Market experience with modeling for defined-benefit pension funds: evidence from four countries
Bibliographic record
Abstract
This paper takes a look at the modeling side of pension fund management. It is based on interviews with pension fund managers, regulators, consultants, and academics in four countries – the Netherlands, Switzerland, the United Kingdom, and the United States. The objective was to understand, through the experience of market participants, the role of modeling in managing defined-benefit pension funds. The 28 defined-benefit pension funds participating in the study have a total of €334 billion ($436 billion) assets under management. The findings of our study show that modeling is now considered an indispensable tool by many market participants. The need to manage the risk inherent in defined-benefit pension plans is the key motivation behind the growing use of modeling. In the Netherlands, for example, where private-sector plans did not experience serious underfunding problems after the 2000 market crash, the use of modeling is widespread and well-integrated in the decision-making process. Dutch regulators have recently mandated a risk-based approach and specified broad principles of sound modeling, including the marking to market of assets and liabilities.
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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 | 0.017 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".