The Informational Content Of Voluntary Embedded Value (EV) Financial Disclosures By Canadian Life Insurance Companies During The Recent Period Of Market Turmoil
Bibliographic record
Abstract
The informational content and relevance to external stakeholders of voluntary financial disclosures by commercial banks is now becoming more widely recognized. For instance, banks voluntary disclosures of liquidity, interest rate and market risk metrics have been found to be closely associated with market value of equity and credit ratings. So far, there has been very scarce published research on investigating the informational content and relevance to external stakeholders of voluntary financial disclosures by life insurance companies during the recent period of market turmoil. In order to improve upon this situation, this paper updates previous findings and reports on the informational content of voluntary embedded value (EV) financial disclosures by Canadian life insurance companies during the 2000-2010 time period. As opposed to traditional statutory balance sheet and earnings reporting, EV voluntary disclosure attempts to estimate the present value of future earnings generated by a life insurers current book of various insurance businesses. The preliminary results presented in this study indicate that recent EV voluntary financial disclosures failed to communicate intrinsic informational content and to provide value relevance to external stakeholders in the sense that they were not found to be closely associated with life insurers market value of equity and credit ratings during the recent 2007-2010 period of market turmoil.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".