On the market valuation of insurance accounting: An assessment of historical cost and fair value measurements
Bibliographic record
Abstract
Abstract We analyze the relationship between stock prices and insurance accounting and compare a historical cost with a full fair value measurement approach. During our sample period, European insurers had to determine the fair value of all assets and liabilities according to the Solvency II (SII) regulation, in addition to the historical‐cost‐based setup of the International Financial Reporting Standards (IFRS). This alternative source of information allowed investors to update their expectations about future dividends, risks, and firm values. Comparing both frameworks, we report three findings. First, we show that the association between stock prices and SII full fair value accounting items is greater than that of IFRS historical cost measurements. Second, we find that this effect stems from unexpected news disclosed by regulatory reporting. Third, our results suggest that insurance accounting is relevant for firms exposed to lower insolvency risk and offers no additional information when the risk level is high.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".