de Roon for providing factor returns for the Canadian market. For helpful comments and suggestions, we thank
Bibliographic record
Abstract
We assess and analyze the three components of active management (asset allocation, market timing and security selection) in the performance of pension funds. Security selection explains most of the differences in pension fund returns. Large pension funds in our sample on average provide value to their clients after accounting for all investment-related costs, both before and after risk-adjusting: we find an annual alpha of 17 basis points from changes in asset allocation, 27 basis points from market timing, and 45 basis points from security selection. All three active management components exhibit significant liquidity limitations, which are important in all asset classes, including equity and fixed income. Security selection outperformance is largely driven by momentum trading. Accounting for momentum reduces the security selection alpha by about 72 basis points and offsets most of the positive risk-adjusted returns from market timing and asset allocation changes. Larger funds realize economies of scale in their relatively small allocation to private asset classes, like private equity and real estate. However, in equity and fixed income markets they experience substantial liquidity-related diseconomies of scale.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.251 | 0.028 |
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".