Happy Losers: Subcontracting in International Asset Management
Bibliographic record
Abstract
We study international outsourcing in the asset management industry. We argue that subcontractor management companies use the funds they manage on behalf of third parties to subsidize their own inhouse funds. On average, inhouse funds outperform the outsourced funds by 7.5 basis points per month. We attribute this difference in performance to within-company subsidization and identify risk-taking as the main source of the observed performance differentials. Portfolios of inhouse funds are 5-8% more illiquid, load 5% more on the market factor, and score consistently higher in the within-style tournament. Inhouse funds engage in performance-improving cross-trading with affiliated outsourced funds. The trades between inhouse and outsourced funds of the same company are more illiquid than outside trades. Inhouse funds use outsourced funds as insurance at the time of distress; a one standard deviation increase in the fraction of outsourced funds managed by the same company mitigates the negative performance impact of large outflows of an inhouse fund by up to 30%. However, outsourcing families still benefit from outsourcing even after subsidization. We endogenize the outsourcing decision and we see that, via outsourcing, fund families mitigate the negative effects of being located far away and improve expected return by 31.2 bp a month on average. This suggests that outsourcing is used as a means of overcoming segmented equity markets.
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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 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".