(untitled) (gjstudies, 2022)
This research introduces a novel, cross-disciplinary methodology that applies computational linguistics and machine learning to the emerging domain of environmentalrisk disclosure in corporate financial reporting. Traditional financial analysis has largelytreated environmental disclosures as qualitative, peripheral information. We proposea paradigm shift by developing a predictive analytics framework that quantifies andforecasts the material financial impact of disclosed environmental risks. Our approachis distinctive in its hybrid technique, combining sentiment analysis derived from socialpsychology with probabilistic graphical models adapted from computational biology tomap the causal pathways between environmental risk language and subsequent marketperformance. We formulate the unconventional problem of ’latent financial materiality’—identifying which environmental disclosures, though not currently priced by markets, contain predictive signals for future volatility and valuation shocks. The methodology processes a unique corpus of 10-K and sustainability report filings from 2000 to2004, employing a bespoke lexicon for environmental risk semantics that moves beyondsimple keyword counting. Our results demonstrate that a composite ’E-Risk Signal’derived from our model exhibits a statistically significant, non-linear relationship with12-month forward stock return volatility and analyst forecast dispersion. Crucially,we identify a subset of ’stealth risk’ disclosures—characterized by specific syntacticstructures and connotative language—that precede negative earnings surprises by anaverage of three quarters, a finding previously obscured in conventional analysis. Thiswork provides original contributions to information systems, financial accounting, andsustainable finance by offering a computationally rigorous tool for investors to decodeearly-warning signals in corporate environmental communication, thereby addressinga critical gap in the efficient assimilation of non-financial risk data into investmentmodels