Discontinuities and Earnings Management: Evidence from Restatements Related to Securities Litigation*
Bibliographic record
Abstract
Abstract A heated debate exists as to whether discontinuities in earnings distributions are indicative of earnings management. While many studies attribute discontinuities in earnings distributions to earnings management, other studies argue that earnings discontinuities are artifacts of sample selection and research design. Overall, there is limited direct evidence of a connection between earnings discontinuities and earnings management. In this study, we provide direct evidence linking earnings management to earnings discontinuities for a sample of firms that settle securities class action lawsuits and restate earnings from the alleged GAAP violation period. We compare the distribution of restated (“unmanaged”) earnings to originally reported (“managed”) earnings. We find that discontinuities are not present in the distribution of analyst forecast errors and earnings changes using unmanaged earnings but are present using managed earnings. The discontinuity in the earnings level distribution is attenuated, but not eliminated, on an unmanaged basis. These shifts among our sample of firms are caused by earnings management and cannot be explained by sample selection or research design issues. Our findings are important because many studies use earnings discontinuities as a proxy for intentional earnings manipulations and we provide the first direct evidence of a link between these two phenomena.
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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 | 0.005 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; a candidate call from one teacher head, 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".