The Impact on Stock Returns of Crowding by MutualFunds
Bibliographic record
Abstract
Evidence from recent financial debacles suggests that crowding can adversely impact the subsequent performance of crowded investments and destabilize financial markets. However, the term “crowding” has been used loosely in the public media. To be precise, the authors define and develop a measure of crowding that captures the interaction of correlated trades and illiquidity and use this metric to study how crowding on stocks by mutual funds affects the subsequent returns on the stocks for the period from 1981 to 2012. They find a strong negative association between the crowding measure and the quarterly returns two quarters ahead. More in-depth analysis reveals that a long–short portfolio with a long position in the least crowded stocks and a short position in the most crowded stocks can earn an annualized abnormal return as high as 14.53% after adjusting for size, book to market, and momentum characteristics. The authors further confirm that the substantial abnormal returns are not driven by time-varying expected returns. Surprisingly, the abnormal returns can mostly be attributed to the least crowded stocks, which have characteristics resembling stocks neglected by mutual funds. They demonstrate that their crowding measure is an improvement over the liquidity measure and conveys important signals beyond what is embedded in turnover. TOPICS:Fundamental equity analysis, mutual fund performance, performance measurement
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".