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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".