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Record W3123114487 · doi:10.1177/0148558x19832685

Management Sales Forecasts and Firm Market Power

2019· article· en· W3123114487 on OpenAlexaff
Andrew A. Acito, David Folsom, Rong Zhao

Bibliographic record

VenueJournal of Accounting Auditing & Finance · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversity of Calgary
FundersBinghamton UniversityMichigan State UniversityBrigham Young University
KeywordsSales journalCompetitor analysisMarket powerEarningsSales managementMargin (machine learning)BusinessCompetition (biology)Product (mathematics)EconomicsFinancial economicsMarketingFinanceMicroeconomicsMonopoly

Abstract

fetched live from OpenAlex

We investigate whether a firm’s market power within its product market affects management sales forecast behavior. Our examination of the relation between market power and management sales forecasts is motivated by the notion that sales forecasts differ from other types of forecasts because sales forecasts provide investors and competitors with a more transparent signal of a firm’s short-term demand expectations and its immediate actions in the product market than other forecasts. Thus, we first provide evidence consistent with sales forecasts containing unique information about future sales and evidence consistent with this information being related to competitor pricing and output decisions. We then find evidence that higher market power, proxied by excess margin, is associated with a higher likelihood of issuing sales forecasts after controlling for industry-level competition effects and earnings forecast behavior. Overall, our findings are consistent with higher-market-power firms being more willing to publicly disclose sales forecast information.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.541
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0010.001
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 head0.005
GPT teacher head0.194
Teacher spread0.189 · 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.

Study designNot applicable
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
Published2019
Admission routes1
Has abstractyes

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