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Record W2014341357 · doi:10.1080/17446540802298043

Profit warnings: will openness be rewarded?

2009· article· en· W2014341357 on OpenAlexaff
Matthew Church, Han Donker

Bibliographic record

VenueApplied Economics Letters · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsShareholderInformation asymmetryBusinessProfit (economics)Openness to experienceMonetary economicsEmpirical evidenceAccountingEconomicsIndustrial organizationMicroeconomicsFinanceCorporate governance

Abstract

fetched live from OpenAlex

We investigate the information content of profit warnings released by firms on the abnormal returns for a sample of 149 firms listed on the Euronext Amsterdam in 2000–2002. We propose that firms can diminish the negative influence of profit warnings on shareholder's returns by releasing detailed information, thereby reducing the information asymmetry between shareholders and management. We find empirical evidence that a greater degree of disclosure has a significantly positive impact on the abnormal returns of firms with multiple successive profit warnings. We argue that negative abnormal returns will occur with firms which provide external reasons in their press releases indicating that the causes of the current situation are a market-wide phenomenon and beyond their scope. We report a negative – but not significant – impact of information regarding external reasons on the abnormal returns to shareholders of firms with profit warnings. Our research findings offer valuable insights into the practical implications of the information content of profit warnings.

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)0.004
metaresearch head score (Gemma)0.068
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.068
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.176
Teacher spread0.165 · 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 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

Citations8
Published2009
Admission routes1
Has abstractyes

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