arXiv：cs.LG· Sourav Bose, Taoufik Bouraoui·· 3 小时前AI 评分38
用共形机器学习量化企业“漂绿”：算法验证与市场纪律
The Price of Greenwashing: Algorithmic Verification and Market Discipline using Conformal Machine Learning
AI 导读
研究融合美国 SEC 财务数据与 EPA 设施级温室气体登记数据，用梯度提升与 Mondrian Conformal Prediction 构建企业排放的算法基线，并提出 Conformal-Weighted Continuous Divergence（CWCD）指标衡量自报数据与该基线的偏差。
正文
Abstract:While corporate sustainability mandates are expanding, the systemic reliance on self-reported emissions data exposes financial markets to pervasive greenwashing. Current literature relies heavily on subjective ESG ratings or textual sentiment analysis, leaving a critical econometric gap in objectively quantifying physical climate realities. To resolve this information asymmetry, we fuse U.S. SEC financial fundamentals with facility-level EPA greenhouse gas registries to establish a mathematically guaranteed baseline of physical corporate emissions. Leveraging a gradient boosting architecture and Mondrian Conformal Prediction, we quantify the shortfall between self-reported data and this algorithmic baseline into a novel Conformal-Weighted Continuous Divergence (CWCD) metric. Evaluating this divergence via a cross-sectional lead-lag econometric design, we uncover a robust mechanism of market discipline: algorithmic emissions divergence exhibits a severe, statistically significant negative relationship with subsequent market valuation (Tobin's Q) and operational profitability (ROA). Providing definitive evidence against the market blindness hypothesis, this study proves that institutional capital actively prices environmental deception not merely as an ethical lapse, but as a leading indicator of fundamental corporate mismanagement. Ultimately, these findings provide the quantitative justification necessary for asset managers and regulators to deploy algorithmic auditing infrastructure at scale.
| Subjects: | Machine Learning (cs.LG); Econometrics (econ.EM) |
| Cite as: | arXiv:2610.02225 [cs.LG] |
| (or arXiv:2610.02225v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.02225 arXiv-issued DOI via DataCite |
Submission history
From: Sourav Bose [view email]
[v1]
Sat, 19 Sep 2026 22:24:21 UTC (1,635 KB)
来源：arXiv：cs.LG · arxiv.org