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Record W2173170459 · doi:10.19030/iber.v8i12.3192

The Informational Content Of Voluntary Embedded Value (EV) Financial Disclosures By Canadian Life Insurance Companies

2011· article· en· W2173170459 on OpenAlexaffabout
Jacques Préfontaine, Jean Desrochers, Lise Godbout

Bibliographic record

VenueInternational Business & Economics Research Journal (IBER) · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicRisk Management in Financial Firms
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsBusinessLife insuranceAccountingVoluntary disclosureEquity (law)EarningsValue (mathematics)Relevance (law)TurnoverOrder (exchange)Statutory lawFinanceActuarial scienceEconomics

Abstract

fetched live from OpenAlex

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 bound 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. In order to improve upon this situation, this paper studies and reports the informational content of voluntary embedded value (EV) financial disclosures by Canadian life insurance companies. 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 EV voluntary financial disclosures communicate intrinsic informational content and provide value relevance to external stakeholders in the sense that they were found to be closely associated with life insurers market value of equity.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.003
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.076
GPT teacher head0.267
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2011
Admission routes2
Has abstractyes

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