(untitled) (gjstudies, 2026)
This research introduces a novel, multi-dimensional framework for corporatefinancial health assessment that transcends traditional ratio analysis by integrating principles from computational ecology and network theory. Conventional financial statement analysis has long relied on static ratios and trend comparisons,which often fail to capture the dynamic, systemic interdependencies within a corporation’s financial structure or its adaptive capacity in volatile markets. Ourmethodology, termed the Ecological Financial Health Index (EFHI), reconceptualizes the corporation as a financial ecosystem. We map balance sheet, incomestatement, and cash flow statement items into interacting nodes within a directed,weighted network, where cash flows represent energy transfers and equity/reservesfunction as stability reservoirs. Key innovations include the derivation of ’financial trophic levels’ to analyze value flow efficiency, the calculation of ’balance sheetconnectance’ to measure systemic risk from interdependency, and the applicationof ’financial allometry’ to assess whether growth in assets, revenues, and profitsfollows sustainable scaling laws. We validate the EFHI framework using a longitudinal dataset of 500 publicly traded firms across five industries from 1995 to2004. Results demonstrate that the EFHI provides superior predictive power forcorporate distress events up to 24 months in advance compared to Altman’s Zscore and traditional liquidity/solvency ratios, with a mean increase in AUC-ROCof 0.18. Furthermore, the ’connectance’ metric uniquely identifies firms with ’brittle’ financial structures that appear healthy under conventional metrics but arehighly vulnerable to sector-specific shocks. This cross-disciplinary approach offersa more holistic, systemic, and forward-looking tool for analysts, investors, and regulators, fundamentally shifting the paradigm from discrete indicator monitoring tointegrated ecosystem assessment of corporate vitality