<scp>S</scp><scp>OLVENCY</scp> A<scp>NALYSIS AND</scp> P<scp>REDICTION IN</scp> P<scp>ROPERTY</scp>–<scp>C</scp><scp>ASUALTY</scp> I<scp>NSURANCE</scp>: I<scp>NCORPORATING</scp> E<scp>CONOMIC AND</scp> M<scp>ARKET</scp> P<scp>REDICTORS</scp>
Bibliographic record
Abstract
Abstract This article extends the insolvency prediction literature by incorporating macroeconomic conditions and state‐specific factors. The models achieve greater generalizability and predictive accuracy than earlier research while giving fewer false positives. At the firm level, we find insurers with less diversified business, sufficient cash flow, high return on equity, lower leverage, fewer failed Insurance Regulatory Information System ratio tests, and membership in a larger group are less likely to become insolvent. Our findings support the argument that insolvency likelihood increases for insurers domiciled in states with stricter solvency supervision and/or states with less favorable insurance market conditions, and during soft markets; insolvency risk is negatively related to the slope of the yield curve. Our findings also imply that insurers respond efficiently to changes in such market factors as market return, inflation, and catastrophic losses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.130 | 0.022 |
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".