A Symbolic Neutrosophic Models for Corporate Financial Management Performance: Integrating Multi-Layer Algebra and Case Analysis - Yan Wu (Neutrosophic Sets and Systems)
This paper introduces a dual-framework methodology for analyzing corporate financial performance under uncertainty by integrating two original models: the MetaSymbolic Neutrosophic Performance Algebra (MSNPA) and the Symbolic Neutrosophic Multi-Layer Topological Algebra (SNMTA). These models fuse symbolic representations of financial indicators with neutrosophic logic, allowing multi-dimensional encoding of truth, indeterminacy, and falsity across time, sources, and semantic roles. We define new mathematical constructs such as semantic clarity, epistemic degradation, and filtering monotonicity. Formal properties including continuity and semantic compactness are proven within a topological neutrosophic space. A real-world case study using Tesla and Apple financial indicators validates the model's effectiveness and shows how different truth layers affect trustworthiness. Comparative evaluation with fuzzy logic reveals the limitations of traditional scalar-based reasoning and highlights the interpretive power of symbolic-neutrosophic logic. The proposed framework offers a rigorous, expressive, and explainable solution for financial decision-making in uncertain environments.