate the complexities of modern financial markets shaped by VUCA and BANI forces.