Real earnings management and the strategic release of new products: evidence from the motion picture industry
Bibliographic record
Abstract
Abstract Prior studies on real earnings management (REM) focus mainly on estimating abnormal operating and investing activities at the firm level. We extend this literature by providing micro-level evidence regarding how financial reporting pressures influence new product release decisions, or product-level REM. Specifically, we compare how public and private studios differentially time the release of their movies. We find that, faced with pressure to boost quarterly revenues and earnings, public studios are more likely to release movies with high expected revenues in the last month of a fiscal quarter, compared to private studios. This documented result is stronger for firms with recent poor past performance, but is not present for movies in genres with a more targeted release window (e.g., romance and horror movies) and those using directors who have a history of collaboration with the studio. These results suggest that studios choose REM activities that have a lower impact on consumer demand and that minimize conflict with talent, consistent with choosing less costly activities to achieve financial reporting goals. A negative consequence of this financial reporting–driven product release strategy is that movies released in the last month of a quarter have lower international box office revenues. Taken together, these results provide evidence of the existence and consequences of product-level REM.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".