Maintaining a Reputation for Consistently Beating Earnings Expectations and the Slippery Slope to Earnings Manipulation
Bibliographic record
Abstract
ABSTRACT This paper investigates whether maintaining a reputation for consistently beating analysts' earnings expectations can motivate executives to move from “within GAAP” earnings management to “outside of GAAP” earnings manipulation. We analyze firms subject to SEC enforcement actions and find that these firms consistently beat analysts' quarterly earnings forecasts in the three years prior to the manipulation period and continue to do so by smaller “beats” during the manipulation period. We find that manipulating firms beat expectations around 86 percent of the time in the 12 quarters prior to the manipulation period (versus 75 percent for control firms) and that manipulation often ends with a miss in expectations. We document that executives of manipulating firms face strong stock market and CEO pressure to perform. Prior to the manipulation period, these firms have high analyst optimism, growing institutional interest, and high market valuations, along with powerful CEOs. Further, we find that maintaining a reputation for beating expectations is more important than CEO overconfidence and is incremental to CEO equity incentives for explaining manipulation. Our results suggest that pressure to maintain a reputation for beating analysts' expectations can encourage aggressive accounting and, ultimately, earnings manipulation.
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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 | 0.003 | 0.027 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".