Environmental, Social, and Governance Factors in Emerging Markets: a Volatility Study
Bibliographic record
Abstract
Orientation: Asset managers constructing an emerging market portfolio of stocks should, along with more traditional risk metrics, consider ESG data in their due diligence and investment decision-making processes.Research purpose: To determine whether a company's higher relative focus on ESG incorporation results in the observation of lower levels of share price return volatility, as predicted by EWMA and GARCH models. Study motivation:Institutional investors wish to understand the role that ESG data plays in mitigating the risk of emerging market portfolios and whether the results necessitate the incorporation of ESG data in due diligence and investment decision-making processes.Research approach/design and method: Categorisation of emerging market stocks using ESG scores and return volatility predicted by EWMA and GARCH models allowed for the analysis of aggregate corporate market risk.These volatilities paired with their respective annual ESG scores permitted a more company-specific view of this relationship.Main findings: Companies with higher relative ESG Combined Scores exhibit lower levels of weekly volatility, but using annualised volatility weakens this relationship.The predictive ability of ESG scores to predict volatility is weak, and this weakens still further after the onset of crises, such as the COVID-19 global pandemic.Practical/managerial implications: Incorporating ESG data into portfolio performance analysis could assist in mitigating corporate market risk.Contribution/value add: Most research considers the state of ESG investing in developed markets rather than companies domiciled in emerging markets.This work could provide a more complete perspective of the state of ESG investing.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".