Asymmetric Impact of Active Management on the Performance of ESG Funds
Bibliographic record
Abstract
This paper investigates the asymmetric impact of fund active management style on the performance of ESG funds. Unlike conventional measures of synchronicity, we propose new measures that capture the asymmetric patterns in a fund’s management style in upside and downside market conditions. Our data includes 170 equity funds that are identified as socially responsible, with a period spanning from 2010 to 2022. Our proposed methodology allows us to capture the asymmetric patterns in the fund management styles under different market conditions while mitigating the challenge of outliers, which is crucial when assessing funds’ active management activities. We find that while ESG funds promote sustainability, their active management is only beneficial during periods of market downturns. Our results are robust after controlling for different funds characteristics, for several active management proxies, and across various model specifications. This paper thus provides crucial guidelines for fund managers since it shows that their success is greatly influenced by their time-varying skills and management style in changing market conditions. Our findings incentivize ESG fund managers to pursue information acquisition activities during market downturns, as these activities improve market informational efficiency while aligning with their sustainability goals.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".