Do Firms Time Changes in Accounting Estimates to Manage Earnings?<sup>†</sup>
Bibliographic record
Abstract
ABSTRACT Prior earnings management research often focuses on specific accounts or on estimations of discretionary accruals but provides only limited insight into the methods firms actually use to manage earnings. In order to begin exploration of some of the operational details regarding how earnings are managed, we investigate whether firms time their decisions to make changes in accounting estimates (CAEs) in consideration of their earnings benchmarks. Using CAE data across all accounts from 2006 to 2018, we find that 28.1% of income‐increasing CAEs are implemented in quarters where pre‐CAE earnings are below a forecasted earnings benchmark but inclusion of the CAE effectively allows the firm to meet the benchmark. We find that income‐increasing CAEs are more likely implemented when a firm's pre‐CAE earnings are further below the benchmark. We also find that firms are more likely to implement income‐decreasing CAEs under two scenarios: (i) when pre‐CAE earnings are relatively high, as a way either to smooth earnings or to “bury bad news,” and (ii) when pre‐CAE earnings are already low, as a way to take a financial “big bath” and position the firm for positive future earnings. In addition, we present evidence that firms using CAEs to achieve an earnings benchmark face financial consequences in terms of poorer immediate stock price performance and subsequent return on assets. Our conclusions hold after performing several additional analyses, including consideration of other discretionary options, addressing endogeneity concerns, and conducting falsification tests. In sum, we contribute to the earnings management literature by presenting consistent evidence that firms appear to time CAEs to meet earnings benchmarks or achieve other reporting objectives.
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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 | 0.005 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".