Predictive Analytics for Environmental Risk Disclosure and Investor Decision Making

Abstract

This research introduces a novel, cross-disciplinary methodology that applies computational linguistics and machine learning to the emerging domain of environmental
risk disclosure in corporate financial reporting. Traditional financial analysis has largely
treated environmental disclosures as qualitative, peripheral information. We propose
a paradigm shift by developing a predictive analytics framework that quantifies and
forecasts the material financial impact of disclosed environmental risks. Our approach
is distinctive in its hybrid technique, combining sentiment analysis derived from social
psychology with probabilistic graphical models adapted from computational biology to
map the causal pathways between environmental risk language and subsequent market
performance. 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 to
2004, employing a bespoke lexicon for environmental risk semantics that moves beyond
simple keyword counting. Our results demonstrate that a composite ’E-Risk Signal’
derived from our model exhibits a statistically significant, non-linear relationship with
12-month forward stock return volatility and analyst forecast dispersion. Crucially,
we identify a subset of ’stealth risk’ disclosures—characterized by specific syntactic
structures and connotative language—that precede negative earnings surprises by an
average of three quarters, a finding previously obscured in conventional analysis. This
work provides original contributions to information systems, financial accounting, and
sustainable finance by offering a computationally rigorous tool for investors to decode
early-warning signals in corporate environmental communication, thereby addressing
a critical gap in the efficient assimilation of non-financial risk data into investment
models