Does Regulation Matter? Riskiness and Procyclicality of Pension Asset Allocation
Bibliographic record
Abstract
In this paper, we investigate the relative importance of drivers to pension funds’ asset allocation choices. We specifically test if the contrast between regulatory approaches of public and private Defined Benefits (DB) pension funds in the US, Canada and the Netherlands have an impact on the riskiness and procyclicality of their asset allocation. Derived from panel data analysis of a unique database comprising of more than 800 pension funds’ detailed asset allocations, our results underscore the economic importance of regulation in the funds’ asset allocation choices, relative to institutional and individual funds’ characteristics. In particular, quantitative risk-based capital requirements, and to a lesser extent valuation and funding requirements (i.e., the choice of the liability discount rate) or the presence of quantitative investment restrictions, induce pension funds to significantly decrease their asset allocation to risky assets, especially to equities. Allocation to alternatives, which are comparatively treated quite favorably by solvency standards, is higher in the presence of risk-based capital requirements. Contrary to popular conviction that regulatory mechanisms encourage procyclical asset allocation, we find that funds subject to risk-based capital requirements were likely to be less procyclical during the last crisis – an outcome possibly tempered by temporary regulatory slackening in response to the crisis.
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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 | 0.021 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".