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Record W4249198273 · doi:10.1504/ijaf.2017.088027

Value relevance of the voluntary disclosure of advertising expenditure: evidence from Canada

2017· article· en· W4249198273 on OpenAlexaffabout
Philip McIlkenny, Ajax Persaud

Bibliographic record

VenueInternational Journal of Accounting and Finance · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsEndogeneityValue (mathematics)Voluntary disclosureEnterprise valueBusinessMonetary economicsSample (material)EconomicsAccountingEconometrics

Abstract

fetched live from OpenAlex

This paper investigates whether the voluntary disclosure of advertising expenditure by Canadian firms is value relevant. The analysis is based on a matched sample of 876 firms listed on the Canadian Toronto Stock Exchange over the period 2007-2014. There were 438 firms that disclosed their advertising expenditures, which we refer to as disclosers and 438 firms that did not disclose, which we refer to as non-disclosers. The findings suggest that advertising expenditure is positively related to firm value, a firm size effect exists and that only for certain sectors is this expenditure value relevant. We also found a positive relationship between market value and advertising expenditure for discloser firms. The results are stable across several econometric models that control for endogeneity. The implications for disclosure strategies of firms are discussed.

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)0.002
metaresearch head score (Gemma)0.022
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 score0.027
Threshold uncertainty score0.199

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.007
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.008
GPT teacher head0.222
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 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

Citations7
Published2017
Admission routes2
Has abstractyes

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