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Record W3124837273 · doi:10.2308/accr-52445

Firm Risk and Disclosures about Dispersion of Asset Values: Evidence from Oil and Gas Reserves

2019· article· en· W3124837273 on OpenAlexaboutno aff
Marc Badia‐Miró, Mary E. Barth, Miguel Duro, Gaizka Ormazábal

Bibliographic record

VenueThe Accounting Review · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsOpportunismDispersion (optics)BusinessEquity (law)Systematic riskVolatility (finance)Monetary economicsAsset (computer security)Sample (material)EconomicsFinancial economicsFinance

Abstract

fetched live from OpenAlex

ABSTRACT The question we address is whether mandated disclosure about dispersion of nonfinancial asset values can provide information relevant to assessing firm risk. Using a sample of Canadian oil and gas (O&G) firms between 2004 and 2011, we find that the difference between the disclosed 10th and 50th percentiles from the O&G reserves distribution, which measures dispersion of the distribution, is positively associated with future total and idiosyncratic equity return volatility, systematic risk, and credit risk. We also find that disclosure of increased reserves dispersion is associated with weaker stock price reactions to increases in reserves and with increases in bid-ask spreads, both of which indicate the disclosures convey information about risk associated with reserves. Additional tests reveal little evidence of managerial opportunism in the reserves disclosures. Taken together, our evidence suggests that quantitative disclosures about the dispersion of nonfinancial 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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.154
Threshold uncertainty score0.360

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.032
GPT teacher head0.246
Teacher spread0.214 · 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

Citations28
Published2019
Admission routes1
Has abstractyes

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