Financial Statement Analysis Techniques for Evaluating Corporate Financial Health

Abstract

This research introduces a novel, cross-disciplinary methodology for evaluating
corporate financial health by integrating traditional financial statement analysis
with computational linguistics and network theory. While conventional approaches
rely heavily on ratio analysis and trend examination, our framework introduces
three innovative dimensions: semantic analysis of management discussion and analysis (MDA) sections to quantify qualitative disclosures, temporal network modeling of financial statement interconnections to detect systemic vulnerabilities, and
anomaly detection through unsupervised machine learning applied to financial time
series. We develop a composite Financial Health Index (FHI) that synthesizes quantitative metrics, qualitative sentiment, and relational stability. The methodology is
applied to a unique dataset of 500 publicly traded companies across ten sectors over
a fifteen-year period, including periods of economic stress. Results demonstrate that
our integrated approach identifies early warning signals of financial distress with 34