Changes in accounting estimates: An update of priors or an earnings management strategy of “last resort”?
Bibliographic record
Abstract
Abstract Evidence from prior research is mixed about whether accounting estimate changes are strategically motivated, on average, or whether they reflect new or updated information. To interpret this difference, we investigate, by category of material changes in accounting estimates, the association between estimate changes and subsequent restatements. We also explore the determinants of both income‐increasing and income‐decreasing estimate changes for different categories of estimate changes. We find that the motivations for and the determinants of estimate changes depend on the type of change and on whether the changes in estimates are income‐increasing or income‐decreasing. Overall, we conclude that when companies are motivated to bias earnings and they cannot do so by manipulating other within generally accepted accounting principles (GAAP) accruals, they sometimes resort to using estimate changes. Our more detailed investigation of estimate changes at the account level suggests a more nuanced view of the determinants of changes in accounting estimates. We develop a more complete model of the determinants of changes in accounting estimates than those used in this emerging literature, which should be of interest to accounting academics, regulators, audit practitioners and audit committee members.
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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 | 0.017 | 0.130 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".