Managing Earnings Using Classification Shifting: Evidence from Quarterly Special Items
Bibliographic record
Abstract
ABSTRACT: McVay (2006) concludes that managers opportunistically shift core expenses to special items to inflate current core earnings, resulting in a positive relation between unexpected core earnings and income-decreasing special items. However, she further notes that this relation disappears when contemporaneous accruals are dropped from the core earnings expectations model. McVay (2006) calls for research to improve the core earnings expectations model and to provide additional cross-sectional tests of classification shifting. Using a core earnings expectations model that is not dependent on accrual special items, we show that classification shifting is more likely in the fourth quarter than in interim quarters. We also find more evidence of classification shifting when the ability of managers to manipulate accruals appears to be constrained and in meeting a range of earnings benchmarks. Overall, our evidence provides broad support for McVay’s (2006) conclusion that managers engage in classification shifting. Our study also sheds new understanding of the conditions under which managers are more likely to employ classification shifting.
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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 | 0.006 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".