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Record W2738742709

Firm Risk and Disclosures About Dispersion in Asset Values

2017· preprint· en· W2738742709 on OpenAlexaboutno aff
Marc Badia‐Miró, Mary E. Barth, Miguel Duro, Gaizka Ormazábal

Bibliographic record

VenueRePEc: Research Papers in Economics · 2017
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsnot available
Fundersnot available
KeywordsDispersion (optics)OpportunismVolatility (finance)Systematic riskBusinessEquity (law)Monetary economicsEconomicsSample (material)Financial economics
DOInot available

Abstract

fetched live from OpenAlex

This study examines whether mandated disclosure about the dispersion of the value of oil and gas (O&G) reserves provides information about firm risk. Based on a sample of Canadian O&G firms between 2004 and 2011, we find that the difference between the 10th and 50th percentiles of O&G reserves, which is a measure of dispersion of the reserves distribution, is positively associated with future total and idiosyncratic equity return volatility, systematic risk, and credit risk. We also find that disclosure of increases in reserves dispersion is associated with weaker stock price reactions to increases in reserve levels and with increases in bid-ask spreads, both of which indicate the disclosures convey information about risk associated with the reserves. Additional tests reveal it is unlikely that our findings are attributable to managerial opportunism in estimating reserves. Taken together, our study provides evidence that disclosures relating to the dispersion of non-financial asset values can provide information relevant to assessing firm risk.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.051
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.308
Teacher spread0.271 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2017
Admission routes1
Has abstractyes

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