A Reexamination of the Incremental Information Content of Capital Expenditures
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".