Analysis of Financial Investment Stock Markets Based on Linguistic Cubic Modified Fuzzy Decision-Making Strategies

Pythagorean fuzzy set (PFS) and their modified frameworks deal with uncertainty more flexibly than the intuitionistic fuzzy set (IFS) frameworks. The existing frameworks address only partial aspects of real-life uncertainty, such as real-valued, interval-valued or complex-valued information. The existing models integrating interval-valued or complex-valued information lack of capable to represent linguistic uncertainty while the fuzzy set (FS) models based on linguistic term lack the capability to present complex-valued or higher order real-life uncertainty. Therefore, the existing techniques fail to simultaneously model interval imprecision, linguistic vagueness, and complex-valued cubic uncertainty within a unified framework. To overcome these limitations, the newly defined framework, called linguistic complex cubic Pythagorean fuzzy set (LCCuPFS) is demonstrated, integrating complex cubic Pythagorean fuzzy sets (CCuPFSs) with linguistic feature, providing a more realistic and robust representation of multifaceted uncertainty in daily real-life applications. Some basic operational laws and their corresponding aggregation operators (AOs) such as averaging AOs, geometric AOs, weighted averaging AOs, and weighted geometric AOs within the framework of LCCuPFSs are established. The key properties of the proposed AOs are investigated. Furthermore, a decision-making (DM) algorithm based on the newly defined approaches is developed. We employ the proposed techniques to establish a DM approach for solving real-world problems. To verify its practical utility, we apply the newly defined approaches in a real-world financial investment DM problem, where assessments are inherently imprecise, linguistically expressed, and influenced by interval-valued and complex-valued uncertainties. Finally, a comparative study between the proposed and existing approaches is investigated based on the ranking-wise performance and characteristic-wise evaluation.