Ethical strategy focus and mutual fund management: performance and persistence
Bibliographic record
Abstract
Over the last few years academics and practitioners alike have been analyzing the relative performance of different types of mutual funds, with a particular emphasis on comparing the performance of conventional versus socially responsible investment (SRI). The methods and samples used, as well as the results obtained are diverse, but they generally point to the difficulties found by SRI to yield an equivalent performance as that of its conventional peers—given the investment constraints they face. In this study we focus on the comparative performance of a sample of SRI funds, which we decompose mainly into Environmental, Social and Governance (ESG), environmental, and religious, and which invest in three different geographical areas. For these funds we measure not only performance but, more importantly, their persistence—i.e., whether the best (worst) funds are past winners (losers) as well. This twofold objective turns out to be essential to uncover some trends in the industry. Specifically, whereas ESG, in general, outperform their environmental peers, a deeper scrutiny focusing also on performance persistence reveals that this claim should be tempered, since investing in the best past environmental funds yields superior performance than investing in the best past ESG funds. This result, which holds for the two main geographical regions analyzed (Europe and US/Canada), would indicate that the comparison between these two types of funds is more intricate than what we might a priori expect, being particularly relevant to factor in the comparison an evaluation of performance persistence.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".