The Effect of ESG Performance on Stock Price Resiliency and Volatility in Korea: Evidence from COVID-19 Pandemic
Bibliographic record
Abstract
Although the COVID-19 pandemic differs in origin from traditional economic crises, its adverse effects on the global financial market are notably more pronounced. Following the initial emergence of COVID-19, stock market indices experienced a sharp decline, and stock prices became more volatile. This paper focuses on the role of Environmental, Social, and Governance (ESG) performance of Korean-listed firms during the financial crisis stemming from the COVID-19 pandemic. This paper uses the ESG score and rating data from the Korea Institute of Corporate Governance and Sustainability (KCGS). It divides the analysis period into crisis (1st quarter in 2020) and post-crisis (from 2nd quarter in 2020 to 1st quarter in 2021) periods. The results show that the stock price fall of firms with good ESG performance in the crisis period was higher than that of firms with poor performance. However, the stock price resilience of firms with good performance is markedly higher. Additionally, the price volatility of firms with good ESG performance is lower than that of firms with poor performance. This paper provides new empirical evidence that ESG activity plays an important role in stock price resiliency and volatility in Korea, even during financial crises like the one spawned by the COVID-19 pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".