Finding 3132Emerging EvidenceValidation V0
This paper introduces a flexible, domain-agnostic machine learning framework for estimating diverse firm-level sustainability metrics from standard financial data, uniquely addressing incomplete ESG data. Incorporating prediction uncertainty quantification and prediction-powered inference, it advances methodological robustness, offering significant impact for quantitative finance as sustainability data becomes crucial for investment and risk management.
75%Confidence
1Evidence objects
v1Version
DraftStatus
Evidence trail
Supporting75% linkage confidence
This paper introduces a flexible, domain-agnostic machine learning framework for estimating diverse firm-level sustainability metrics from standard financial data, uniquely addressing incomplete ESG data. Incorporating prediction uncertainty quantification and prediction-powered inference, it advances methodological robustness, offering significant impact for quantitative finance as sustainability data becomes crucial for investment and risk management.
key_findings bullet 4 · key_findings
Inspect source: Corporate Sustainability Data and Machine Learning →This Finding was extracted from the configured corpus. It is versioned, traceable, and may evolve through editorial review or new corpus evidence.