Appraisal of Uncertainty-Driven Financial Performance: Bringing Multi-Criteria Decision Making Techniques with Neutrosophic Theory

Evaluating financial performance is essential yet challenging due to various factors, including ambiguity, insufficient information, and conflicting evaluation criteria. Conventional Multi-Criteria Decision-Making (MCDM) techniques often struggle to manage these complexities effectively. To address these limitations and enhance financial performance assessments, this research proposes an innovative approach integrating Single-Valued Neutrosophic Sets (SVNS) with established MCDM methodologies. SVNS uniquely manages uncertainty by concurrently quantifying degrees of truth, indeterminacy, and falsity within financial data. This research specifically employs entropy-based weighting methods integrated with Additive Ratio Assessment (ARAS) and Multi-Objective Optimization by Ratio Analysis (MOORA) methodologies in the SVNS environment to systematically rank enterprises based on financial performance under uncertainty. By constructing a systematic and flexible model, this research significantly advances decision-making practices in financial management. The findings highlight substantial improvements in accuracy and reliability, providing decision-makers with a robust and reliable tool for informed financial evaluations.