Do <scp>ESG</scp> funds engage in portfolio pumping to gain higher flows? An application of <scp>Benford's Law</scp>
Bibliographic record
Abstract
Abstract Portfolio pumping is identified as an ‘illegal’ trading practice that involves inflating quarter‐ and year‐end portfolio returns. Utilizing U.S. domestic equity mutual fund daily return data, we examine whether environmental, social, and governance (ESG) funds engage in portfolio pumping to generate higher flows. Our findings reveal that, on average, ESG funds attract 0.4% higher flows than other funds. However, they engage in portfolio pumping and achieve returns that are 5.3 basis points (bps) higher at quarter ends compared to their returns during the rest of the year. This practice does not result in additional fund flows. Notably, compared to other funds, ESG funds exhibit a significant 4 bps reduction through portfolio pumping. This implies that, on the last day of the quarter, ESG funds earn approximately 4 bps lower returns compared to other funds. Portfolio pumping is costly for both investors and financial markets since it leads to trading activities that cause stock prices to deviate from their fundamental values. ESG funds engage less in portfolio pumping than other funds, which indicates that their primary focus is to maximize fund flows rather than enhance or create a positive social impact on the underlying firm portfolio. Investors seem to understand this goal, particularly when ESG funds engage in portfolio pumping and avoid investing in ESG funds.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".