Real Earnings Management and the Strategic Release of New Products
Bibliographic record
Abstract
Prior studies on real earnings management (REM) mainly focus on the estimation of abnormal operating and investing activities using Compustat data at the firm level. We extend this literature by providing micro-level evidence regarding how financial reporting pressures influence new product release decisions, i.e., product-level REM. Specifically, we compare how public and private studios time the release of their movies differently. We find that, facing pressure to boost quarterly revenues and earnings, public studios are more likely to release movies with high expected revenues in the third month of a quarter than private studios. To corroborate our findings, we examine variations in movie release patterns within public studios. We find that among public studios, when their recent past performance is poor, they are more likely to release movies with high expected box office revenues in the third month of a quarter. Furthermore, among movies with high expected revenues, those movies in genres with a more targeted release window (e.g., romance movies and horror movies), and those with directors who have worked with the studio in the past, are less likely to be released in the third month of a quarter. This result suggests that studios choose the least costly path to achieve financial reporting goals. One negative consequence of this financial reporting motivated product release strategy is that movies released in the third month of a quarter have lower international box office revenues. Taken together, these results provide evidence of the existence and consequences of product-level real earnings management.
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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 | 0.001 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".