A Quantum-Inspired Holographic Model for Decoding and Predicting Corporate Performance

Abstract

A fundamental challenge in quantitative finance and organizational sciences is to quantify the time-lagged relationship between a company's internal organizational dynamics and its tangible outputs. This study introduces an analytical framework that models this relationship using a quantum-inspired holographic concept. Applying a multi-stage statistical pipeline to 10 years of public data from six firms, we tested time-series models representing innovation and collaboration. The analysis revealed this new framework functions as an effective analytical filter for out-of-sample data. The results indicate that per-company calibrated models—which synchronize objective output data with calibrated, conceptually paired proxy metrics (like R&D spend for innovation)—achieved statistically significant predictive power (p = 0.021) against financial productivity data, whereas traditional, single-variable models did not. The findings demonstrate this Holographic Evaluation System for Topologic Interrelational Analysis (HESTIA) framework is a robust method for developing and validating firm-specific models for profits and valuation. The process is analogous to an imaging technique, transforming noisy proxy signals from a latent signal domain into an interpretable map of a firm's unique operational rhythm. The resulting models visualize a firm's value-creation cycle with a statistically high likelihood of predicting future “productivity,” which in this study is defined as profit and share price.