Do Korean Firms Disclose What Matters? AI-Measured Financially Material ESG Disclosure, Regulatory Anticipation and Firm Value

Investors value sustainability information that is financially material to a firm’s industry, but little is known about how much of it firms outside the United States and Europe report. We examine 7,278 statutory business reports of 808 non-financial KOSPI firms for fiscal years 2016–2025. A classifier trained on large language model annotations sorts every narrative sentence by environmental, social and governance (ESG) issue and specificity, and the industry standards of the Sustainability Accounting Standards Board separate material from immaterial content. The classifier outperforms dictionary and zero-shot methods; blind human coding confirms this advantage but shows that the classifier understates material content. Material ESG information is a small, slowly growing, mostly qualitative part of the reports. After Korea’s 2021 roadmap for mandatory sustainability disclosure, the largest firms expanded their ESG content more than other firms. They added specific material information but also generic and, less robustly, immaterial statements, so their disclosure did not shift significantly towards material issues; the estimates are sensitive to flexible trend controls. Material disclosure is not robustly related to firm value, and its weak association with better liquidity is shared by ESG content generally. Anticipated mandates can expand disclosure without clearly making it more useful to investors.

Authors

Publication Details

Journal
Preprints.org
Published
2026-11-03
DOI
https://doi.org/10.20944/preprints202611.0206.v1
Primary Topic
Corporate Social Responsibility Reporting
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

Do Korean Firms Disclose What Matters? AI-Measured Financially Material ESG Disclosure, Regulatory Anticipation and Firm Value

Young‐Chan Lee, Chuyu Yang
Preprints.org
Corporate Social Responsibility Reporting
preprint

Do Korean Firms Disclose What Matters? AI-Measured Financially Material ESG Disclosure, Regulatory Anticipation and Firm Value

Young‐Chan Lee, Chuyu Yang
preprint en

Abstract

Investors value sustainability information that is financially material to a firm’s industry, but little is known about how much of it firms outside the United States and Europe report. We examine 7,278 statutory business reports of 808 non-financial KOSPI firms for fiscal years 2016–2025. A classifier trained on large language model annotations sorts every narrative sentence by environmental, social and governance (ESG) issue and specificity, and the industry standards of the Sustainability Accounting Standards Board separate material from immaterial content. The classifier outperforms dictionary and zero-shot methods; blind human coding confirms this advantage but shows that the classifier understates material content. Material ESG information is a small, slowly growing, mostly qualitative part of the reports. After Korea’s 2021 roadmap for mandatory sustainability disclosure, the largest firms expanded their ESG content more than other firms. They added specific material information but also generic and, less robustly, immaterial statements, so their disclosure did not shift significantly towards material issues; the estimates are sensitive to flexible trend controls. Material disclosure is not robustly related to firm value, and its weak association with better liquidity is shared by ESG content generally. Anticipated mandates can expand disclosure without clearly making it more useful to investors.

Preprints.org
Corporate Social Responsibility Reporting
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon