The Geography of Subadvisors, Managerial Structure, and the Performance of International Equity Mutual Funds
Bibliographic record
Abstract
Abstract We study whether subadvising abroad provides an information advantage that improves the performance of international equity mutual funds. We find that it does not. In fact, internationally outsourced funds underperform on a risk-adjusted basis by up to 162 bps annually. The underperformance is concentrated in funds managed by single subadvisors, who are less likely to be terminated after poor performance compared to funds with multiple subadvisors. We dissect fund performance by the location of its subadvisors and find that international subadvisors underperform primarily in their local holdings. Finally, we show that the industry is nonetheless on a path toward equilibrium. (JEL G11, G12, G14, G15, G23). Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".