A Symbolic Neutrosophic Models for Corporate Financial Management Performance: Integrating Multi-Layer Algebra and Case Analysis

Abstract

This paper introduces a dual-framework methodology for analyzing corporate 
financial performance under uncertainty by integrating two original models: the Meta
Symbolic 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. 

DOI: 10.5281/zenodo.15843751