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Record W2123671299 · doi:10.1177/0148558x0301800206

A Reexamination of the Incremental Information Content of Capital Expenditures

2003· article· en· W2123671299 on OpenAlexaboutno aff
Chul W. Park, Morton Pincus

Bibliographic record

VenueJournal of Accounting Auditing & Finance · 2003
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsEarningsProfitability indexCapital expenditureCapital (architecture)Quarter (Canadian coin)RevenueEconomicsFiscal yearCost of capitalMonetary economicsFinanceMicroeconomics

Abstract

fetched live from OpenAlex

Under generally accepted accounting principles (GAAP), firms must postpone recognition of the earnings effects of capital expenditures until they realize the resulting revenues and expenses. However, if capital expenditures change the profile of future profits, we expect share prices to impound that revision in profitability prior to its recognition under GAAP. This suggests that changes in capital expenditures should have information content beyond current-period unexpected earnings. Prior research considered annual changes in capital expenditures and detected incremental information only in restricted samples. However, we examine more general samples and observe that quarterly as well as annual changes in capital expenditures are informative beyond unexpected earnings. Furthermore, we predict greater incremental information content for fiscal fourth quarter changes in capital expenditures when considering all fiscal quarters simultaneously, because the effect of the earnings recognition delay under GAAP should be magnified for capital expenditures made late in the fiscal year. Our results are consistent with this prediction and inconsistent with the prediction that changes in fiscal fourth quarter capital expenditures reflect lower profitability that results from an inefficient bunching of capital expenditures in the fiscal fourth quarter.

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)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.217
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0010.000
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 head0.010
GPT teacher head0.196
Teacher spread0.186 · 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 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
Published2003
Admission routes1
Has abstractyes

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