Diversification versus Concentration Motivesin Mutual Fund Mergers
Bibliographic record
Abstract
This study examines the commonality between characteristics of acquirers and those of targets in mutual fund mergers. A positive and significant commonality would align with acquirers targeting similar funds and thus expecting further concentration in their segment. An opposite result would lend credence to the hypothesis that acquirers aim to diversify away from their original characteristics. Our empirical results show that acquirers and targets share positively correlated total net assets, expenses, turnover, and age, whereas they exhibit nonsignificant correlations in performance and flows. Thus, the potential for diversification could stem from the latter two characteristics. We then test whether the differences between the characteristics of acquirers and targets predict the post-merger performance of the acquirers. We find that acquirers that target funds with poorer performance, higher turnover, and higher expense ratios exhibit a decrease in their post-merger performance. TOPICS:Mutual funds/passive investing/indexing, 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 | 0.001 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".