Bibliographic record
Abstract
This paper offers a novel explanation of the financial underwriting cycle in the property-liability insurance industry. By doing so it resolves that significant anomaly in asset pricing theory posed by cycles in the efficient pricing of insurance coverage. In contrast to the reliance on a variety of institutional or capital market failures underlying all previous explanations of this cycle, we directly augment the complete-markets environment of traditional asset-pricing models through the presence of a single source of risk that cannot be fully hedged through existing financial markets. We realistically interpret this source of risk as unforecastable noise in the implementation of insurance regulations. Cycles in the value of underwriting insurance coverage can arise in this simple variant of a standard complete-markets pricing model owing to the effect of such regulatory risk. We offer a sufficient condition for a stable cycle to endogenously exist in market equilibrium and illustrate this condition in the context of a representative insurance firm and a regulator pursuing a countercyclical policy with noisy implementation. Interestingly, while insurance pricing is efficient in the absence of the regulator, cyclic pricing and underwriting profitability can be induced by a countercyclical regulator policy designed to stabilize the very cycle it creates.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".