Enhancing Stock Portfolio Selection with Trapezoidal Bipolar Fuzzy VIKOR Technique with Boruta-GA Hybrid Optimization Model: A Multicriteria Decision-Making Approach - Sunil Kumar Sharma (International Journal of Computational Intelligence Systems)
Abstract The investors’ main objective is to minimize the risks and to get the maximum returns in the random stock market requires them to choose the correct mix of the stocks. The traditional portfolio selection methods often struggle with market volatility, leading to less-than-the targeted profits. This research attempts to apply trapezoidal bipolar fuzzy Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method to help in decision-making. It also combines the fuzzy set theory with the VIKOR method, the trapezoidal bipolar fuzzy with VIKOR (TrBFV) approach offers a comprehensive and flexible system for evaluating investment options. The proposed approach has been validated through real-world illustrations. The analysis has been made with the VIKOR method and its integration with trapezoidal bipolar fuzzy sets. Financial decision-making can be very hard for investors who have access to big data due to the overwhelming amount of information they are required to interpret. The Boruta-GA approach combines the advantages of the Genetic Algorithm (GA) and Boruta Optimization Algorithm (BOA) methods, implementing the comprehensive feature selection capability of Boruta to detect all relevant features and harnessing the strength of GA to bring about improvement within a wide range of datasets. The result shows that this novel approach is effective and can help to investors to take decisions in the unpredictable financial market. This study is an attempt to provides investors with an appropriate approach to navigate the challenges of stock market investment. Abbreviations serve as a nomenclature table, detailing all acronyms.