ESG ratings and financial performance of exchange-traded funds during the COVID-19 pandemic
Bibliographic record
Abstract
With the advent of the COVID-19 pandemic, the world has experienced economic and social fragility, which calls for alternative approaches to navigate towards sustainable outcomes. While recent studies show that responsible investments (RI) are resilient during the economic downturn caused by crises such as COVID-19, there has been little exploration into exchange-traded funds (ETFs). Using ANOVA and multivariate regression models, we analyze the differences and relationship between the financial returns of ETFs and their Eco-fund ratings during the COVID-19 pandemic-related financial market crash. Our results indicate that higher levels of the sustainability performance of ETFs do not safeguard investments from financial losses during a severe market downturn. These results contribute to the research by exposing weaknesses of current sustainability scores and rating methods to provide an initial analysis of RI during the COVID-19 pandemic
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".