A Neutrosophic SuperHyper Number Framework for Accurate Statistical Evaluation of Financial Performance in High-Tech Enterprises

This paper introduces a new mathematical model that evaluates the financial performance of high-tech enterprises using neutrosophic numbers and superhyper uncertainty theory. Traditional financial analysis often fails to address incomplete, indeterminate, or conflicting information. To solve this problem, we develop a Neutrosophic SuperHyper Number Framework (NSHNF) that combines elements from neutrosophic number theory, superhyper structures, and neutrosophic probability/statistics. Each financial metric is modeled as a neutrosophic number in the form x=a+bI, where I capture indeterminacy. These values are then structured using powersets Pn to allow for multiple levels of uncertainty. We introduce several new formulas, such as the Neutrosophic Mean, Variance, and a new index called NSHNPI (Neutrosophic SuperHyper Number Performance Index). Realistic examples are provided with full calculations to show the model's usefulness and accuracy. This work represents a new direction in financial evaluation by combining strong mathematical logic, deep uncertainty handling, and practical applications in business analytics.